diff --git a/src/skill_seekers/benchmark/__init__.py b/src/skill_seekers/benchmark/__init__.py
index cdd4861..c842885 100644
--- a/src/skill_seekers/benchmark/__init__.py
+++ b/src/skill_seekers/benchmark/__init__.py
@@ -33,9 +33,9 @@ from .runner import BenchmarkRunner
 from .models import BenchmarkReport, Metric
 
 __all__ = [
-    'Benchmark',
-    'BenchmarkResult',
-    'BenchmarkRunner',
-    'BenchmarkReport',
-    'Metric',
+    "Benchmark",
+    "BenchmarkResult",
+    "BenchmarkRunner",
+    "BenchmarkReport",
+    "Metric",
 ]
diff --git a/src/skill_seekers/benchmark/framework.py b/src/skill_seekers/benchmark/framework.py
index ba1fb89..8086841 100644
--- a/src/skill_seekers/benchmark/framework.py
+++ b/src/skill_seekers/benchmark/framework.py
@@ -11,12 +11,7 @@ from typing import Any
 from collections.abc import Callable
 from pathlib import Path
 
-from .models import (
-    Metric,
-    TimingResult,
-    MemoryUsage,
-    BenchmarkReport
-)
+from .models import Metric, TimingResult, MemoryUsage, BenchmarkReport
 
 
 class BenchmarkResult:
@@ -97,7 +92,7 @@ class BenchmarkResult:
             memory=self.memory,
             metrics=self.metrics,
             system_info=self.system_info,
-            recommendations=self.recommendations
+            recommendations=self.recommendations,
         )
 
 
@@ -161,7 +156,7 @@ class Benchmark:
                 operation=operation,
                 duration=duration,
                 iterations=iterations,
-                avg_duration=duration / iterations if iterations > 1 else duration
+                avg_duration=duration / iterations if iterations > 1 else duration,
             )
 
             self.result.add_timing(timing)
@@ -201,7 +196,7 @@ class Benchmark:
                 before_mb=mem_before,
                 after_mb=mem_after,
                 peak_mb=peak_memory,
-                allocated_mb=mem_after - mem_before
+                allocated_mb=mem_after - mem_before,
             )
 
             self.result.add_memory(usage)
@@ -212,7 +207,7 @@ class Benchmark:
         *args,
         operation: str | None = None,
         track_memory: bool = False,
-        **kwargs
+        **kwargs,
     ) -> Any:
         """
         Measure function execution.
@@ -260,17 +255,16 @@ class Benchmark:
             def load_config(path):
                 return json.load(open(path))
         """
+
         def decorator(func: Callable) -> Callable:
             @functools.wraps(func)
             def wrapper(*args, **kwargs):
                 return self.measure(
-                    func,
-                    *args,
-                    operation=operation,
-                    track_memory=track_memory,
-                    **kwargs
+                    func, *args, operation=operation, track_memory=track_memory, **kwargs
                 )
+
             return wrapper
+
         return decorator
 
     def metric(self, name: str, value: float, unit: str):
@@ -285,11 +279,7 @@ class Benchmark:
         Examples:
             benchmark.metric("pages_per_sec", 12.5, "pages/sec")
         """
-        metric = Metric(
-            name=name,
-            value=value,
-            unit=unit
-        )
+        metric = Metric(name=name, value=value, unit=unit)
         self.result.add_metric(metric)
 
     def recommend(self, text: str):
@@ -328,7 +318,7 @@ class Benchmark:
 
         path.parent.mkdir(parents=True, exist_ok=True)
 
-        with open(path, 'w') as f:
+        with open(path, "w") as f:
             f.write(report.model_dump_json(indent=2))
 
     def analyze(self):
@@ -339,11 +329,7 @@ class Benchmark:
         """
         # Analyze timing bottlenecks
         if self.result.timings:
-            sorted_timings = sorted(
-                self.result.timings,
-                key=lambda t: t.duration,
-                reverse=True
-            )
+            sorted_timings = sorted(self.result.timings, key=lambda t: t.duration, reverse=True)
 
             slowest = sorted_timings[0]
             total_time = sum(t.duration for t in self.result.timings)
@@ -351,7 +337,7 @@ class Benchmark:
             if slowest.duration > total_time * 0.5:
                 self.recommend(
                     f"Bottleneck: '{slowest.operation}' takes "
-                    f"{slowest.duration:.1f}s ({slowest.duration/total_time*100:.0f}% of total)"
+                    f"{slowest.duration:.1f}s ({slowest.duration / total_time * 100:.0f}% of total)"
                 )
 
         # Analyze memory usage
@@ -360,8 +346,7 @@ class Benchmark:
 
             if peak > 1000:  # >1GB
                 self.recommend(
-                    f"High memory usage: {peak:.0f}MB peak. "
-                    "Consider processing in batches."
+                    f"High memory usage: {peak:.0f}MB peak. Consider processing in batches."
                 )
 
             # Check for memory leaks
diff --git a/src/skill_seekers/benchmark/models.py b/src/skill_seekers/benchmark/models.py
index 107d100..f8da8a7 100644
--- a/src/skill_seekers/benchmark/models.py
+++ b/src/skill_seekers/benchmark/models.py
@@ -14,8 +14,7 @@ class Metric(BaseModel):
     value: float = Field(..., description="Metric value")
     unit: str = Field(..., description="Unit (seconds, bytes, pages/sec, etc.)")
     timestamp: datetime = Field(
-        default_factory=datetime.utcnow,
-        description="When metric was recorded"
+        default_factory=datetime.utcnow, description="When metric was recorded"
     )
 
 
@@ -48,26 +47,13 @@ class BenchmarkReport(BaseModel):
     finished_at: datetime = Field(..., description="Finish time")
     total_duration: float = Field(..., description="Total duration in seconds")
 
-    timings: list[TimingResult] = Field(
-        default_factory=list,
-        description="Timing results"
-    )
-    memory: list[MemoryUsage] = Field(
-        default_factory=list,
-        description="Memory usage results"
-    )
-    metrics: list[Metric] = Field(
-        default_factory=list,
-        description="Additional metrics"
-    )
+    timings: list[TimingResult] = Field(default_factory=list, description="Timing results")
+    memory: list[MemoryUsage] = Field(default_factory=list, description="Memory usage results")
+    metrics: list[Metric] = Field(default_factory=list, description="Additional metrics")
 
-    system_info: dict[str, Any] = Field(
-        default_factory=dict,
-        description="System information"
-    )
+    system_info: dict[str, Any] = Field(default_factory=dict, description="System information")
     recommendations: list[str] = Field(
-        default_factory=list,
-        description="Optimization recommendations"
+        default_factory=list, description="Optimization recommendations"
     )
 
     @property
@@ -89,14 +75,8 @@ class ComparisonReport(BaseModel):
     baseline: BenchmarkReport = Field(..., description="Baseline benchmark")
     current: BenchmarkReport = Field(..., description="Current benchmark")
 
-    improvements: list[str] = Field(
-        default_factory=list,
-        description="Performance improvements"
-    )
-    regressions: list[str] = Field(
-        default_factory=list,
-        description="Performance regressions"
-    )
+    improvements: list[str] = Field(default_factory=list, description="Performance improvements")
+    regressions: list[str] = Field(default_factory=list, description="Performance regressions")
 
     speedup_factor: float = Field(..., description="Overall speedup factor")
     memory_change_mb: float = Field(..., description="Memory usage change (MB)")
diff --git a/src/skill_seekers/benchmark/runner.py b/src/skill_seekers/benchmark/runner.py
index 1233453..32d62e2 100644
--- a/src/skill_seekers/benchmark/runner.py
+++ b/src/skill_seekers/benchmark/runner.py
@@ -46,10 +46,7 @@ class BenchmarkRunner:
         self.output_dir.mkdir(parents=True, exist_ok=True)
 
     def run(
-        self,
-        name: str,
-        benchmark_func: Callable[[Benchmark], None],
-        save: bool = True
+        self, name: str, benchmark_func: Callable[[Benchmark], None], save: bool = True
     ) -> BenchmarkReport:
         """
         Run single benchmark.
@@ -83,7 +80,7 @@ class BenchmarkRunner:
             filename = f"{name}_{timestamp}.json"
             path = self.output_dir / filename
 
-            with open(path, 'w') as f:
+            with open(path, "w") as f:
                 f.write(report.model_dump_json(indent=2))
 
             print(f"📊 Saved benchmark: {path}")
@@ -91,9 +88,7 @@ class BenchmarkRunner:
         return report
 
     def run_suite(
-        self,
-        benchmarks: dict[str, Callable[[Benchmark], None]],
-        save: bool = True
+        self, benchmarks: dict[str, Callable[[Benchmark], None]], save: bool = True
     ) -> dict[str, BenchmarkReport]:
         """
         Run multiple benchmarks.
@@ -122,11 +117,7 @@ class BenchmarkRunner:
 
         return reports
 
-    def compare(
-        self,
-        baseline_path: Path,
-        current_path: Path
-    ) -> ComparisonReport:
+    def compare(self, baseline_path: Path, current_path: Path) -> ComparisonReport:
         """
         Compare two benchmark reports.
 
@@ -215,7 +206,7 @@ class BenchmarkRunner:
             improvements=improvements,
             regressions=regressions,
             speedup_factor=speedup_factor,
-            memory_change_mb=memory_change_mb
+            memory_change_mb=memory_change_mb,
         )
 
     def list_benchmarks(self) -> list[dict[str, Any]]:
@@ -237,13 +228,15 @@ class BenchmarkRunner:
                 with open(path) as f:
                     data = json.load(f)
 
-                benchmarks.append({
-                    "name": data["name"],
-                    "path": str(path),
-                    "started_at": data["started_at"],
-                    "duration": data["total_duration"],
-                    "operations": len(data.get("timings", []))
-                })
+                benchmarks.append(
+                    {
+                        "name": data["name"],
+                        "path": str(path),
+                        "started_at": data["started_at"],
+                        "duration": data["total_duration"],
+                        "operations": len(data.get("timings", [])),
+                    }
+                )
             except Exception:
                 # Skip invalid files
                 continue
diff --git a/src/skill_seekers/cli/adaptors/base.py b/src/skill_seekers/cli/adaptors/base.py
index 4b8246e..ca02c30 100644
--- a/src/skill_seekers/cli/adaptors/base.py
+++ b/src/skill_seekers/cli/adaptors/base.py
@@ -74,7 +74,7 @@ class SkillAdaptor(ABC):
         output_path: Path,
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
-        preserve_code_blocks: bool = True
+        preserve_code_blocks: bool = True,
     ) -> Path:
         """
         Package skill for platform (ZIP, tar.gz, etc.).
@@ -282,7 +282,7 @@ class SkillAdaptor(ABC):
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
         preserve_code_blocks: bool = True,
-        source_file: str = None
+        source_file: str = None,
     ) -> list[tuple[str, dict]]:
         """
         Optionally chunk content for RAG platforms.
@@ -326,33 +326,31 @@ class SkillAdaptor(ABC):
             chunk_overlap=max(50, chunk_max_tokens // 10),  # 10% overlap
             preserve_code_blocks=preserve_code_blocks,
             preserve_paragraphs=True,
-            min_chunk_size=100  # 100 tokens minimum
+            min_chunk_size=100,  # 100 tokens minimum
         )
 
         # Chunk the document
         chunks = chunker.chunk_document(
             text=content,
             metadata=metadata,
-            source_file=source_file or metadata.get('file', 'unknown')
+            source_file=source_file or metadata.get("file", "unknown"),
         )
 
         # Convert RAGChunker output format to (text, metadata) tuples
         result = []
         for chunk_dict in chunks:
-            chunk_text = chunk_dict['page_content']
+            chunk_text = chunk_dict["page_content"]
             chunk_meta = {
                 **metadata,  # Base metadata
-                **chunk_dict['metadata'],  # RAGChunker metadata (chunk_index, etc.)
-                'is_chunked': True,
-                'chunk_id': chunk_dict['chunk_id']
+                **chunk_dict["metadata"],  # RAGChunker metadata (chunk_index, etc.)
+                "is_chunked": True,
+                "chunk_id": chunk_dict["chunk_id"],
             }
             result.append((chunk_text, chunk_meta))
 
         return result
 
-    def _format_output_path(
-        self, skill_dir: Path, output_path: Path, suffix: str
-    ) -> Path:
+    def _format_output_path(self, skill_dir: Path, output_path: Path, suffix: str) -> Path:
         """
         Generate standardized output path with intelligent format handling.
 
@@ -379,11 +377,13 @@ class SkillAdaptor(ABC):
         output_str = str(output_path)
 
         # Extract the file extension from suffix (e.g., ".json" from "-langchain.json")
-        correct_ext = suffix.split('.')[-1] if '.' in suffix else ''
+        correct_ext = suffix.split(".")[-1] if "." in suffix else ""
 
         if correct_ext and not output_str.endswith(f".{correct_ext}"):
             # Replace common incorrect extensions
-            output_str = output_str.replace(".zip", f".{correct_ext}").replace(".tar.gz", f".{correct_ext}")
+            output_str = output_str.replace(".zip", f".{correct_ext}").replace(
+                ".tar.gz", f".{correct_ext}"
+            )
 
             # Ensure platform suffix is present
             if not output_str.endswith(suffix):
@@ -395,9 +395,7 @@ class SkillAdaptor(ABC):
 
         return Path(output_str)
 
-    def _generate_deterministic_id(
-        self, content: str, metadata: dict, format: str = "hex"
-    ) -> str:
+    def _generate_deterministic_id(self, content: str, metadata: dict, format: str = "hex") -> str:
         """
         Generate deterministic ID from content and metadata.
 
diff --git a/src/skill_seekers/cli/adaptors/chroma.py b/src/skill_seekers/cli/adaptors/chroma.py
index a6b876c..836d937 100644
--- a/src/skill_seekers/cli/adaptors/chroma.py
+++ b/src/skill_seekers/cli/adaptors/chroma.py
@@ -43,11 +43,7 @@ class ChromaAdaptor(SkillAdaptor):
         return self._generate_deterministic_id(content, metadata, format="hex")
 
     def format_skill_md(
-        self,
-        skill_dir: Path,
-        metadata: SkillMetadata,
-        enable_chunking: bool = False,
-        **kwargs
+        self, skill_dir: Path, metadata: SkillMetadata, enable_chunking: bool = False, **kwargs
     ) -> str:
         """
         Format skill as JSON for Chroma ingestion.
@@ -90,9 +86,9 @@ class ChromaAdaptor(SkillAdaptor):
                     content,
                     doc_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file="SKILL.md"
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file="SKILL.md",
                 )
 
                 # Add all chunks to parallel arrays
@@ -120,9 +116,9 @@ class ChromaAdaptor(SkillAdaptor):
                     ref_content,
                     doc_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file=ref_file.name
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file=ref_file.name,
                 )
 
                 # Add all chunks to parallel arrays
@@ -149,7 +145,7 @@ class ChromaAdaptor(SkillAdaptor):
         output_path: Path,
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
-        preserve_code_blocks: bool = True
+        preserve_code_blocks: bool = True,
     ) -> Path:
         """
         Package skill into JSON file for Chroma.
@@ -183,7 +179,7 @@ class ChromaAdaptor(SkillAdaptor):
             metadata,
             enable_chunking=enable_chunking,
             chunk_max_tokens=chunk_max_tokens,
-            preserve_code_blocks=preserve_code_blocks
+            preserve_code_blocks=preserve_code_blocks,
         )
 
         # Write to file
@@ -233,7 +229,7 @@ class ChromaAdaptor(SkillAdaptor):
         except ImportError:
             return {
                 "success": False,
-                "message": "chromadb not installed. Run: pip install chromadb"
+                "message": "chromadb not installed. Run: pip install chromadb",
             }
 
         # Load package
@@ -241,8 +237,8 @@ class ChromaAdaptor(SkillAdaptor):
             data = json.load(f)
 
         # Determine client type and configuration
-        persist_directory = kwargs.get('persist_directory')
-        chroma_url = kwargs.get('chroma_url')
+        persist_directory = kwargs.get("persist_directory")
+        chroma_url = kwargs.get("chroma_url")
 
         try:
             if persist_directory:
@@ -253,15 +249,15 @@ class ChromaAdaptor(SkillAdaptor):
                 # Remote HTTP client
                 print(f"🌐 Connecting to ChromaDB at: {chroma_url}")
                 # Parse URL
-                if '://' in chroma_url:
-                    parts = chroma_url.split('://')
+                if "://" in chroma_url:
+                    parts = chroma_url.split("://")
                     parts[0]
                     host_port = parts[1]
                 else:
                     host_port = chroma_url
 
-                if ':' in host_port:
-                    host, port = host_port.rsplit(':', 1)
+                if ":" in host_port:
+                    host, port = host_port.rsplit(":", 1)
                     port = int(port)
                 else:
                     host = host_port
@@ -276,12 +272,12 @@ class ChromaAdaptor(SkillAdaptor):
         except Exception as e:
             return {
                 "success": False,
-                "message": f"Failed to connect to ChromaDB: {e}\n\nTry:\n  pip install chromadb\n  chroma run  # Start local server"
+                "message": f"Failed to connect to ChromaDB: {e}\n\nTry:\n  pip install chromadb\n  chroma run  # Start local server",
             }
 
         # Get or create collection
-        collection_name = kwargs.get('collection_name', data.get('collection_name', 'skill_docs'))
-        distance_function = kwargs.get('distance_function', 'cosine')
+        collection_name = kwargs.get("collection_name", data.get("collection_name", "skill_docs"))
+        distance_function = kwargs.get("distance_function", "cosine")
 
         try:
             # Try to get existing collection
@@ -291,62 +287,57 @@ class ChromaAdaptor(SkillAdaptor):
             try:
                 # Create new collection
                 metadata = {"hnsw:space": distance_function}
-                collection = client.create_collection(
-                    name=collection_name,
-                    metadata=metadata
-                )
+                collection = client.create_collection(name=collection_name, metadata=metadata)
                 print(f"✅ Created collection: {collection_name} (distance: {distance_function})")
             except Exception as e:
                 return {
                     "success": False,
-                    "message": f"Failed to create collection '{collection_name}': {e}"
+                    "message": f"Failed to create collection '{collection_name}': {e}",
                 }
 
         # Handle embeddings
-        embedding_function = kwargs.get('embedding_function')
+        embedding_function = kwargs.get("embedding_function")
 
         try:
-            if embedding_function == 'openai':
+            if embedding_function == "openai":
                 # Generate embeddings with OpenAI
                 print("🔄 Generating OpenAI embeddings...")
                 embeddings = self._generate_openai_embeddings(
-                    data['documents'],
-                    api_key=kwargs.get('openai_api_key')
+                    data["documents"], api_key=kwargs.get("openai_api_key")
                 )
                 collection.add(
-                    documents=data['documents'],
-                    metadatas=data['metadatas'],
-                    ids=data['ids'],
-                    embeddings=embeddings
+                    documents=data["documents"],
+                    metadatas=data["metadatas"],
+                    ids=data["ids"],
+                    embeddings=embeddings,
                 )
-            elif embedding_function == 'sentence-transformers':
+            elif embedding_function == "sentence-transformers":
                 # Use sentence-transformers
                 print("🔄 Generating sentence-transformer embeddings...")
                 try:
                     from chromadb.utils import embedding_functions
+
                     ef = embedding_functions.SentenceTransformerEmbeddingFunction()
-                    embeddings = [ef([doc])[0] for doc in data['documents']]
+                    embeddings = [ef([doc])[0] for doc in data["documents"]]
                     collection.add(
-                        documents=data['documents'],
-                        metadatas=data['metadatas'],
-                        ids=data['ids'],
-                        embeddings=embeddings
+                        documents=data["documents"],
+                        metadatas=data["metadatas"],
+                        ids=data["ids"],
+                        embeddings=embeddings,
                     )
                 except ImportError:
                     return {
                         "success": False,
-                        "message": "sentence-transformers not installed. Run: pip install sentence-transformers"
+                        "message": "sentence-transformers not installed. Run: pip install sentence-transformers",
                     }
             else:
                 # No embeddings - Chroma will auto-generate
                 print("🔄 Using Chroma's default embedding function...")
                 collection.add(
-                    documents=data['documents'],
-                    metadatas=data['metadatas'],
-                    ids=data['ids']
+                    documents=data["documents"], metadatas=data["metadatas"], ids=data["ids"]
                 )
 
-            count = len(data['documents'])
+            count = len(data["documents"])
             print(f"✅ Uploaded {count} documents to ChromaDB")
             print(f"📊 Collection '{collection_name}' now has {collection.count()} total documents")
 
@@ -355,19 +346,14 @@ class ChromaAdaptor(SkillAdaptor):
                 "message": f"Uploaded {count} documents to ChromaDB collection '{collection_name}'",
                 "collection": collection_name,
                 "count": count,
-                "url": f"{chroma_url}/collections/{collection_name}" if chroma_url else None
+                "url": f"{chroma_url}/collections/{collection_name}" if chroma_url else None,
             }
 
         except Exception as e:
-            return {
-                "success": False,
-                "message": f"Upload failed: {e}"
-            }
+            return {"success": False, "message": f"Upload failed: {e}"}
 
     def _generate_openai_embeddings(
-        self,
-        documents: list[str],
-        api_key: str = None
+        self, documents: list[str], api_key: str = None
     ) -> list[list[float]]:
         """
         Generate embeddings using OpenAI API.
@@ -380,12 +366,13 @@ class ChromaAdaptor(SkillAdaptor):
             List of embedding vectors
         """
         import os
+
         try:
             from openai import OpenAI
         except ImportError:
             raise ImportError("openai not installed. Run: pip install openai") from None
 
-        api_key = api_key or os.getenv('OPENAI_API_KEY')
+        api_key = api_key or os.getenv("OPENAI_API_KEY")
         if not api_key:
             raise ValueError("OPENAI_API_KEY not set. Set via env var or --openai-api-key")
 
@@ -398,14 +385,14 @@ class ChromaAdaptor(SkillAdaptor):
         print(f"  Generating embeddings for {len(documents)} documents...")
 
         for i in range(0, len(documents), batch_size):
-            batch = documents[i:i+batch_size]
+            batch = documents[i : i + batch_size]
             try:
                 response = client.embeddings.create(
                     input=batch,
-                    model="text-embedding-3-small"  # Cheapest, fastest
+                    model="text-embedding-3-small",  # Cheapest, fastest
                 )
                 embeddings.extend([item.embedding for item in response.data])
-                print(f"  ✓ Processed {min(i+batch_size, len(documents))}/{len(documents)}")
+                print(f"  ✓ Processed {min(i + batch_size, len(documents))}/{len(documents)}")
             except Exception as e:
                 raise Exception(f"OpenAI embedding generation failed: {e}") from e
 
diff --git a/src/skill_seekers/cli/adaptors/claude.py b/src/skill_seekers/cli/adaptors/claude.py
index 82ec1bc..503ca1d 100644
--- a/src/skill_seekers/cli/adaptors/claude.py
+++ b/src/skill_seekers/cli/adaptors/claude.py
@@ -81,7 +81,14 @@ version: {metadata.version}
 {content_body}
 """
 
-    def package(self, skill_dir: Path, output_path: Path, enable_chunking: bool = False, chunk_max_tokens: int = 512, preserve_code_blocks: bool = True) -> Path:
+    def package(
+        self,
+        skill_dir: Path,
+        output_path: Path,
+        enable_chunking: bool = False,
+        chunk_max_tokens: int = 512,
+        preserve_code_blocks: bool = True,
+    ) -> Path:
         """
         Package skill into ZIP file for Claude.
 
diff --git a/src/skill_seekers/cli/adaptors/faiss_helpers.py b/src/skill_seekers/cli/adaptors/faiss_helpers.py
index 2a4480a..62c8539 100644
--- a/src/skill_seekers/cli/adaptors/faiss_helpers.py
+++ b/src/skill_seekers/cli/adaptors/faiss_helpers.py
@@ -46,11 +46,7 @@ class FAISSHelpers(SkillAdaptor):
         return self._generate_deterministic_id(content, metadata, format="hex")
 
     def format_skill_md(
-        self,
-        skill_dir: Path,
-        metadata: SkillMetadata,
-        enable_chunking: bool = False,
-        **kwargs
+        self, skill_dir: Path, metadata: SkillMetadata, enable_chunking: bool = False, **kwargs
     ) -> str:
         """
         Format skill as JSON for FAISS ingestion.
@@ -92,9 +88,9 @@ class FAISSHelpers(SkillAdaptor):
                     content,
                     doc_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file="SKILL.md"
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file="SKILL.md",
                 )
 
                 # Add all chunks to parallel arrays
@@ -121,9 +117,9 @@ class FAISSHelpers(SkillAdaptor):
                     ref_content,
                     doc_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file=ref_file.name
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file=ref_file.name,
                 )
 
                 # Add all chunks to parallel arrays
@@ -160,7 +156,7 @@ class FAISSHelpers(SkillAdaptor):
         output_path: Path,
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
-        preserve_code_blocks: bool = True
+        preserve_code_blocks: bool = True,
     ) -> Path:
         """
         Package skill into JSON file for FAISS.
@@ -193,7 +189,7 @@ class FAISSHelpers(SkillAdaptor):
             metadata,
             enable_chunking=enable_chunking,
             chunk_max_tokens=chunk_max_tokens,
-            preserve_code_blocks=preserve_code_blocks
+            preserve_code_blocks=preserve_code_blocks,
         )
 
         # Write to file
diff --git a/src/skill_seekers/cli/adaptors/gemini.py b/src/skill_seekers/cli/adaptors/gemini.py
index 692480f..af74a8a 100644
--- a/src/skill_seekers/cli/adaptors/gemini.py
+++ b/src/skill_seekers/cli/adaptors/gemini.py
@@ -86,7 +86,14 @@ See the references directory for complete documentation with examples and best p
         # Return plain markdown (NO frontmatter)
         return content_body
 
-    def package(self, skill_dir: Path, output_path: Path, enable_chunking: bool = False, chunk_max_tokens: int = 512, preserve_code_blocks: bool = True) -> Path:
+    def package(
+        self,
+        skill_dir: Path,
+        output_path: Path,
+        enable_chunking: bool = False,
+        chunk_max_tokens: int = 512,
+        preserve_code_blocks: bool = True,
+    ) -> Path:
         """
         Package skill into tar.gz file for Gemini.
 
diff --git a/src/skill_seekers/cli/adaptors/haystack.py b/src/skill_seekers/cli/adaptors/haystack.py
index eb9541f..7876ccc 100644
--- a/src/skill_seekers/cli/adaptors/haystack.py
+++ b/src/skill_seekers/cli/adaptors/haystack.py
@@ -29,11 +29,7 @@ class HaystackAdaptor(SkillAdaptor):
     DEFAULT_API_ENDPOINT = None  # No upload endpoint
 
     def format_skill_md(
-        self,
-        skill_dir: Path,
-        metadata: SkillMetadata,
-        enable_chunking: bool = False,
-        **kwargs
+        self, skill_dir: Path, metadata: SkillMetadata, enable_chunking: bool = False, **kwargs
     ) -> str:
         """
         Format skill as JSON array of Haystack Documents.
@@ -73,17 +69,19 @@ class HaystackAdaptor(SkillAdaptor):
                     content,
                     doc_meta,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file="SKILL.md"
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file="SKILL.md",
                 )
 
                 # Add all chunks as documents
                 for chunk_text, chunk_meta in chunks:
-                    documents.append({
-                        "content": chunk_text,
-                        "meta": chunk_meta,
-                    })
+                    documents.append(
+                        {
+                            "content": chunk_text,
+                            "meta": chunk_meta,
+                        }
+                    )
 
         # Convert all reference files using base helper method
         for ref_file, ref_content in self._iterate_references(skill_dir):
@@ -104,17 +102,19 @@ class HaystackAdaptor(SkillAdaptor):
                     ref_content,
                     doc_meta,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file=ref_file.name
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file=ref_file.name,
                 )
 
                 # Add all chunks as documents
                 for chunk_text, chunk_meta in chunks:
-                    documents.append({
-                        "content": chunk_text,
-                        "meta": chunk_meta,
-                    })
+                    documents.append(
+                        {
+                            "content": chunk_text,
+                            "meta": chunk_meta,
+                        }
+                    )
 
         # Return as formatted JSON
         return json.dumps(documents, indent=2, ensure_ascii=False)
@@ -125,7 +125,7 @@ class HaystackAdaptor(SkillAdaptor):
         output_path: Path,
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
-        preserve_code_blocks: bool = True
+        preserve_code_blocks: bool = True,
     ) -> Path:
         """
         Package skill into JSON file for Haystack.
@@ -159,7 +159,7 @@ class HaystackAdaptor(SkillAdaptor):
             metadata,
             enable_chunking=enable_chunking,
             chunk_max_tokens=chunk_max_tokens,
-            preserve_code_blocks=preserve_code_blocks
+            preserve_code_blocks=preserve_code_blocks,
         )
 
         # Write to file
diff --git a/src/skill_seekers/cli/adaptors/langchain.py b/src/skill_seekers/cli/adaptors/langchain.py
index 4481384..d937290 100644
--- a/src/skill_seekers/cli/adaptors/langchain.py
+++ b/src/skill_seekers/cli/adaptors/langchain.py
@@ -29,11 +29,7 @@ class LangChainAdaptor(SkillAdaptor):
     DEFAULT_API_ENDPOINT = None  # No upload endpoint
 
     def format_skill_md(
-        self,
-        skill_dir: Path,
-        metadata: SkillMetadata,
-        enable_chunking: bool = False,
-        **kwargs
+        self, skill_dir: Path, metadata: SkillMetadata, enable_chunking: bool = False, **kwargs
     ) -> str:
         """
         Format skill as JSON array of LangChain Documents.
@@ -73,17 +69,14 @@ class LangChainAdaptor(SkillAdaptor):
                     content,
                     doc_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file="SKILL.md"
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file="SKILL.md",
                 )
 
                 # Add all chunks to documents
                 for chunk_text, chunk_meta in chunks:
-                    documents.append({
-                        "page_content": chunk_text,
-                        "metadata": chunk_meta
-                    })
+                    documents.append({"page_content": chunk_text, "metadata": chunk_meta})
 
         # Convert all reference files using base helper method
         for ref_file, ref_content in self._iterate_references(skill_dir):
@@ -104,17 +97,14 @@ class LangChainAdaptor(SkillAdaptor):
                     ref_content,
                     doc_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file=ref_file.name
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file=ref_file.name,
                 )
 
                 # Add all chunks to documents
                 for chunk_text, chunk_meta in chunks:
-                    documents.append({
-                        "page_content": chunk_text,
-                        "metadata": chunk_meta
-                    })
+                    documents.append({"page_content": chunk_text, "metadata": chunk_meta})
 
         # Return as formatted JSON
         return json.dumps(documents, indent=2, ensure_ascii=False)
@@ -125,7 +115,7 @@ class LangChainAdaptor(SkillAdaptor):
         output_path: Path,
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
-        preserve_code_blocks: bool = True
+        preserve_code_blocks: bool = True,
     ) -> Path:
         """
         Package skill into JSON file for LangChain.
@@ -162,7 +152,7 @@ class LangChainAdaptor(SkillAdaptor):
             metadata,
             enable_chunking=enable_chunking,
             chunk_max_tokens=chunk_max_tokens,
-            preserve_code_blocks=preserve_code_blocks
+            preserve_code_blocks=preserve_code_blocks,
         )
 
         # Write to file
diff --git a/src/skill_seekers/cli/adaptors/llama_index.py b/src/skill_seekers/cli/adaptors/llama_index.py
index f4a0637..7ea6ed9 100644
--- a/src/skill_seekers/cli/adaptors/llama_index.py
+++ b/src/skill_seekers/cli/adaptors/llama_index.py
@@ -42,11 +42,7 @@ class LlamaIndexAdaptor(SkillAdaptor):
         return self._generate_deterministic_id(content, metadata, format="hex")
 
     def format_skill_md(
-        self,
-        skill_dir: Path,
-        metadata: SkillMetadata,
-        enable_chunking: bool = False,
-        **kwargs
+        self, skill_dir: Path, metadata: SkillMetadata, enable_chunking: bool = False, **kwargs
     ) -> str:
         """
         Format skill as JSON array of LlamaIndex Nodes.
@@ -88,19 +84,21 @@ class LlamaIndexAdaptor(SkillAdaptor):
                     content,
                     node_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file="SKILL.md"
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file="SKILL.md",
                 )
 
                 # Add all chunks as nodes
                 for chunk_text, chunk_meta in chunks:
-                    nodes.append({
-                        "text": chunk_text,
-                        "metadata": chunk_meta,
-                        "id_": self._generate_node_id(chunk_text, chunk_meta),
-                        "embedding": None,
-                    })
+                    nodes.append(
+                        {
+                            "text": chunk_text,
+                            "metadata": chunk_meta,
+                            "id_": self._generate_node_id(chunk_text, chunk_meta),
+                            "embedding": None,
+                        }
+                    )
 
         # Convert all reference files using base helper method
         for ref_file, ref_content in self._iterate_references(skill_dir):
@@ -121,19 +119,21 @@ class LlamaIndexAdaptor(SkillAdaptor):
                     ref_content,
                     node_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file=ref_file.name
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file=ref_file.name,
                 )
 
                 # Add all chunks as nodes
                 for chunk_text, chunk_meta in chunks:
-                    nodes.append({
-                        "text": chunk_text,
-                        "metadata": chunk_meta,
-                        "id_": self._generate_node_id(chunk_text, chunk_meta),
-                        "embedding": None,
-                    })
+                    nodes.append(
+                        {
+                            "text": chunk_text,
+                            "metadata": chunk_meta,
+                            "id_": self._generate_node_id(chunk_text, chunk_meta),
+                            "embedding": None,
+                        }
+                    )
 
         # Return as formatted JSON
         return json.dumps(nodes, indent=2, ensure_ascii=False)
@@ -144,7 +144,7 @@ class LlamaIndexAdaptor(SkillAdaptor):
         output_path: Path,
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
-        preserve_code_blocks: bool = True
+        preserve_code_blocks: bool = True,
     ) -> Path:
         """
         Package skill into JSON file for LlamaIndex.
@@ -178,7 +178,7 @@ class LlamaIndexAdaptor(SkillAdaptor):
             metadata,
             enable_chunking=enable_chunking,
             chunk_max_tokens=chunk_max_tokens,
-            preserve_code_blocks=preserve_code_blocks
+            preserve_code_blocks=preserve_code_blocks,
         )
 
         # Write to file
diff --git a/src/skill_seekers/cli/adaptors/markdown.py b/src/skill_seekers/cli/adaptors/markdown.py
index 057f662..5d60033 100644
--- a/src/skill_seekers/cli/adaptors/markdown.py
+++ b/src/skill_seekers/cli/adaptors/markdown.py
@@ -81,7 +81,14 @@ Browse the reference files for detailed information on each topic. All files are
         # Return pure markdown (no frontmatter, no special formatting)
         return content_body
 
-    def package(self, skill_dir: Path, output_path: Path, enable_chunking: bool = False, chunk_max_tokens: int = 512, preserve_code_blocks: bool = True) -> Path:
+    def package(
+        self,
+        skill_dir: Path,
+        output_path: Path,
+        enable_chunking: bool = False,
+        chunk_max_tokens: int = 512,
+        preserve_code_blocks: bool = True,
+    ) -> Path:
         """
         Package skill into ZIP file with markdown documentation.
 
diff --git a/src/skill_seekers/cli/adaptors/openai.py b/src/skill_seekers/cli/adaptors/openai.py
index 5384238..e6437af 100644
--- a/src/skill_seekers/cli/adaptors/openai.py
+++ b/src/skill_seekers/cli/adaptors/openai.py
@@ -103,7 +103,14 @@ Always prioritize accuracy by consulting the attached documentation files before
         # Return plain text instructions (NO frontmatter)
         return content_body
 
-    def package(self, skill_dir: Path, output_path: Path, enable_chunking: bool = False, chunk_max_tokens: int = 512, preserve_code_blocks: bool = True) -> Path:
+    def package(
+        self,
+        skill_dir: Path,
+        output_path: Path,
+        enable_chunking: bool = False,
+        chunk_max_tokens: int = 512,
+        preserve_code_blocks: bool = True,
+    ) -> Path:
         """
         Package skill into ZIP file for OpenAI Assistants.
 
diff --git a/src/skill_seekers/cli/adaptors/qdrant.py b/src/skill_seekers/cli/adaptors/qdrant.py
index b74815e..d201510 100644
--- a/src/skill_seekers/cli/adaptors/qdrant.py
+++ b/src/skill_seekers/cli/adaptors/qdrant.py
@@ -44,11 +44,7 @@ class QdrantAdaptor(SkillAdaptor):
         return self._generate_deterministic_id(content, metadata, format="uuid5")
 
     def format_skill_md(
-        self,
-        skill_dir: Path,
-        metadata: SkillMetadata,
-        enable_chunking: bool = False,
-        **kwargs
+        self, skill_dir: Path, metadata: SkillMetadata, enable_chunking: bool = False, **kwargs
     ) -> str:
         """
         Format skill as Qdrant collection JSON.
@@ -87,30 +83,35 @@ class QdrantAdaptor(SkillAdaptor):
                     content,
                     payload_meta,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file="SKILL.md"
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file="SKILL.md",
                 )
 
                 # Add all chunks as points
                 for chunk_text, chunk_meta in chunks:
-                    point_id = self._generate_point_id(chunk_text, {
-                        "source": chunk_meta.get("source", metadata.name),
-                        "file": chunk_meta.get("file", "SKILL.md")
-                    })
-
-                    points.append({
-                        "id": point_id,
-                        "vector": None,  # User will generate embeddings
-                        "payload": {
-                            "content": chunk_text,
+                    point_id = self._generate_point_id(
+                        chunk_text,
+                        {
                             "source": chunk_meta.get("source", metadata.name),
-                            "category": chunk_meta.get("category", "overview"),
                             "file": chunk_meta.get("file", "SKILL.md"),
-                            "type": chunk_meta.get("type", "documentation"),
-                            "version": chunk_meta.get("version", metadata.version),
+                        },
+                    )
+
+                    points.append(
+                        {
+                            "id": point_id,
+                            "vector": None,  # User will generate embeddings
+                            "payload": {
+                                "content": chunk_text,
+                                "source": chunk_meta.get("source", metadata.name),
+                                "category": chunk_meta.get("category", "overview"),
+                                "file": chunk_meta.get("file", "SKILL.md"),
+                                "type": chunk_meta.get("type", "documentation"),
+                                "version": chunk_meta.get("version", metadata.version),
+                            },
                         }
-                    })
+                    )
 
         # Convert all reference files using base helper method
         for ref_file, ref_content in self._iterate_references(skill_dir):
@@ -130,30 +131,35 @@ class QdrantAdaptor(SkillAdaptor):
                     ref_content,
                     payload_meta,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file=ref_file.name
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file=ref_file.name,
                 )
 
                 # Add all chunks as points
                 for chunk_text, chunk_meta in chunks:
-                    point_id = self._generate_point_id(chunk_text, {
-                        "source": chunk_meta.get("source", metadata.name),
-                        "file": chunk_meta.get("file", ref_file.name)
-                    })
-
-                    points.append({
-                        "id": point_id,
-                        "vector": None,  # User will generate embeddings
-                        "payload": {
-                            "content": chunk_text,
+                    point_id = self._generate_point_id(
+                        chunk_text,
+                        {
                             "source": chunk_meta.get("source", metadata.name),
-                            "category": chunk_meta.get("category", category),
                             "file": chunk_meta.get("file", ref_file.name),
-                            "type": chunk_meta.get("type", "reference"),
-                            "version": chunk_meta.get("version", metadata.version),
+                        },
+                    )
+
+                    points.append(
+                        {
+                            "id": point_id,
+                            "vector": None,  # User will generate embeddings
+                            "payload": {
+                                "content": chunk_text,
+                                "source": chunk_meta.get("source", metadata.name),
+                                "category": chunk_meta.get("category", category),
+                                "file": chunk_meta.get("file", ref_file.name),
+                                "type": chunk_meta.get("type", "reference"),
+                                "version": chunk_meta.get("version", metadata.version),
+                            },
                         }
-                    })
+                    )
 
         # Qdrant configuration
         config = {
@@ -184,7 +190,7 @@ class QdrantAdaptor(SkillAdaptor):
         output_path: Path,
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
-        preserve_code_blocks: bool = True
+        preserve_code_blocks: bool = True,
     ) -> Path:
         """
         Package skill into JSON file for Qdrant.
@@ -217,7 +223,7 @@ class QdrantAdaptor(SkillAdaptor):
             metadata,
             enable_chunking=enable_chunking,
             chunk_max_tokens=chunk_max_tokens,
-            preserve_code_blocks=preserve_code_blocks
+            preserve_code_blocks=preserve_code_blocks,
         )
 
         # Write to file
diff --git a/src/skill_seekers/cli/adaptors/streaming_adaptor.py b/src/skill_seekers/cli/adaptors/streaming_adaptor.py
index 164a3ed..09319e2 100644
--- a/src/skill_seekers/cli/adaptors/streaming_adaptor.py
+++ b/src/skill_seekers/cli/adaptors/streaming_adaptor.py
@@ -36,7 +36,7 @@ class StreamingAdaptorMixin:
         chunk_size: int = 4000,
         chunk_overlap: int = 200,
         batch_size: int = 100,
-        progress_callback: callable | None = None
+        progress_callback: callable | None = None,
     ) -> Path:
         """
         Package skill using streaming ingestion.
@@ -60,9 +60,7 @@ class StreamingAdaptorMixin:
 
         # Initialize streaming ingester
         ingester = StreamingIngester(
-            chunk_size=chunk_size,
-            chunk_overlap=chunk_overlap,
-            batch_size=batch_size
+            chunk_size=chunk_size, chunk_overlap=chunk_overlap, batch_size=batch_size
         )
 
         print(f"\n📊 Streaming ingestion starting...")
@@ -77,9 +75,11 @@ class StreamingAdaptorMixin:
             nonlocal last_update
             # Update every 10 chunks
             if progress.processed_chunks - last_update >= 10:
-                print(f"   {progress.progress_percent:.1f}% - "
-                      f"{progress.processed_chunks}/{progress.total_chunks} chunks "
-                      f"({progress.chunks_per_second:.1f} chunks/sec)")
+                print(
+                    f"   {progress.progress_percent:.1f}% - "
+                    f"{progress.processed_chunks}/{progress.total_chunks} chunks "
+                    f"({progress.chunks_per_second:.1f} chunks/sec)"
+                )
                 last_update = progress.processed_chunks
 
             if progress_callback:
@@ -97,10 +97,7 @@ class StreamingAdaptorMixin:
 
         # Convert chunks to platform format
         print(f"\n📦 Converting to {self.PLATFORM_NAME} format...")
-        package_data = self._convert_chunks_to_platform_format(
-            all_chunks,
-            skill_dir.name
-        )
+        package_data = self._convert_chunks_to_platform_format(all_chunks, skill_dir.name)
 
         # Determine output filename
         if output_path.is_dir() or str(output_path).endswith("/"):
@@ -114,8 +111,7 @@ class StreamingAdaptorMixin:
         # Write output
         output_path.parent.mkdir(parents=True, exist_ok=True)
         output_path.write_text(
-            json.dumps(package_data, indent=2, ensure_ascii=False),
-            encoding="utf-8"
+            json.dumps(package_data, indent=2, ensure_ascii=False), encoding="utf-8"
         )
 
         print(f"✅ Package created: {output_path}")
@@ -124,9 +120,7 @@ class StreamingAdaptorMixin:
         return output_path
 
     def _convert_chunks_to_platform_format(
-        self,
-        chunks: list[tuple[str, dict]],
-        skill_name: str
+        self, chunks: list[tuple[str, dict]], skill_name: str
     ) -> dict:
         """
         Convert chunks to platform-specific format.
@@ -156,14 +150,11 @@ class StreamingAdaptorMixin:
             "metadatas": metadatas,
             "ids": ids,
             "total_chunks": len(chunks),
-            "streaming": True
+            "streaming": True,
         }
 
     def estimate_chunks(
-        self,
-        skill_dir: Path,
-        chunk_size: int = 4000,
-        chunk_overlap: int = 200
+        self, skill_dir: Path, chunk_size: int = 4000, chunk_overlap: int = 200
     ) -> dict[str, Any]:
         """
         Estimate chunking for a skill directory.
@@ -179,10 +170,7 @@ class StreamingAdaptorMixin:
             Estimation statistics
         """
         skill_dir = Path(skill_dir)
-        StreamingIngester(
-            chunk_size=chunk_size,
-            chunk_overlap=chunk_overlap
-        )
+        StreamingIngester(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
 
         # Count files and estimate chunks
         total_docs = 0
@@ -201,11 +189,9 @@ class StreamingAdaptorMixin:
             total_chars += char_count
             estimated_chunks += chunk_count
 
-            file_stats.append({
-                "file": "SKILL.md",
-                "chars": char_count,
-                "estimated_chunks": chunk_count
-            })
+            file_stats.append(
+                {"file": "SKILL.md", "chars": char_count, "estimated_chunks": chunk_count}
+            )
 
         # Reference files
         refs_dir = skill_dir / "references"
@@ -214,17 +200,21 @@ class StreamingAdaptorMixin:
                 if ref_file.is_file() and not ref_file.name.startswith("."):
                     content = ref_file.read_text(encoding="utf-8")
                     char_count = len(content)
-                    chunk_count = max(1, (char_count - chunk_overlap) // (chunk_size - chunk_overlap) + 1)
+                    chunk_count = max(
+                        1, (char_count - chunk_overlap) // (chunk_size - chunk_overlap) + 1
+                    )
 
                     total_docs += 1
                     total_chars += char_count
                     estimated_chunks += chunk_count
 
-                    file_stats.append({
-                        "file": ref_file.name,
-                        "chars": char_count,
-                        "estimated_chunks": chunk_count
-                    })
+                    file_stats.append(
+                        {
+                            "file": ref_file.name,
+                            "chars": char_count,
+                            "estimated_chunks": chunk_count,
+                        }
+                    )
 
         return {
             "skill_name": skill_dir.name,
@@ -235,7 +225,7 @@ class StreamingAdaptorMixin:
             "chunk_overlap": chunk_overlap,
             "file_stats": file_stats,
             "estimated_memory_mb": (total_chars * 2) / (1024 * 1024),  # UTF-8 estimate
-            "recommended_streaming": total_chars > 1_000_000 or total_docs > 100
+            "recommended_streaming": total_chars > 1_000_000 or total_docs > 100,
         }
 
 
@@ -251,25 +241,27 @@ class StreamingLangChainAdaptor(StreamingAdaptorMixin):
         documents = []
 
         for chunk_text, chunk_meta in chunks:
-            documents.append({
-                "page_content": chunk_text,
-                "metadata": {
-                    "source": chunk_meta["source"],
-                    "category": chunk_meta["category"],
-                    "file": chunk_meta["file"],
-                    "chunk_id": chunk_meta["chunk_id"],
-                    "chunk_index": chunk_meta["chunk_index"],
-                    "total_chunks": chunk_meta["total_chunks"],
-                    "type": chunk_meta.get("type", "documentation"),
-                    "version": chunk_meta.get("version", "1.0.0"),
+            documents.append(
+                {
+                    "page_content": chunk_text,
+                    "metadata": {
+                        "source": chunk_meta["source"],
+                        "category": chunk_meta["category"],
+                        "file": chunk_meta["file"],
+                        "chunk_id": chunk_meta["chunk_id"],
+                        "chunk_index": chunk_meta["chunk_index"],
+                        "total_chunks": chunk_meta["total_chunks"],
+                        "type": chunk_meta.get("type", "documentation"),
+                        "version": chunk_meta.get("version", "1.0.0"),
+                    },
                 }
-            })
+            )
 
         return {
             "documents": documents,
             "total_chunks": len(chunks),
             "streaming": True,
-            "format": "LangChain Document"
+            "format": "LangChain Document",
         }
 
 
@@ -287,14 +279,16 @@ class StreamingChromaAdaptor(StreamingAdaptorMixin):
 
         for chunk_text, chunk_meta in chunks:
             documents.append(chunk_text)
-            metadatas.append({
-                "source": chunk_meta["source"],
-                "category": chunk_meta["category"],
-                "file": chunk_meta["file"],
-                "chunk_index": chunk_meta["chunk_index"],
-                "total_chunks": chunk_meta["total_chunks"],
-                "type": chunk_meta.get("type", "documentation"),
-            })
+            metadatas.append(
+                {
+                    "source": chunk_meta["source"],
+                    "category": chunk_meta["category"],
+                    "file": chunk_meta["file"],
+                    "chunk_index": chunk_meta["chunk_index"],
+                    "total_chunks": chunk_meta["total_chunks"],
+                    "type": chunk_meta.get("type", "documentation"),
+                }
+            )
             ids.append(chunk_meta["chunk_id"])
 
         return {
@@ -303,7 +297,7 @@ class StreamingChromaAdaptor(StreamingAdaptorMixin):
             "ids": ids,
             "collection_name": skill_name.replace("_", "-"),
             "total_chunks": len(chunks),
-            "streaming": True
+            "streaming": True,
         }
 
 
@@ -339,11 +333,7 @@ def demo_streaming():
     print("=" * 60)
 
     output = adaptor.package_streaming(
-        skill_dir,
-        Path("output"),
-        chunk_size=2000,
-        chunk_overlap=100,
-        batch_size=50
+        skill_dir, Path("output"), chunk_size=2000, chunk_overlap=100, batch_size=50
     )
 
     print(f"\n✅ Complete! Output: {output}")
diff --git a/src/skill_seekers/cli/adaptors/weaviate.py b/src/skill_seekers/cli/adaptors/weaviate.py
index 5159dcd..c06081c 100644
--- a/src/skill_seekers/cli/adaptors/weaviate.py
+++ b/src/skill_seekers/cli/adaptors/weaviate.py
@@ -104,11 +104,7 @@ class WeaviateAdaptor(SkillAdaptor):
         }
 
     def format_skill_md(
-        self,
-        skill_dir: Path,
-        metadata: SkillMetadata,
-        enable_chunking: bool = False,
-        **kwargs
+        self, skill_dir: Path, metadata: SkillMetadata, enable_chunking: bool = False, **kwargs
     ) -> str:
         """
         Format skill as JSON for Weaviate ingestion.
@@ -148,24 +144,26 @@ class WeaviateAdaptor(SkillAdaptor):
                     content,
                     obj_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file="SKILL.md"
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file="SKILL.md",
                 )
 
                 # Add all chunks as objects
                 for chunk_text, chunk_meta in chunks:
-                    objects.append({
-                        "id": self._generate_uuid(chunk_text, chunk_meta),
-                        "properties": {
-                            "content": chunk_text,
-                            "source": chunk_meta.get("source", metadata.name),
-                            "category": chunk_meta.get("category", "overview"),
-                            "file": chunk_meta.get("file", "SKILL.md"),
-                            "type": chunk_meta.get("type", "documentation"),
-                            "version": chunk_meta.get("version", metadata.version),
-                        },
-                    })
+                    objects.append(
+                        {
+                            "id": self._generate_uuid(chunk_text, chunk_meta),
+                            "properties": {
+                                "content": chunk_text,
+                                "source": chunk_meta.get("source", metadata.name),
+                                "category": chunk_meta.get("category", "overview"),
+                                "file": chunk_meta.get("file", "SKILL.md"),
+                                "type": chunk_meta.get("type", "documentation"),
+                                "version": chunk_meta.get("version", metadata.version),
+                            },
+                        }
+                    )
 
         # Convert all reference files using base helper method
         for ref_file, ref_content in self._iterate_references(skill_dir):
@@ -186,24 +184,26 @@ class WeaviateAdaptor(SkillAdaptor):
                     ref_content,
                     obj_metadata,
                     enable_chunking=enable_chunking,
-                    chunk_max_tokens=kwargs.get('chunk_max_tokens', 512),
-                    preserve_code_blocks=kwargs.get('preserve_code_blocks', True),
-                    source_file=ref_file.name
+                    chunk_max_tokens=kwargs.get("chunk_max_tokens", 512),
+                    preserve_code_blocks=kwargs.get("preserve_code_blocks", True),
+                    source_file=ref_file.name,
                 )
 
                 # Add all chunks as objects
                 for chunk_text, chunk_meta in chunks:
-                    objects.append({
-                        "id": self._generate_uuid(chunk_text, chunk_meta),
-                        "properties": {
-                            "content": chunk_text,
-                            "source": chunk_meta.get("source", metadata.name),
-                            "category": chunk_meta.get("category", category),
-                            "file": chunk_meta.get("file", ref_file.name),
-                            "type": chunk_meta.get("type", "reference"),
-                            "version": chunk_meta.get("version", metadata.version),
-                        },
-                    })
+                    objects.append(
+                        {
+                            "id": self._generate_uuid(chunk_text, chunk_meta),
+                            "properties": {
+                                "content": chunk_text,
+                                "source": chunk_meta.get("source", metadata.name),
+                                "category": chunk_meta.get("category", category),
+                                "file": chunk_meta.get("file", ref_file.name),
+                                "type": chunk_meta.get("type", "reference"),
+                                "version": chunk_meta.get("version", metadata.version),
+                            },
+                        }
+                    )
 
         # Generate schema
         class_name = "".join(word.capitalize() for word in metadata.name.split("_"))
@@ -222,7 +222,7 @@ class WeaviateAdaptor(SkillAdaptor):
         output_path: Path,
         enable_chunking: bool = False,
         chunk_max_tokens: int = 512,
-        preserve_code_blocks: bool = True
+        preserve_code_blocks: bool = True,
     ) -> Path:
         """
         Package skill into JSON file for Weaviate.
@@ -258,7 +258,7 @@ class WeaviateAdaptor(SkillAdaptor):
             metadata,
             enable_chunking=enable_chunking,
             chunk_max_tokens=chunk_max_tokens,
-            preserve_code_blocks=preserve_code_blocks
+            preserve_code_blocks=preserve_code_blocks,
         )
 
         # Write to file
@@ -310,7 +310,7 @@ class WeaviateAdaptor(SkillAdaptor):
         except ImportError:
             return {
                 "success": False,
-                "message": "weaviate-client not installed. Run: pip install weaviate-client"
+                "message": "weaviate-client not installed. Run: pip install weaviate-client",
             }
 
         # Load package
@@ -319,16 +319,16 @@ class WeaviateAdaptor(SkillAdaptor):
 
         # Connect to Weaviate
         try:
-            if kwargs.get('use_cloud') and api_key:
+            if kwargs.get("use_cloud") and api_key:
                 # Weaviate Cloud
                 print(f"🌐 Connecting to Weaviate Cloud: {kwargs.get('cluster_url')}")
                 client = weaviate.Client(
-                    url=kwargs.get('cluster_url'),
-                    auth_client_secret=weaviate.AuthApiKey(api_key=api_key)
+                    url=kwargs.get("cluster_url"),
+                    auth_client_secret=weaviate.AuthApiKey(api_key=api_key),
                 )
             else:
                 # Local Weaviate instance
-                weaviate_url = kwargs.get('weaviate_url', 'http://localhost:8080')
+                weaviate_url = kwargs.get("weaviate_url", "http://localhost:8080")
                 print(f"🌐 Connecting to Weaviate at: {weaviate_url}")
                 client = weaviate.Client(url=weaviate_url)
 
@@ -336,69 +336,67 @@ class WeaviateAdaptor(SkillAdaptor):
             if not client.is_ready():
                 return {
                     "success": False,
-                    "message": "Weaviate server not ready. Make sure Weaviate is running:\n  docker run -p 8080:8080 semitechnologies/weaviate:latest"
+                    "message": "Weaviate server not ready. Make sure Weaviate is running:\n  docker run -p 8080:8080 semitechnologies/weaviate:latest",
                 }
 
         except Exception as e:
             return {
                 "success": False,
-                "message": f"Failed to connect to Weaviate: {e}\n\nMake sure Weaviate is running or provide correct credentials."
+                "message": f"Failed to connect to Weaviate: {e}\n\nMake sure Weaviate is running or provide correct credentials.",
             }
 
         # Create schema
         try:
-            client.schema.create_class(data['schema'])
+            client.schema.create_class(data["schema"])
             print(f"✅ Created schema: {data['class_name']}")
         except Exception as e:
             if "already exists" in str(e).lower():
                 print(f"ℹ️  Schema already exists: {data['class_name']}")
             else:
-                return {
-                    "success": False,
-                    "message": f"Schema creation failed: {e}"
-                }
+                return {"success": False, "message": f"Schema creation failed: {e}"}
 
         # Handle embeddings
-        embedding_function = kwargs.get('embedding_function')
+        embedding_function = kwargs.get("embedding_function")
 
         try:
             with client.batch as batch:
                 batch.batch_size = 100
 
-                if embedding_function == 'openai':
+                if embedding_function == "openai":
                     # Generate embeddings with OpenAI
                     print("🔄 Generating OpenAI embeddings and uploading...")
                     embeddings = self._generate_openai_embeddings(
-                        [obj['properties']['content'] for obj in data['objects']],
-                        api_key=kwargs.get('openai_api_key')
+                        [obj["properties"]["content"] for obj in data["objects"]],
+                        api_key=kwargs.get("openai_api_key"),
                     )
 
-                    for i, obj in enumerate(data['objects']):
+                    for i, obj in enumerate(data["objects"]):
                         batch.add_data_object(
-                            data_object=obj['properties'],
-                            class_name=data['class_name'],
-                            uuid=obj['id'],
-                            vector=embeddings[i]
+                            data_object=obj["properties"],
+                            class_name=data["class_name"],
+                            uuid=obj["id"],
+                            vector=embeddings[i],
                         )
 
                         if (i + 1) % 100 == 0:
                             print(f"  ✓ Uploaded {i + 1}/{len(data['objects'])} objects")
 
-                elif embedding_function == 'sentence-transformers':
+                elif embedding_function == "sentence-transformers":
                     # Use sentence-transformers
                     print("🔄 Generating sentence-transformer embeddings and uploading...")
                     try:
                         from sentence_transformers import SentenceTransformer
-                        model = SentenceTransformer('all-MiniLM-L6-v2')
-                        contents = [obj['properties']['content'] for obj in data['objects']]
+
+                        model = SentenceTransformer("all-MiniLM-L6-v2")
+                        contents = [obj["properties"]["content"] for obj in data["objects"]]
                         embeddings = model.encode(contents, show_progress_bar=True).tolist()
 
-                        for i, obj in enumerate(data['objects']):
+                        for i, obj in enumerate(data["objects"]):
                             batch.add_data_object(
-                                data_object=obj['properties'],
-                                class_name=data['class_name'],
-                                uuid=obj['id'],
-                                vector=embeddings[i]
+                                data_object=obj["properties"],
+                                class_name=data["class_name"],
+                                uuid=obj["id"],
+                                vector=embeddings[i],
                             )
 
                             if (i + 1) % 100 == 0:
@@ -407,42 +405,37 @@ class WeaviateAdaptor(SkillAdaptor):
                     except ImportError:
                         return {
                             "success": False,
-                            "message": "sentence-transformers not installed. Run: pip install sentence-transformers"
+                            "message": "sentence-transformers not installed. Run: pip install sentence-transformers",
                         }
 
                 else:
                     # No embeddings - Weaviate will use its configured vectorizer
                     print("🔄 Uploading objects (Weaviate will generate embeddings)...")
-                    for i, obj in enumerate(data['objects']):
+                    for i, obj in enumerate(data["objects"]):
                         batch.add_data_object(
-                            data_object=obj['properties'],
-                            class_name=data['class_name'],
-                            uuid=obj['id']
+                            data_object=obj["properties"],
+                            class_name=data["class_name"],
+                            uuid=obj["id"],
                         )
 
                         if (i + 1) % 100 == 0:
                             print(f"  ✓ Uploaded {i + 1}/{len(data['objects'])} objects")
 
-            count = len(data['objects'])
+            count = len(data["objects"])
             print(f"✅ Upload complete! {count} objects added to Weaviate")
 
             return {
                 "success": True,
                 "message": f"Uploaded {count} objects to Weaviate class '{data['class_name']}'",
-                "class_name": data['class_name'],
-                "count": count
+                "class_name": data["class_name"],
+                "count": count,
             }
 
         except Exception as e:
-            return {
-                "success": False,
-                "message": f"Upload failed: {e}"
-            }
+            return {"success": False, "message": f"Upload failed: {e}"}
 
     def _generate_openai_embeddings(
-        self,
-        documents: list[str],
-        api_key: str = None
+        self, documents: list[str], api_key: str = None
     ) -> list[list[float]]:
         """
         Generate embeddings using OpenAI API.
@@ -455,12 +448,13 @@ class WeaviateAdaptor(SkillAdaptor):
             List of embedding vectors
         """
         import os
+
         try:
             from openai import OpenAI
         except ImportError:
             raise ImportError("openai not installed. Run: pip install openai") from None
 
-        api_key = api_key or os.getenv('OPENAI_API_KEY')
+        api_key = api_key or os.getenv("OPENAI_API_KEY")
         if not api_key:
             raise ValueError("OPENAI_API_KEY not set. Set via env var or --openai-api-key")
 
@@ -473,14 +467,16 @@ class WeaviateAdaptor(SkillAdaptor):
         print(f"  Generating embeddings for {len(documents)} documents...")
 
         for i in range(0, len(documents), batch_size):
-            batch = documents[i:i+batch_size]
+            batch = documents[i : i + batch_size]
             try:
                 response = client.embeddings.create(
                     input=batch,
-                    model="text-embedding-3-small"  # Cheapest, fastest
+                    model="text-embedding-3-small",  # Cheapest, fastest
                 )
                 embeddings.extend([item.embedding for item in response.data])
-                print(f"  ✓ Generated {min(i+batch_size, len(documents))}/{len(documents)} embeddings")
+                print(
+                    f"  ✓ Generated {min(i + batch_size, len(documents))}/{len(documents)} embeddings"
+                )
             except Exception as e:
                 raise Exception(f"OpenAI embedding generation failed: {e}") from e
 
diff --git a/src/skill_seekers/cli/architectural_pattern_detector.py b/src/skill_seekers/cli/architectural_pattern_detector.py
index 66b0a25..e4d86af 100644
--- a/src/skill_seekers/cli/architectural_pattern_detector.py
+++ b/src/skill_seekers/cli/architectural_pattern_detector.py
@@ -101,10 +101,38 @@ class ArchitecturalPatternDetector:
         # Web Frameworks
         "Django": ["django", "manage.py", "settings.py", "urls.py"],
         "Flask": ["flask", "app.py", "wsgi.py"],
-        "Spring": ["springframework", "org.springframework", "@Controller", "@Service", "@Repository"],
-        "ASP.NET": ["Microsoft.AspNetCore", "System.Web", "Controllers", "Models", "Views", ".cshtml", "Startup.cs"],
-        "Rails": ["rails", "action", "app/models", "app/views", "app/controllers", "config/routes.rb"],
-        "Angular": ["@angular", "angular", "app.module.ts", "@Component", "@Injectable", "angular.json"],
+        "Spring": [
+            "springframework",
+            "org.springframework",
+            "@Controller",
+            "@Service",
+            "@Repository",
+        ],
+        "ASP.NET": [
+            "Microsoft.AspNetCore",
+            "System.Web",
+            "Controllers",
+            "Models",
+            "Views",
+            ".cshtml",
+            "Startup.cs",
+        ],
+        "Rails": [
+            "rails",
+            "action",
+            "app/models",
+            "app/views",
+            "app/controllers",
+            "config/routes.rb",
+        ],
+        "Angular": [
+            "@angular",
+            "angular",
+            "app.module.ts",
+            "@Component",
+            "@Injectable",
+            "angular.json",
+        ],
         "React": ["react", "package.json", "components"],
         "Vue.js": ["vue", ".vue", "components"],
         "Express": ["express", "app.js", "routes"],
@@ -208,7 +236,9 @@ class ArchitecturalPatternDetector:
 
         # Create searchable import string
         import_content = " ".join(all_imports)
-        logger.debug(f"Collected {len(all_imports)} imports from {len([f for f in files if f.get('imports')])} files for framework detection")
+        logger.debug(
+            f"Collected {len(all_imports)} imports from {len([f for f in files if f.get('imports')])} files for framework detection"
+        )
 
         # Also check actual directory structure for game engine markers
         # (project.godot, .unity, .uproject are config files, not in analyzed files)
@@ -245,7 +275,9 @@ class ArchitecturalPatternDetector:
             # Check in file paths, directory structure, AND imports
             path_matches = sum(1 for marker in markers if marker.lower() in all_content.lower())
             dir_matches = sum(1 for marker in markers if marker.lower() in dir_content.lower())
-            import_matches = sum(1 for marker in markers if marker.lower() in import_content.lower())
+            import_matches = sum(
+                1 for marker in markers if marker.lower() in import_content.lower()
+            )
 
             # Strategy: Prioritize import-based detection (more accurate)
             # If we have import matches, they're strong signals - use them alone
@@ -257,7 +289,9 @@ class ArchitecturalPatternDetector:
             elif (path_matches + dir_matches) >= 2:
                 # Path/directory-based detection (requires 2+ matches)
                 detected.append(framework)
-                logger.info(f"  📦 Detected framework: {framework} (path:{path_matches} dir:{dir_matches})")
+                logger.info(
+                    f"  📦 Detected framework: {framework} (path:{path_matches} dir:{dir_matches})"
+                )
 
         return detected
 
diff --git a/src/skill_seekers/cli/benchmark_cli.py b/src/skill_seekers/cli/benchmark_cli.py
index 59927fd..c9fefaa 100644
--- a/src/skill_seekers/cli/benchmark_cli.py
+++ b/src/skill_seekers/cli/benchmark_cli.py
@@ -77,7 +77,9 @@ def run_embedding_benchmark(runner, config):
             with bench.timer("batch_embedding"), bench.memory("batch_embedding"):
                 embeddings = generator.generate_batch(texts, model=model)
 
-            bench.metric("embeddings_per_sec", len(embeddings) / bench.result.timings[-1].duration, "emb/sec")
+            bench.metric(
+                "embeddings_per_sec", len(embeddings) / bench.result.timings[-1].duration, "emb/sec"
+            )
 
     name = config.get("name", "embedding-benchmark")
     report = runner.run(name, benchmark_func)
@@ -97,7 +99,7 @@ def run_storage_benchmark(runner, config):
         storage = get_storage_adaptor(provider, bucket=bucket)
 
         # Create test file
-        with NamedTemporaryFile(mode='w', delete=False, suffix='.txt') as f:
+        with NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f:
             f.write("Test data" * 1000)
             test_file = Path(f.name)
 
@@ -128,10 +130,7 @@ def compare_command(args):
     """Compare two benchmarks."""
     runner = BenchmarkRunner()
 
-    comparison = runner.compare(
-        baseline_path=Path(args.baseline),
-        current_path=Path(args.current)
-    )
+    comparison = runner.compare(baseline_path=Path(args.baseline), current_path=Path(args.current))
 
     print(f"\n📊 Comparison: {comparison.name}\n")
     print(f"Overall: {comparison.overall_improvement}\n")
@@ -213,7 +212,7 @@ def cleanup_command(args):
 def main():
     """Main entry point."""
     parser = argparse.ArgumentParser(
-        description='Performance benchmarking suite',
+        description="Performance benchmarking suite",
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
 Examples:
@@ -233,54 +232,46 @@ Examples:
 
   # Cleanup old benchmarks
   skill-seekers-benchmark cleanup --keep 5
-        """
+        """,
     )
 
-    subparsers = parser.add_subparsers(dest='command', help='Command to execute')
+    subparsers = parser.add_subparsers(dest="command", help="Command to execute")
 
     # Run command
-    run_parser = subparsers.add_parser('run', help='Run benchmark')
-    run_parser.add_argument('--config', required=True, help='Benchmark config file')
+    run_parser = subparsers.add_parser("run", help="Run benchmark")
+    run_parser.add_argument("--config", required=True, help="Benchmark config file")
     run_parser.add_argument(
-        '--output-dir', '-o',
-        default='benchmarks',
-        help='Output directory (default: benchmarks)'
+        "--output-dir", "-o", default="benchmarks", help="Output directory (default: benchmarks)"
     )
 
     # Compare command
-    compare_parser = subparsers.add_parser('compare', help='Compare two benchmarks')
-    compare_parser.add_argument('--baseline', required=True, help='Baseline benchmark')
-    compare_parser.add_argument('--current', required=True, help='Current benchmark')
+    compare_parser = subparsers.add_parser("compare", help="Compare two benchmarks")
+    compare_parser.add_argument("--baseline", required=True, help="Baseline benchmark")
+    compare_parser.add_argument("--current", required=True, help="Current benchmark")
     compare_parser.add_argument(
-        '--fail-on-regression',
-        action='store_true',
-        help='Exit with error if regressions detected'
+        "--fail-on-regression", action="store_true", help="Exit with error if regressions detected"
     )
 
     # List command
-    list_parser = subparsers.add_parser('list', help='List saved benchmarks')
+    list_parser = subparsers.add_parser("list", help="List saved benchmarks")
     list_parser.add_argument(
-        '--output-dir', '-o',
-        default='benchmarks',
-        help='Benchmark directory (default: benchmarks)'
+        "--output-dir", "-o", default="benchmarks", help="Benchmark directory (default: benchmarks)"
     )
 
     # Show command
-    show_parser = subparsers.add_parser('show', help='Show benchmark details')
-    show_parser.add_argument('path', help='Path to benchmark file')
+    show_parser = subparsers.add_parser("show", help="Show benchmark details")
+    show_parser.add_argument("path", help="Path to benchmark file")
 
     # Cleanup command
-    cleanup_parser = subparsers.add_parser('cleanup', help='Cleanup old benchmarks')
+    cleanup_parser = subparsers.add_parser("cleanup", help="Cleanup old benchmarks")
     cleanup_parser.add_argument(
-        '--output-dir', '-o',
-        default='benchmarks',
-        help='Benchmark directory (default: benchmarks)'
+        "--output-dir", "-o", default="benchmarks", help="Benchmark directory (default: benchmarks)"
     )
     cleanup_parser.add_argument(
-        '--keep',
+        "--keep",
         type=int,
         default=5,
-        help='Number of latest benchmarks to keep per name (default: 5)'
+        help="Number of latest benchmarks to keep per name (default: 5)",
     )
 
     args = parser.parse_args()
@@ -290,20 +281,20 @@ Examples:
         sys.exit(1)
 
     try:
-        if args.command == 'run':
+        if args.command == "run":
             run_command(args)
-        elif args.command == 'compare':
+        elif args.command == "compare":
             compare_command(args)
-        elif args.command == 'list':
+        elif args.command == "list":
             list_command(args)
-        elif args.command == 'show':
+        elif args.command == "show":
             show_command(args)
-        elif args.command == 'cleanup':
+        elif args.command == "cleanup":
             cleanup_command(args)
     except Exception as e:
         print(f"\n❌ Error: {e}", file=sys.stderr)
         sys.exit(1)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/src/skill_seekers/cli/cloud_storage_cli.py b/src/skill_seekers/cli/cloud_storage_cli.py
index 8f25cb3..f8fa950 100644
--- a/src/skill_seekers/cli/cloud_storage_cli.py
+++ b/src/skill_seekers/cli/cloud_storage_cli.py
@@ -15,18 +15,13 @@ from .storage import get_storage_adaptor
 def upload_command(args):
     """Handle upload subcommand."""
     adaptor = get_storage_adaptor(
-        args.provider,
-        bucket=args.bucket,
-        container=args.container,
-        **parse_extra_args(args.extra)
+        args.provider, bucket=args.bucket, container=args.container, **parse_extra_args(args.extra)
     )
 
     if Path(args.local_path).is_dir():
         print(f"📁 Uploading directory: {args.local_path}")
         uploaded_files = adaptor.upload_directory(
-            args.local_path,
-            args.remote_path,
-            exclude_patterns=args.exclude
+            args.local_path, args.remote_path, exclude_patterns=args.exclude
         )
         print(f"✅ Uploaded {len(uploaded_files)} files")
         if args.verbose:
@@ -41,19 +36,13 @@ def upload_command(args):
 def download_command(args):
     """Handle download subcommand."""
     adaptor = get_storage_adaptor(
-        args.provider,
-        bucket=args.bucket,
-        container=args.container,
-        **parse_extra_args(args.extra)
+        args.provider, bucket=args.bucket, container=args.container, **parse_extra_args(args.extra)
     )
 
     # Check if remote path is a directory (ends with /)
-    if args.remote_path.endswith('/'):
+    if args.remote_path.endswith("/"):
         print(f"📁 Downloading directory: {args.remote_path}")
-        downloaded_files = adaptor.download_directory(
-            args.remote_path,
-            args.local_path
-        )
+        downloaded_files = adaptor.download_directory(args.remote_path, args.local_path)
         print(f"✅ Downloaded {len(downloaded_files)} files")
         if args.verbose:
             for file_path in downloaded_files:
@@ -67,10 +56,7 @@ def download_command(args):
 def list_command(args):
     """Handle list subcommand."""
     adaptor = get_storage_adaptor(
-        args.provider,
-        bucket=args.bucket,
-        container=args.container,
-        **parse_extra_args(args.extra)
+        args.provider, bucket=args.bucket, container=args.container, **parse_extra_args(args.extra)
     )
 
     print(f"📋 Listing files: {args.prefix or '(root)'}")
@@ -99,15 +85,12 @@ def list_command(args):
 def delete_command(args):
     """Handle delete subcommand."""
     adaptor = get_storage_adaptor(
-        args.provider,
-        bucket=args.bucket,
-        container=args.container,
-        **parse_extra_args(args.extra)
+        args.provider, bucket=args.bucket, container=args.container, **parse_extra_args(args.extra)
     )
 
     if not args.force:
         response = input(f"⚠️  Delete {args.remote_path}? [y/N]: ")
-        if response.lower() != 'y':
+        if response.lower() != "y":
             print("❌ Deletion cancelled")
             return
 
@@ -119,10 +102,7 @@ def delete_command(args):
 def url_command(args):
     """Handle url subcommand."""
     adaptor = get_storage_adaptor(
-        args.provider,
-        bucket=args.bucket,
-        container=args.container,
-        **parse_extra_args(args.extra)
+        args.provider, bucket=args.bucket, container=args.container, **parse_extra_args(args.extra)
     )
 
     print(f"🔗 Generating signed URL: {args.remote_path}")
@@ -134,10 +114,7 @@ def url_command(args):
 def copy_command(args):
     """Handle copy subcommand."""
     adaptor = get_storage_adaptor(
-        args.provider,
-        bucket=args.bucket,
-        container=args.container,
-        **parse_extra_args(args.extra)
+        args.provider, bucket=args.bucket, container=args.container, **parse_extra_args(args.extra)
     )
 
     print(f"📋 Copying: {args.source_path} → {args.dest_path}")
@@ -147,7 +124,7 @@ def copy_command(args):
 
 def format_size(size_bytes: int) -> str:
     """Format file size in human-readable format."""
-    for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
+    for unit in ["B", "KB", "MB", "GB", "TB"]:
         if size_bytes < 1024.0:
             return f"{size_bytes:.1f}{unit}"
         size_bytes /= 1024.0
@@ -161,11 +138,11 @@ def parse_extra_args(extra: list | None) -> dict:
 
     result = {}
     for arg in extra:
-        if '=' in arg:
-            key, value = arg.split('=', 1)
-            result[key.lstrip('-')] = value
+        if "=" in arg:
+            key, value = arg.split("=", 1)
+            result[key.lstrip("-")] = value
         else:
-            result[arg.lstrip('-')] = True
+            result[arg.lstrip("-")] = True
 
     return result
 
@@ -173,7 +150,7 @@ def parse_extra_args(extra: list | None) -> dict:
 def main():
     """Main entry point."""
     parser = argparse.ArgumentParser(
-        description='Cloud storage operations for Skill Seekers',
+        description="Cloud storage operations for Skill Seekers",
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
 Examples:
@@ -197,114 +174,66 @@ Provider-specific options:
   S3:    --region=us-west-2 --endpoint-url=https://...
   GCS:   --project=my-project --credentials-path=/path/to/creds.json
   Azure: --account-name=myaccount --account-key=...
-        """
+        """,
     )
 
     # Global arguments
     parser.add_argument(
-        '--provider',
-        choices=['s3', 'gcs', 'azure'],
-        required=True,
-        help='Cloud storage provider'
-    )
-    parser.add_argument(
-        '--bucket',
-        help='S3/GCS bucket name (for S3/GCS)'
-    )
-    parser.add_argument(
-        '--container',
-        help='Azure container name (for Azure)'
-    )
-    parser.add_argument(
-        '--verbose', '-v',
-        action='store_true',
-        help='Verbose output'
+        "--provider", choices=["s3", "gcs", "azure"], required=True, help="Cloud storage provider"
     )
+    parser.add_argument("--bucket", help="S3/GCS bucket name (for S3/GCS)")
+    parser.add_argument("--container", help="Azure container name (for Azure)")
+    parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output")
 
-    subparsers = parser.add_subparsers(dest='command', help='Command to execute')
+    subparsers = parser.add_subparsers(dest="command", help="Command to execute")
 
     # Upload command
-    upload_parser = subparsers.add_parser('upload', help='Upload file or directory')
-    upload_parser.add_argument('local_path', help='Local file or directory path')
-    upload_parser.add_argument('remote_path', help='Remote path in cloud storage')
+    upload_parser = subparsers.add_parser("upload", help="Upload file or directory")
+    upload_parser.add_argument("local_path", help="Local file or directory path")
+    upload_parser.add_argument("remote_path", help="Remote path in cloud storage")
     upload_parser.add_argument(
-        '--exclude',
-        action='append',
-        help='Glob patterns to exclude (for directories)'
-    )
-    upload_parser.add_argument(
-        'extra',
-        nargs='*',
-        help='Provider-specific options (--key=value)'
+        "--exclude", action="append", help="Glob patterns to exclude (for directories)"
     )
+    upload_parser.add_argument("extra", nargs="*", help="Provider-specific options (--key=value)")
 
     # Download command
-    download_parser = subparsers.add_parser('download', help='Download file or directory')
-    download_parser.add_argument('remote_path', help='Remote path in cloud storage')
-    download_parser.add_argument('local_path', help='Local destination path')
-    download_parser.add_argument(
-        'extra',
-        nargs='*',
-        help='Provider-specific options (--key=value)'
-    )
+    download_parser = subparsers.add_parser("download", help="Download file or directory")
+    download_parser.add_argument("remote_path", help="Remote path in cloud storage")
+    download_parser.add_argument("local_path", help="Local destination path")
+    download_parser.add_argument("extra", nargs="*", help="Provider-specific options (--key=value)")
 
     # List command
-    list_parser = subparsers.add_parser('list', help='List files in cloud storage')
+    list_parser = subparsers.add_parser("list", help="List files in cloud storage")
+    list_parser.add_argument("--prefix", default="", help="Prefix to filter files")
     list_parser.add_argument(
-        '--prefix',
-        default='',
-        help='Prefix to filter files'
-    )
-    list_parser.add_argument(
-        '--max-results',
-        type=int,
-        default=1000,
-        help='Maximum number of results'
-    )
-    list_parser.add_argument(
-        'extra',
-        nargs='*',
-        help='Provider-specific options (--key=value)'
+        "--max-results", type=int, default=1000, help="Maximum number of results"
     )
+    list_parser.add_argument("extra", nargs="*", help="Provider-specific options (--key=value)")
 
     # Delete command
-    delete_parser = subparsers.add_parser('delete', help='Delete file from cloud storage')
-    delete_parser.add_argument('remote_path', help='Remote path in cloud storage')
+    delete_parser = subparsers.add_parser("delete", help="Delete file from cloud storage")
+    delete_parser.add_argument("remote_path", help="Remote path in cloud storage")
     delete_parser.add_argument(
-        '--force', '-f',
-        action='store_true',
-        help='Skip confirmation prompt'
-    )
-    delete_parser.add_argument(
-        'extra',
-        nargs='*',
-        help='Provider-specific options (--key=value)'
+        "--force", "-f", action="store_true", help="Skip confirmation prompt"
     )
+    delete_parser.add_argument("extra", nargs="*", help="Provider-specific options (--key=value)")
 
     # URL command
-    url_parser = subparsers.add_parser('url', help='Generate signed URL')
-    url_parser.add_argument('remote_path', help='Remote path in cloud storage')
+    url_parser = subparsers.add_parser("url", help="Generate signed URL")
+    url_parser.add_argument("remote_path", help="Remote path in cloud storage")
     url_parser.add_argument(
-        '--expires-in',
+        "--expires-in",
         type=int,
         default=3600,
-        help='URL expiration time in seconds (default: 3600)'
-    )
-    url_parser.add_argument(
-        'extra',
-        nargs='*',
-        help='Provider-specific options (--key=value)'
+        help="URL expiration time in seconds (default: 3600)",
     )
+    url_parser.add_argument("extra", nargs="*", help="Provider-specific options (--key=value)")
 
     # Copy command
-    copy_parser = subparsers.add_parser('copy', help='Copy file within cloud storage')
-    copy_parser.add_argument('source_path', help='Source path')
-    copy_parser.add_argument('dest_path', help='Destination path')
-    copy_parser.add_argument(
-        'extra',
-        nargs='*',
-        help='Provider-specific options (--key=value)'
-    )
+    copy_parser = subparsers.add_parser("copy", help="Copy file within cloud storage")
+    copy_parser.add_argument("source_path", help="Source path")
+    copy_parser.add_argument("dest_path", help="Destination path")
+    copy_parser.add_argument("extra", nargs="*", help="Provider-specific options (--key=value)")
 
     args = parser.parse_args()
 
@@ -313,26 +242,26 @@ Provider-specific options:
         sys.exit(1)
 
     # Validate bucket/container based on provider
-    if args.provider in ['s3', 'gcs'] and not args.bucket:
+    if args.provider in ["s3", "gcs"] and not args.bucket:
         print(f"❌ Error: --bucket is required for {args.provider.upper()}", file=sys.stderr)
         sys.exit(1)
-    elif args.provider == 'azure' and not args.container:
+    elif args.provider == "azure" and not args.container:
         print("❌ Error: --container is required for Azure", file=sys.stderr)
         sys.exit(1)
 
     try:
         # Execute command
-        if args.command == 'upload':
+        if args.command == "upload":
             upload_command(args)
-        elif args.command == 'download':
+        elif args.command == "download":
             download_command(args)
-        elif args.command == 'list':
+        elif args.command == "list":
             list_command(args)
-        elif args.command == 'delete':
+        elif args.command == "delete":
             delete_command(args)
-        elif args.command == 'url':
+        elif args.command == "url":
             url_command(args)
-        elif args.command == 'copy':
+        elif args.command == "copy":
             copy_command(args)
 
     except FileNotFoundError as e:
@@ -342,9 +271,10 @@ Provider-specific options:
         print(f"❌ Error: {e}", file=sys.stderr)
         if args.verbose:
             import traceback
+
             traceback.print_exc()
         sys.exit(1)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/src/skill_seekers/cli/code_analyzer.py b/src/skill_seekers/cli/code_analyzer.py
index 273161d..95b288e 100644
--- a/src/skill_seekers/cli/code_analyzer.py
+++ b/src/skill_seekers/cli/code_analyzer.py
@@ -376,8 +376,8 @@ class CodeAnalyzer:
             for match in re.finditer(pattern, content):
                 module = match.group(1)
                 # Extract package name (before first /)
-                package = module.split('/')[0]
-                if package and not package.startswith('.'):  # Skip relative imports
+                package = module.split("/")[0]
+                if package and not package.startswith("."):  # Skip relative imports
                     imports.append(package)
 
         return {
@@ -694,11 +694,11 @@ class CodeAnalyzer:
         for match in re.finditer(using_pattern, content):
             namespace = match.group(1).strip()
             # Skip using aliases (using Foo = Bar.Baz)
-            if '=' not in namespace:
+            if "=" not in namespace:
                 # Extract base namespace (first 1-2 segments)
-                parts = namespace.split('.')
+                parts = namespace.split(".")
                 if len(parts) >= 2:
-                    base_ns = '.'.join(parts[:2])
+                    base_ns = ".".join(parts[:2])
                     imports.append(base_ns)
                 elif len(parts) == 1:
                     imports.append(parts[0])
@@ -1130,10 +1130,10 @@ class CodeAnalyzer:
         for match in re.finditer(import_pattern, content):
             import_path = match.group(1).strip()
             # Extract package name (first 2-3 segments for framework detection)
-            parts = import_path.split('.')
+            parts = import_path.split(".")
             if len(parts) >= 2:
                 # Get base package (e.g., "org.springframework" from "org.springframework.boot.SpringApplication")
-                package = '.'.join(parts[:2])
+                package = ".".join(parts[:2])
                 imports.append(package)
 
         return {
@@ -1303,7 +1303,7 @@ class CodeAnalyzer:
         for match in re.finditer(require_pattern, content):
             module = match.group(1)
             # Extract gem name (before first /)
-            gem = module.split('/')[0]
+            gem = module.split("/")[0]
             imports.append(gem)
 
         return {
@@ -1443,7 +1443,7 @@ class CodeAnalyzer:
         for match in re.finditer(use_pattern, content):
             namespace = match.group(1).strip()
             # Extract vendor name (first segment)
-            parts = namespace.split('\\')
+            parts = namespace.split("\\")
             if parts:
                 vendor = parts[0]
                 imports.append(vendor.lower())
diff --git a/src/skill_seekers/cli/codebase_scraper.py b/src/skill_seekers/cli/codebase_scraper.py
index 08a40e2..7696d59 100644
--- a/src/skill_seekers/cli/codebase_scraper.py
+++ b/src/skill_seekers/cli/codebase_scraper.py
@@ -1036,11 +1036,15 @@ def analyze_codebase(
             # Save summary statistics
             summary_json = pattern_output / "summary.json"
             with open(summary_json, "w", encoding="utf-8") as f:
-                json.dump({
-                    "statistics": stats,
-                    "thresholds": multi_level["thresholds"],
-                    "files_analyzed": len(pattern_results),
-                }, f, indent=2)
+                json.dump(
+                    {
+                        "statistics": stats,
+                        "thresholds": multi_level["thresholds"],
+                        "files_analyzed": len(pattern_results),
+                    },
+                    f,
+                    indent=2,
+                )
 
             # Log results with breakdown by confidence
             logger.info(f"✅ Detected {stats['total']} patterns in {len(pattern_results)} files")
@@ -1931,21 +1935,15 @@ def _check_deprecated_flags(args):
                 "⚠️  DEPRECATED: --ai-mode local → use --enhance-level without API key instead"
             )
         elif args.ai_mode == "none":
-            warnings.append(
-                "⚠️  DEPRECATED: --ai-mode none → use --enhance-level 0 instead"
-            )
+            warnings.append("⚠️  DEPRECATED: --ai-mode none → use --enhance-level 0 instead")
 
     # Deprecated: --quick flag
     if hasattr(args, "quick") and args.quick:
-        warnings.append(
-            "⚠️  DEPRECATED: --quick → use --preset quick instead"
-        )
+        warnings.append("⚠️  DEPRECATED: --quick → use --preset quick instead")
 
     # Deprecated: --comprehensive flag
     if hasattr(args, "comprehensive") and args.comprehensive:
-        warnings.append(
-            "⚠️  DEPRECATED: --comprehensive → use --preset comprehensive instead"
-        )
+        warnings.append("⚠️  DEPRECATED: --comprehensive → use --preset comprehensive instead")
 
     # Show warnings if any found
     if warnings:
@@ -2000,24 +1998,22 @@ Examples:
     parser.add_argument(
         "--preset",
         choices=["quick", "standard", "comprehensive"],
-        help="Analysis preset: quick (1-2 min), standard (5-10 min, DEFAULT), comprehensive (20-60 min)"
+        help="Analysis preset: quick (1-2 min), standard (5-10 min, DEFAULT), comprehensive (20-60 min)",
     )
     parser.add_argument(
-        "--preset-list",
-        action="store_true",
-        help="Show available presets and exit"
+        "--preset-list", action="store_true", help="Show available presets and exit"
     )
 
     # Legacy preset flags (kept for backward compatibility)
     parser.add_argument(
         "--quick",
         action="store_true",
-        help="[DEPRECATED] Quick analysis - use '--preset quick' instead"
+        help="[DEPRECATED] Quick analysis - use '--preset quick' instead",
     )
     parser.add_argument(
         "--comprehensive",
         action="store_true",
-        help="[DEPRECATED] Comprehensive analysis - use '--preset comprehensive' instead"
+        help="[DEPRECATED] Comprehensive analysis - use '--preset comprehensive' instead",
     )
 
     parser.add_argument(
@@ -2129,6 +2125,7 @@ Examples:
     # Handle --preset-list flag BEFORE parse_args() to avoid required --directory validation
     if "--preset-list" in sys.argv:
         from skill_seekers.cli.presets import PresetManager
+
         print(PresetManager.format_preset_help())
         return 0
 
@@ -2155,6 +2152,7 @@ Examples:
     # Apply preset using PresetManager
     if preset_name:
         from skill_seekers.cli.presets import PresetManager
+
         try:
             preset_args = PresetManager.apply_preset(preset_name, vars(args))
             # Update args with preset values
@@ -2162,9 +2160,7 @@ Examples:
                 setattr(args, key, value)
 
             preset = PresetManager.get_preset(preset_name)
-            logger.info(
-                f"{preset.icon} {preset.name} analysis mode: {preset.description}"
-            )
+            logger.info(f"{preset.icon} {preset.name} analysis mode: {preset.description}")
         except ValueError as e:
             logger.error(f"❌ {e}")
             return 1
diff --git a/src/skill_seekers/cli/embedding_pipeline.py b/src/skill_seekers/cli/embedding_pipeline.py
index ef7a200..6650a86 100644
--- a/src/skill_seekers/cli/embedding_pipeline.py
+++ b/src/skill_seekers/cli/embedding_pipeline.py
@@ -19,6 +19,7 @@ import numpy as np
 @dataclass
 class EmbeddingConfig:
     """Configuration for embedding generation."""
+
     provider: str  # 'openai', 'cohere', 'huggingface', 'local'
     model: str
     dimension: int
@@ -31,6 +32,7 @@ class EmbeddingConfig:
 @dataclass
 class EmbeddingResult:
     """Result of embedding generation."""
+
     embeddings: list[list[float]]
     metadata: dict[str, Any] = field(default_factory=dict)
     cached_count: int = 0
@@ -42,6 +44,7 @@ class EmbeddingResult:
 @dataclass
 class CostTracker:
     """Track embedding generation costs."""
+
     total_tokens: int = 0
     total_requests: int = 0
     cache_hits: int = 0
@@ -64,12 +67,12 @@ class CostTracker:
         cache_rate = (self.cache_hits / self.total_requests * 100) if self.total_requests > 0 else 0
 
         return {
-            'total_requests': self.total_requests,
-            'total_tokens': self.total_tokens,
-            'cache_hits': self.cache_hits,
-            'cache_misses': self.cache_misses,
-            'cache_rate': f"{cache_rate:.1f}%",
-            'estimated_cost': f"${self.estimated_cost:.4f}"
+            "total_requests": self.total_requests,
+            "total_tokens": self.total_tokens,
+            "cache_hits": self.cache_hits,
+            "cache_misses": self.cache_misses,
+            "cache_rate": f"{cache_rate:.1f}%",
+            "estimated_cost": f"${self.estimated_cost:.4f}",
         }
 
 
@@ -97,18 +100,18 @@ class OpenAIEmbeddingProvider(EmbeddingProvider):
 
     # Pricing per 1M tokens (as of 2026)
     PRICING = {
-        'text-embedding-ada-002': 0.10,
-        'text-embedding-3-small': 0.02,
-        'text-embedding-3-large': 0.13,
+        "text-embedding-ada-002": 0.10,
+        "text-embedding-3-small": 0.02,
+        "text-embedding-3-large": 0.13,
     }
 
     DIMENSIONS = {
-        'text-embedding-ada-002': 1536,
-        'text-embedding-3-small': 1536,
-        'text-embedding-3-large': 3072,
+        "text-embedding-ada-002": 1536,
+        "text-embedding-3-small": 1536,
+        "text-embedding-3-large": 3072,
     }
 
-    def __init__(self, model: str = 'text-embedding-ada-002', api_key: str | None = None):
+    def __init__(self, model: str = "text-embedding-ada-002", api_key: str | None = None):
         """Initialize OpenAI provider."""
         self.model = model
         self.api_key = api_key
@@ -119,9 +122,12 @@ class OpenAIEmbeddingProvider(EmbeddingProvider):
         if self._client is None:
             try:
                 from openai import OpenAI
+
                 self._client = OpenAI(api_key=self.api_key)
             except ImportError:
-                raise ImportError("OpenAI package not installed. Install with: pip install openai") from None
+                raise ImportError(
+                    "OpenAI package not installed. Install with: pip install openai"
+                ) from None
         return self._client
 
     def generate_embeddings(self, texts: list[str]) -> list[list[float]]:
@@ -130,10 +136,7 @@ class OpenAIEmbeddingProvider(EmbeddingProvider):
 
         embeddings = []
         for text in texts:
-            response = client.embeddings.create(
-                model=self.model,
-                input=text
-            )
+            response = client.embeddings.create(model=self.model, input=text)
             embeddings.append(response.data[0].embedding)
 
         return embeddings
@@ -207,7 +210,7 @@ class EmbeddingCache:
             if cache_file.exists():
                 try:
                     data = json.loads(cache_file.read_text())
-                    embedding = data['embedding']
+                    embedding = data["embedding"]
                     self._memory_cache[cache_key] = embedding
                     return embedding
                 except Exception:
@@ -226,12 +229,16 @@ class EmbeddingCache:
         if self.cache_dir:
             cache_file = self.cache_dir / f"{cache_key}.json"
             try:
-                cache_file.write_text(json.dumps({
-                    'text_hash': cache_key,
-                    'model': model,
-                    'embedding': embedding,
-                    'timestamp': time.time()
-                }))
+                cache_file.write_text(
+                    json.dumps(
+                        {
+                            "text_hash": cache_key,
+                            "model": model,
+                            "embedding": embedding,
+                            "timestamp": time.time(),
+                        }
+                    )
+                )
             except Exception as e:
                 print(f"⚠️  Warning: Failed to write cache: {e}")
 
@@ -252,9 +259,9 @@ class EmbeddingPipeline:
 
     def _create_provider(self) -> EmbeddingProvider:
         """Create provider based on config."""
-        if self.config.provider == 'openai':
+        if self.config.provider == "openai":
             return OpenAIEmbeddingProvider(self.config.model)
-        elif self.config.provider == 'local':
+        elif self.config.provider == "local":
             return LocalEmbeddingProvider(self.config.dimension)
         else:
             raise ValueError(f"Unknown provider: {self.config.provider}")
@@ -264,11 +271,7 @@ class EmbeddingPipeline:
         # Rough estimate: 1 token ≈ 4 characters
         return len(text) // 4
 
-    def generate_batch(
-        self,
-        texts: list[str],
-        show_progress: bool = True
-    ) -> EmbeddingResult:
+    def generate_batch(self, texts: list[str], show_progress: bool = True) -> EmbeddingResult:
         """
         Generate embeddings for batch of texts.
 
@@ -293,7 +296,7 @@ class EmbeddingPipeline:
 
         # Process in batches
         for i in range(0, len(texts), self.config.batch_size):
-            batch = texts[i:i + self.config.batch_size]
+            batch = texts[i : i + self.config.batch_size]
             batch_embeddings = []
             to_generate = []
             to_generate_indices = []
@@ -331,7 +334,7 @@ class EmbeddingPipeline:
 
             if show_progress and len(texts) > self.config.batch_size:
                 progress = min(i + self.config.batch_size, len(texts))
-                print(f"   Progress: {progress}/{len(texts)} ({progress/len(texts)*100:.1f}%)")
+                print(f"   Progress: {progress}/{len(texts)} ({progress / len(texts) * 100:.1f}%)")
 
         total_time = time.time() - start_time
 
@@ -342,21 +345,21 @@ class EmbeddingPipeline:
             print(f"   Generated: {generated_count}")
             print(f"   Time: {total_time:.2f}s")
 
-            if self.config.provider != 'local':
+            if self.config.provider != "local":
                 stats = self.cost_tracker.get_stats()
                 print(f"   Cost: {stats['estimated_cost']}")
 
         return EmbeddingResult(
             embeddings=embeddings,
             metadata={
-                'provider': self.config.provider,
-                'model': self.config.model,
-                'dimension': self.provider.get_dimension()
+                "provider": self.config.provider,
+                "model": self.config.model,
+                "dimension": self.provider.get_dimension(),
             },
             cached_count=cached_count,
             generated_count=generated_count,
             total_time=total_time,
-            cost_estimate=self.cost_tracker.estimated_cost
+            cost_estimate=self.cost_tracker.estimated_cost,
         )
 
     def validate_dimensions(self, embeddings: list[list[float]]) -> bool:
@@ -373,8 +376,10 @@ class EmbeddingPipeline:
 
         for i, embedding in enumerate(embeddings):
             if len(embedding) != expected_dim:
-                print(f"❌ Dimension mismatch at index {i}: "
-                      f"expected {expected_dim}, got {len(embedding)}")
+                print(
+                    f"❌ Dimension mismatch at index {i}: "
+                    f"expected {expected_dim}, got {len(embedding)}"
+                )
                 return False
 
         return True
@@ -390,11 +395,11 @@ def example_usage():
 
     # Configure pipeline
     config = EmbeddingConfig(
-        provider='local',  # Use 'openai' for production
-        model='text-embedding-ada-002',
+        provider="local",  # Use 'openai' for production
+        model="text-embedding-ada-002",
         dimension=384,
         batch_size=50,
-        cache_dir=Path("output/.embeddings_cache")
+        cache_dir=Path("output/.embeddings_cache"),
     )
 
     # Initialize pipeline
diff --git a/src/skill_seekers/cli/enhance_skill_local.py b/src/skill_seekers/cli/enhance_skill_local.py
index 6950b9d..bc2c680 100644
--- a/src/skill_seekers/cli/enhance_skill_local.py
+++ b/src/skill_seekers/cli/enhance_skill_local.py
@@ -175,8 +175,7 @@ class LocalSkillEnhancer:
         dangerous_chars = [";", "&", "|", "$", "`", "\n", "\r"]
         if any(char in cmd_template for char in dangerous_chars):
             raise ValueError(
-                "Custom command contains dangerous shell characters. "
-                f"Command: {cmd_template}"
+                f"Custom command contains dangerous shell characters. Command: {cmd_template}"
             )
 
         try:
@@ -888,9 +887,7 @@ rm {prompt_file}
                     print("❌ SKILL.md not found after enhancement")
                     return False
             else:
-                print(
-                    f"❌ {self.agent_display} returned error (exit code: {result.returncode})"
-                )
+                print(f"❌ {self.agent_display} returned error (exit code: {result.returncode})")
                 if result.stderr:
                     print(f"   Error: {result.stderr[:200]}")
                 return False
diff --git a/src/skill_seekers/cli/incremental_updater.py b/src/skill_seekers/cli/incremental_updater.py
index a6b3ff3..a2e2f36 100644
--- a/src/skill_seekers/cli/incremental_updater.py
+++ b/src/skill_seekers/cli/incremental_updater.py
@@ -16,6 +16,7 @@ from datetime import datetime
 @dataclass
 class DocumentVersion:
     """Version information for a document."""
+
     file_path: str
     content_hash: str
     size_bytes: int
@@ -26,6 +27,7 @@ class DocumentVersion:
 @dataclass
 class ChangeSet:
     """Set of changes detected."""
+
     added: list[DocumentVersion]
     modified: list[DocumentVersion]
     deleted: list[str]
@@ -45,6 +47,7 @@ class ChangeSet:
 @dataclass
 class UpdateMetadata:
     """Metadata for an incremental update."""
+
     timestamp: str
     previous_version: str
     new_version: str
@@ -86,7 +89,7 @@ class IncrementalUpdater:
         sha256 = hashlib.sha256()
 
         try:
-            with open(file_path, 'rb') as f:
+            with open(file_path, "rb") as f:
                 while chunk := f.read(8192):
                     sha256.update(chunk)
             return sha256.hexdigest()
@@ -111,7 +114,7 @@ class IncrementalUpdater:
                 content_hash=self._compute_file_hash(skill_md),
                 size_bytes=skill_md.stat().st_size,
                 last_modified=skill_md.stat().st_mtime,
-                version=1
+                version=1,
             )
 
         # Scan references
@@ -125,7 +128,7 @@ class IncrementalUpdater:
                         content_hash=self._compute_file_hash(ref_file),
                         size_bytes=ref_file.stat().st_size,
                         last_modified=ref_file.stat().st_mtime,
-                        version=1
+                        version=1,
                     )
 
         return versions
@@ -157,9 +160,8 @@ class IncrementalUpdater:
             "timestamp": datetime.now().isoformat(),
             "version": "1.0.0",
             "documents": {
-                file_path: asdict(version)
-                for file_path, version in self.current_versions.items()
-            }
+                file_path: asdict(version) for file_path, version in self.current_versions.items()
+            },
         }
 
         self.version_file.write_text(json.dumps(data, indent=2))
@@ -180,10 +182,7 @@ class IncrementalUpdater:
         if not has_previous:
             # First time - all files are "added"
             return ChangeSet(
-                added=list(self.current_versions.values()),
-                modified=[],
-                deleted=[],
-                unchanged=[]
+                added=list(self.current_versions.values()), modified=[], deleted=[], unchanged=[]
             )
 
         # Detect changes
@@ -215,18 +214,10 @@ class IncrementalUpdater:
             else:
                 unchanged.append(current)
 
-        return ChangeSet(
-            added=added,
-            modified=modified,
-            deleted=deleted,
-            unchanged=unchanged
-        )
+        return ChangeSet(added=added, modified=modified, deleted=deleted, unchanged=unchanged)
 
     def generate_update_package(
-        self,
-        change_set: ChangeSet,
-        output_path: Path,
-        include_content: bool = True
+        self, change_set: ChangeSet, output_path: Path, include_content: bool = True
     ) -> Path:
         """
         Generate incremental update package.
@@ -250,11 +241,11 @@ class IncrementalUpdater:
                     "added": len(change_set.added),
                     "modified": len(change_set.modified),
                     "deleted": len(change_set.deleted),
-                    "unchanged": len(change_set.unchanged)
+                    "unchanged": len(change_set.unchanged),
                 },
-                "total_changes": change_set.total_changes
+                "total_changes": change_set.total_changes,
             },
-            "changes": {}
+            "changes": {},
         }
 
         # Include changed documents
@@ -267,7 +258,7 @@ class IncrementalUpdater:
                     "version": doc.version,
                     "content": file_path.read_text(encoding="utf-8"),
                     "hash": doc.content_hash,
-                    "size": doc.size_bytes
+                    "size": doc.size_bytes,
                 }
 
             # Modified documents
@@ -278,14 +269,12 @@ class IncrementalUpdater:
                     "version": doc.version,
                     "content": file_path.read_text(encoding="utf-8"),
                     "hash": doc.content_hash,
-                    "size": doc.size_bytes
+                    "size": doc.size_bytes,
                 }
 
             # Deleted documents
             for file_path in change_set.deleted:
-                update_data["changes"][file_path] = {
-                    "action": "delete"
-                }
+                update_data["changes"][file_path] = {"action": "delete"}
 
         # Write package
         output_path.parent.mkdir(parents=True, exist_ok=True)
@@ -332,7 +321,9 @@ class IncrementalUpdater:
                 if prev:
                     size_diff = doc.size_bytes - prev.size_bytes
                     size_str = f"{size_diff:+,} bytes" if size_diff != 0 else "same size"
-                    lines.append(f"   ~ {doc.file_path} (v{prev.version} → v{doc.version}, {size_str})")
+                    lines.append(
+                        f"   ~ {doc.file_path} (v{prev.version} → v{doc.version}, {size_str})"
+                    )
                 else:
                     lines.append(f"   ~ {doc.file_path} (v{doc.version})")
             lines.append("")
@@ -473,4 +464,5 @@ def main():
 
 if __name__ == "__main__":
     import sys
+
     sys.exit(main())
diff --git a/src/skill_seekers/cli/language_detector.py b/src/skill_seekers/cli/language_detector.py
index 4582ca7..e6fa971 100644
--- a/src/skill_seekers/cli/language_detector.py
+++ b/src/skill_seekers/cli/language_detector.py
@@ -369,8 +369,6 @@ LANGUAGE_PATTERNS: dict[str, list[tuple[str, int]]] = {
         (r"\$[0-9]+", 4),
         (r"->", 3),
     ],
-
-
     # ===== Markup/Config Languages =====
     "html": [
         (r"<!DOCTYPE\s+html>", 5),
diff --git a/src/skill_seekers/cli/main.py b/src/skill_seekers/cli/main.py
index 1bcdecb..4b26948 100644
--- a/src/skill_seekers/cli/main.py
+++ b/src/skill_seekers/cli/main.py
@@ -42,25 +42,25 @@ from skill_seekers.cli import __version__
 
 # Command module mapping (command name -> module path)
 COMMAND_MODULES = {
-    'config': 'skill_seekers.cli.config_command',
-    'scrape': 'skill_seekers.cli.doc_scraper',
-    'github': 'skill_seekers.cli.github_scraper',
-    'pdf': 'skill_seekers.cli.pdf_scraper',
-    'unified': 'skill_seekers.cli.unified_scraper',
-    'enhance': 'skill_seekers.cli.enhance_skill_local',
-    'enhance-status': 'skill_seekers.cli.enhance_status',
-    'package': 'skill_seekers.cli.package_skill',
-    'upload': 'skill_seekers.cli.upload_skill',
-    'estimate': 'skill_seekers.cli.estimate_pages',
-    'extract-test-examples': 'skill_seekers.cli.test_example_extractor',
-    'install-agent': 'skill_seekers.cli.install_agent',
-    'analyze': 'skill_seekers.cli.codebase_scraper',
-    'install': 'skill_seekers.cli.install_skill',
-    'resume': 'skill_seekers.cli.resume_command',
-    'stream': 'skill_seekers.cli.streaming_ingest',
-    'update': 'skill_seekers.cli.incremental_updater',
-    'multilang': 'skill_seekers.cli.multilang_support',
-    'quality': 'skill_seekers.cli.quality_metrics',
+    "config": "skill_seekers.cli.config_command",
+    "scrape": "skill_seekers.cli.doc_scraper",
+    "github": "skill_seekers.cli.github_scraper",
+    "pdf": "skill_seekers.cli.pdf_scraper",
+    "unified": "skill_seekers.cli.unified_scraper",
+    "enhance": "skill_seekers.cli.enhance_skill_local",
+    "enhance-status": "skill_seekers.cli.enhance_status",
+    "package": "skill_seekers.cli.package_skill",
+    "upload": "skill_seekers.cli.upload_skill",
+    "estimate": "skill_seekers.cli.estimate_pages",
+    "extract-test-examples": "skill_seekers.cli.test_example_extractor",
+    "install-agent": "skill_seekers.cli.install_agent",
+    "analyze": "skill_seekers.cli.codebase_scraper",
+    "install": "skill_seekers.cli.install_skill",
+    "resume": "skill_seekers.cli.resume_command",
+    "stream": "skill_seekers.cli.streaming_ingest",
+    "update": "skill_seekers.cli.incremental_updater",
+    "multilang": "skill_seekers.cli.multilang_support",
+    "quality": "skill_seekers.cli.quality_metrics",
 }
 
 
@@ -124,12 +124,21 @@ def _reconstruct_argv(command: str, args: argparse.Namespace) -> list[str]:
 
     # Convert args to sys.argv format
     for key, value in vars(args).items():
-        if key == 'command':
+        if key == "command":
             continue
 
         # Handle positional arguments (no -- prefix)
-        if key in ['url', 'directory', 'file', 'job_id', 'skill_directory', 'zip_file', 'config', 'input_file']:
-            if value is not None and value != '':
+        if key in [
+            "url",
+            "directory",
+            "file",
+            "job_id",
+            "skill_directory",
+            "zip_file",
+            "config",
+            "input_file",
+        ]:
+            if value is not None and value != "":
                 argv.append(str(value))
             continue
 
@@ -172,7 +181,7 @@ def main(argv: list[str] | None = None) -> int:
         return 1
 
     # Special handling for 'analyze' command (has post-processing)
-    if args.command == 'analyze':
+    if args.command == "analyze":
         return _handle_analyze_command(args)
 
     # Standard delegation for all other commands
@@ -200,6 +209,7 @@ def main(argv: list[str] | None = None) -> int:
 
         # Show traceback in verbose mode
         import traceback
+
         if hasattr(args, "verbose") and getattr(args, "verbose", False):
             traceback.print_exc()
 
@@ -226,13 +236,16 @@ def _handle_analyze_command(args: argparse.Namespace) -> int:
 
     # Handle preset flags (depth and features)
     if args.quick:
-        sys.argv.extend([
-            "--depth", "surface",
-            "--skip-patterns",
-            "--skip-test-examples",
-            "--skip-how-to-guides",
-            "--skip-config-patterns",
-        ])
+        sys.argv.extend(
+            [
+                "--depth",
+                "surface",
+                "--skip-patterns",
+                "--skip-test-examples",
+                "--skip-how-to-guides",
+                "--skip-config-patterns",
+            ]
+        )
     elif args.comprehensive:
         sys.argv.extend(["--depth", "full"])
     elif args.depth:
@@ -246,6 +259,7 @@ def _handle_analyze_command(args: argparse.Namespace) -> int:
     elif args.enhance:
         try:
             from skill_seekers.cli.config_manager import get_config_manager
+
             config = get_config_manager()
             enhance_level = config.get_default_enhance_level()
         except Exception:
diff --git a/src/skill_seekers/cli/multilang_support.py b/src/skill_seekers/cli/multilang_support.py
index 8074ef4..e456742 100644
--- a/src/skill_seekers/cli/multilang_support.py
+++ b/src/skill_seekers/cli/multilang_support.py
@@ -15,6 +15,7 @@ import json
 @dataclass
 class LanguageInfo:
     """Language information for a document."""
+
     code: str  # ISO 639-1 code (e.g., 'en', 'es', 'zh')
     name: str  # Full name (e.g., 'English', 'Spanish', 'Chinese')
     confidence: float  # Detection confidence (0.0-1.0)
@@ -24,6 +25,7 @@ class LanguageInfo:
 @dataclass
 class TranslationStatus:
     """Translation status for a document."""
+
     source_language: str
     target_languages: list[str]
     translated_languages: set[str]
@@ -40,74 +42,81 @@ class LanguageDetector:
 
     # Common word patterns by language
     LANGUAGE_PATTERNS = {
-        'en': [
-            r'\b(the|and|is|are|in|to|of|for|with|on|at|by|from)\b',
-            r'\b(this|that|these|those|what|which|who|where|when)\b',
+        "en": [
+            r"\b(the|and|is|are|in|to|of|for|with|on|at|by|from)\b",
+            r"\b(this|that|these|those|what|which|who|where|when)\b",
         ],
-        'es': [
-            r'\b(el|la|los|las|de|en|y|a|es|por|para|con|su)\b',
-            r'\b(que|no|un|una|como|más|pero|muy|todo|ya)\b',
+        "es": [
+            r"\b(el|la|los|las|de|en|y|a|es|por|para|con|su)\b",
+            r"\b(que|no|un|una|como|más|pero|muy|todo|ya)\b",
         ],
-        'fr': [
-            r'\b(le|la|les|de|et|en|un|une|pour|dans|que|sur|avec)\b',
-            r'\b(est|sont|ce|qui|plus|ne|pas|nous|vous|tout)\b',
+        "fr": [
+            r"\b(le|la|les|de|et|en|un|une|pour|dans|que|sur|avec)\b",
+            r"\b(est|sont|ce|qui|plus|ne|pas|nous|vous|tout)\b",
         ],
-        'de': [
-            r'\b(der|die|das|und|in|zu|den|von|ist|mit|für|auf)\b',
-            r'\b(ein|eine|nicht|sich|auch|werden|an|als|ich|sie)\b',
+        "de": [
+            r"\b(der|die|das|und|in|zu|den|von|ist|mit|für|auf)\b",
+            r"\b(ein|eine|nicht|sich|auch|werden|an|als|ich|sie)\b",
         ],
-        'zh': [
-            r'[\u4e00-\u9fff]',  # Chinese characters
-            r'(的|了|和|是|在|有|我|他|不|这)',
+        "zh": [
+            r"[\u4e00-\u9fff]",  # Chinese characters
+            r"(的|了|和|是|在|有|我|他|不|这)",
         ],
-        'ja': [
-            r'[\u3040-\u309f]',  # Hiragana
-            r'[\u30a0-\u30ff]',  # Katakana
-            r'[\u4e00-\u9faf]',  # Kanji
+        "ja": [
+            r"[\u3040-\u309f]",  # Hiragana
+            r"[\u30a0-\u30ff]",  # Katakana
+            r"[\u4e00-\u9faf]",  # Kanji
         ],
-        'ko': [
-            r'[\uac00-\ud7af]',  # Hangul
-            r'(의|가|이|은|들|는|좀|잘|께|을)',
+        "ko": [
+            r"[\uac00-\ud7af]",  # Hangul
+            r"(의|가|이|은|들|는|좀|잘|께|을)",
         ],
-        'ru': [
-            r'[\u0400-\u04ff]',  # Cyrillic
-            r'\b(и|в|не|на|с|что|он|по|а|как|это|все)\b',
+        "ru": [
+            r"[\u0400-\u04ff]",  # Cyrillic
+            r"\b(и|в|не|на|с|что|он|по|а|как|это|все)\b",
         ],
-        'pt': [
-            r'\b(o|a|de|e|do|da|em|um|para|é|com|não|os|as)\b',
-            r'\b(que|se|mais|por|dos|das|como|mas|uma|ou)\b',
+        "pt": [
+            r"\b(o|a|de|e|do|da|em|um|para|é|com|não|os|as)\b",
+            r"\b(que|se|mais|por|dos|das|como|mas|uma|ou)\b",
         ],
-        'it': [
-            r'\b(il|la|di|e|a|da|in|che|per|un|una|non|del)\b',
-            r'\b(con|alla|della|al|nel|sono|come|più|ma|dei)\b',
+        "it": [
+            r"\b(il|la|di|e|a|da|in|che|per|un|una|non|del)\b",
+            r"\b(con|alla|della|al|nel|sono|come|più|ma|dei)\b",
         ],
-        'ar': [
-            r'[\u0600-\u06ff]',  # Arabic
-            r'(في|من|على|إلى|هذا|ما|أن|كان|هو|التي)',
+        "ar": [
+            r"[\u0600-\u06ff]",  # Arabic
+            r"(في|من|على|إلى|هذا|ما|أن|كان|هو|التي)",
         ],
     }
 
     # Language names
     LANGUAGE_NAMES = {
-        'en': 'English',
-        'es': 'Spanish',
-        'fr': 'French',
-        'de': 'German',
-        'zh': 'Chinese',
-        'ja': 'Japanese',
-        'ko': 'Korean',
-        'ru': 'Russian',
-        'pt': 'Portuguese',
-        'it': 'Italian',
-        'ar': 'Arabic',
+        "en": "English",
+        "es": "Spanish",
+        "fr": "French",
+        "de": "German",
+        "zh": "Chinese",
+        "ja": "Japanese",
+        "ko": "Korean",
+        "ru": "Russian",
+        "pt": "Portuguese",
+        "it": "Italian",
+        "ar": "Arabic",
     }
 
     # Script types
     SCRIPTS = {
-        'en': 'Latin', 'es': 'Latin', 'fr': 'Latin', 'de': 'Latin',
-        'pt': 'Latin', 'it': 'Latin',
-        'zh': 'Han', 'ja': 'Japanese', 'ko': 'Hangul',
-        'ru': 'Cyrillic', 'ar': 'Arabic',
+        "en": "Latin",
+        "es": "Latin",
+        "fr": "Latin",
+        "de": "Latin",
+        "pt": "Latin",
+        "it": "Latin",
+        "zh": "Han",
+        "ja": "Japanese",
+        "ko": "Hangul",
+        "ru": "Cyrillic",
+        "ar": "Arabic",
     }
 
     def detect(self, text: str, sample_size: int = 2000) -> LanguageInfo:
@@ -122,7 +131,7 @@ class LanguageDetector:
             LanguageInfo with detected language
         """
         if not text.strip():
-            return LanguageInfo('en', 'English', 0.0)
+            return LanguageInfo("en", "English", 0.0)
 
         # Sample text for efficiency
         sample = text[:sample_size].lower()
@@ -140,7 +149,7 @@ class LanguageDetector:
         # Find best match
         if not scores or max(scores.values()) == 0:
             # Default to English
-            return LanguageInfo('en', 'English', 0.1)
+            return LanguageInfo("en", "English", 0.1)
 
         best_lang = max(scores, key=scores.get)
         total_score = sum(scores.values())
@@ -150,7 +159,7 @@ class LanguageDetector:
             code=best_lang,
             name=self.LANGUAGE_NAMES.get(best_lang, best_lang.upper()),
             confidence=min(confidence, 1.0),
-            script=self.SCRIPTS.get(best_lang)
+            script=self.SCRIPTS.get(best_lang),
         )
 
     def detect_from_filename(self, filename: str) -> str | None:
@@ -170,12 +179,12 @@ class LanguageDetector:
             ISO 639-1 language code or None
         """
         # Pattern: file.en.md
-        match = re.search(r'\.([a-z]{2})\.md$', filename)
+        match = re.search(r"\.([a-z]{2})\.md$", filename)
         if match and match.group(1) in self.LANGUAGE_NAMES:
             return match.group(1)
 
         # Pattern: file_en.md or file-en.md
-        match = re.search(r'[_-]([a-z]{2})\.md$', filename)
+        match = re.search(r"[_-]([a-z]{2})\.md$", filename)
         if match and match.group(1) in self.LANGUAGE_NAMES:
             return match.group(1)
 
@@ -200,7 +209,7 @@ class MultiLanguageManager:
         file_path: str,
         content: str,
         metadata: dict | None = None,
-        force_language: str | None = None
+        force_language: str | None = None,
     ) -> None:
         """
         Add document with language detection.
@@ -218,7 +227,7 @@ class MultiLanguageManager:
                 code=lang_code,
                 name=self.detector.LANGUAGE_NAMES.get(lang_code, lang_code.upper()),
                 confidence=1.0,
-                script=self.detector.SCRIPTS.get(lang_code)
+                script=self.detector.SCRIPTS.get(lang_code),
             )
         else:
             # Try filename pattern first
@@ -229,7 +238,7 @@ class MultiLanguageManager:
                     code=lang_code,
                     name=self.detector.LANGUAGE_NAMES.get(lang_code, lang_code.upper()),
                     confidence=0.95,
-                    script=self.detector.SCRIPTS.get(lang_code)
+                    script=self.detector.SCRIPTS.get(lang_code),
                 )
             else:
                 # Detect from content
@@ -245,13 +254,13 @@ class MultiLanguageManager:
             self.documents[lang_code] = []
 
         doc = {
-            'file_path': file_path,
-            'content': content,
-            'language': lang_info.code,
-            'language_name': lang_info.name,
-            'confidence': lang_info.confidence,
-            'script': lang_info.script,
-            'metadata': metadata or {}
+            "file_path": file_path,
+            "content": content,
+            "language": lang_info.code,
+            "language_name": lang_info.name,
+            "confidence": lang_info.confidence,
+            "script": lang_info.script,
+            "metadata": metadata or {},
         }
 
         self.documents[lang_code].append(doc)
@@ -284,7 +293,7 @@ class MultiLanguageManager:
         Returns:
             Translation status summary
         """
-        base_lang = base_language or self.primary_language or 'en'
+        base_lang = base_language or self.primary_language or "en"
 
         all_languages = set(self.documents.keys())
         base_count = self.get_document_count(base_lang)
@@ -295,7 +304,7 @@ class MultiLanguageManager:
                 target_languages=[],
                 translated_languages=set(),
                 missing_languages=set(),
-                completeness=0.0
+                completeness=0.0,
             )
 
         # Check which languages have translations
@@ -305,7 +314,7 @@ class MultiLanguageManager:
                 translated.add(lang)
 
         # Commonly expected languages for completeness
-        expected_languages = {'en', 'es', 'fr', 'de', 'zh', 'ja'}
+        expected_languages = {"en", "es", "fr", "de", "zh", "ja"}
         missing = expected_languages - all_languages
 
         completeness = len(all_languages) / len(expected_languages)
@@ -315,7 +324,7 @@ class MultiLanguageManager:
             target_languages=list(all_languages - {base_lang}),
             translated_languages=translated,
             missing_languages=missing,
-            completeness=min(completeness, 1.0)
+            completeness=min(completeness, 1.0),
         )
 
     def export_by_language(self, output_dir: Path) -> dict[str, Path]:
@@ -337,10 +346,10 @@ class MultiLanguageManager:
             lang_file = output_dir / f"documents_{lang_code}.json"
 
             export_data = {
-                'language': lang_code,
-                'language_name': self.detector.LANGUAGE_NAMES.get(lang_code, lang_code.upper()),
-                'document_count': len(docs),
-                'documents': docs
+                "language": lang_code,
+                "language_name": self.detector.LANGUAGE_NAMES.get(lang_code, lang_code.upper()),
+                "document_count": len(docs),
+                "documents": docs,
             }
 
             lang_file.write_text(json.dumps(export_data, indent=2, ensure_ascii=False))
@@ -419,9 +428,7 @@ def main():
     skill_md = skill_dir / "SKILL.md"
     if skill_md.exists():
         manager.add_document(
-            "SKILL.md",
-            skill_md.read_text(encoding="utf-8"),
-            {"category": "overview"}
+            "SKILL.md", skill_md.read_text(encoding="utf-8"), {"category": "overview"}
         )
 
     # Load reference files
@@ -429,9 +436,7 @@ def main():
     if refs_dir.exists():
         for ref_file in refs_dir.glob("*.md"):
             manager.add_document(
-                ref_file.name,
-                ref_file.read_text(encoding="utf-8"),
-                {"category": ref_file.stem}
+                ref_file.name, ref_file.read_text(encoding="utf-8"), {"category": ref_file.stem}
             )
 
     # Detect languages
@@ -460,4 +465,5 @@ def main():
 
 if __name__ == "__main__":
     import sys
+
     sys.exit(main())
diff --git a/src/skill_seekers/cli/package_skill.py b/src/skill_seekers/cli/package_skill.py
index 9be22b1..6a31744 100644
--- a/src/skill_seekers/cli/package_skill.py
+++ b/src/skill_seekers/cli/package_skill.py
@@ -113,7 +113,15 @@ def package_skill(
     output_dir = skill_path.parent
 
     # Auto-enable chunking for RAG platforms
-    RAG_PLATFORMS = ['langchain', 'llama-index', 'haystack', 'weaviate', 'chroma', 'faiss', 'qdrant']
+    RAG_PLATFORMS = [
+        "langchain",
+        "llama-index",
+        "haystack",
+        "weaviate",
+        "chroma",
+        "faiss",
+        "qdrant",
+    ]
 
     if target in RAG_PLATFORMS and not enable_chunking:
         print(f"ℹ️  Auto-enabling chunking for {target} platform")
@@ -126,17 +134,19 @@ def package_skill(
     if streaming:
         print(f"   Mode: Streaming (chunk_size={chunk_size}, overlap={chunk_overlap})")
     elif enable_chunking:
-        print(f"   Chunking: Enabled (max_tokens={chunk_max_tokens}, preserve_code={preserve_code_blocks})")
+        print(
+            f"   Chunking: Enabled (max_tokens={chunk_max_tokens}, preserve_code={preserve_code_blocks})"
+        )
 
     try:
         # Use streaming if requested and supported
-        if streaming and hasattr(adaptor, 'package_streaming'):
+        if streaming and hasattr(adaptor, "package_streaming"):
             package_path = adaptor.package_streaming(
                 skill_path,
                 output_dir,
                 chunk_size=chunk_size,
                 chunk_overlap=chunk_overlap,
-                batch_size=batch_size
+                batch_size=batch_size,
             )
         elif streaming:
             print("⚠️  Streaming not supported for this platform, using standard packaging")
@@ -145,7 +155,7 @@ def package_skill(
                 output_dir,
                 enable_chunking=enable_chunking,
                 chunk_max_tokens=chunk_max_tokens,
-                preserve_code_blocks=preserve_code_blocks
+                preserve_code_blocks=preserve_code_blocks,
             )
         else:
             package_path = adaptor.package(
@@ -153,7 +163,7 @@ def package_skill(
                 output_dir,
                 enable_chunking=enable_chunking,
                 chunk_max_tokens=chunk_max_tokens,
-                preserve_code_blocks=preserve_code_blocks
+                preserve_code_blocks=preserve_code_blocks,
             )
 
         print(f"   Output: {package_path}")
@@ -212,7 +222,19 @@ Examples:
 
     parser.add_argument(
         "--target",
-        choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "haystack", "weaviate", "chroma", "faiss", "qdrant"],
+        choices=[
+            "claude",
+            "gemini",
+            "openai",
+            "markdown",
+            "langchain",
+            "llama-index",
+            "haystack",
+            "weaviate",
+            "chroma",
+            "faiss",
+            "qdrant",
+        ],
         default="claude",
         help="Target LLM platform (default: claude)",
     )
diff --git a/src/skill_seekers/cli/parsers/__init__.py b/src/skill_seekers/cli/parsers/__init__.py
index f6c59a8..0db900a 100644
--- a/src/skill_seekers/cli/parsers/__init__.py
+++ b/src/skill_seekers/cli/parsers/__init__.py
@@ -3,6 +3,7 @@
 This module registers all subcommand parsers and provides a factory
 function to create them.
 """
+
 from .base import SubcommandParser
 
 # Import all parser classes
diff --git a/src/skill_seekers/cli/parsers/analyze_parser.py b/src/skill_seekers/cli/parsers/analyze_parser.py
index e6c792e..34e1d1c 100644
--- a/src/skill_seekers/cli/parsers/analyze_parser.py
+++ b/src/skill_seekers/cli/parsers/analyze_parser.py
@@ -1,4 +1,5 @@
 """Analyze subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -21,26 +22,26 @@ class AnalyzeParser(SubcommandParser):
         """Add analyze-specific arguments."""
         parser.add_argument("--directory", required=True, help="Directory to analyze")
         parser.add_argument(
-            "--output", default="output/codebase/", help="Output directory (default: output/codebase/)"
+            "--output",
+            default="output/codebase/",
+            help="Output directory (default: output/codebase/)",
         )
 
         # Preset selection (NEW - recommended way)
         parser.add_argument(
             "--preset",
             choices=["quick", "standard", "comprehensive"],
-            help="Analysis preset: quick (1-2 min), standard (5-10 min, DEFAULT), comprehensive (20-60 min)"
+            help="Analysis preset: quick (1-2 min), standard (5-10 min, DEFAULT), comprehensive (20-60 min)",
         )
         parser.add_argument(
-            "--preset-list",
-            action="store_true",
-            help="Show available presets and exit"
+            "--preset-list", action="store_true", help="Show available presets and exit"
         )
 
         # Legacy preset flags (kept for backward compatibility)
         parser.add_argument(
             "--quick",
             action="store_true",
-            help="[DEPRECATED] Quick analysis - use '--preset quick' instead"
+            help="[DEPRECATED] Quick analysis - use '--preset quick' instead",
         )
         parser.add_argument(
             "--comprehensive",
@@ -71,15 +72,9 @@ class AnalyzeParser(SubcommandParser):
             help="AI enhancement level: 0=off, 1=SKILL.md only (default), 2=+Architecture+Config, 3=full",
         )
         parser.add_argument("--skip-api-reference", action="store_true", help="Skip API docs")
-        parser.add_argument(
-            "--skip-dependency-graph", action="store_true", help="Skip dep graph"
-        )
-        parser.add_argument(
-            "--skip-patterns", action="store_true", help="Skip pattern detection"
-        )
-        parser.add_argument(
-            "--skip-test-examples", action="store_true", help="Skip test examples"
-        )
+        parser.add_argument("--skip-dependency-graph", action="store_true", help="Skip dep graph")
+        parser.add_argument("--skip-patterns", action="store_true", help="Skip pattern detection")
+        parser.add_argument("--skip-test-examples", action="store_true", help="Skip test examples")
         parser.add_argument("--skip-how-to-guides", action="store_true", help="Skip guides")
         parser.add_argument("--skip-config-patterns", action="store_true", help="Skip config")
         parser.add_argument(
diff --git a/src/skill_seekers/cli/parsers/base.py b/src/skill_seekers/cli/parsers/base.py
index 80b535a..765cf01 100644
--- a/src/skill_seekers/cli/parsers/base.py
+++ b/src/skill_seekers/cli/parsers/base.py
@@ -1,4 +1,5 @@
 """Base parser class for subcommands."""
+
 from abc import ABC, abstractmethod
 import argparse
 
@@ -48,10 +49,6 @@ class SubcommandParser(ABC):
         Returns:
             Configured ArgumentParser for this subcommand
         """
-        parser = subparsers.add_parser(
-            self.name,
-            help=self.help,
-            description=self.description
-        )
+        parser = subparsers.add_parser(self.name, help=self.help, description=self.description)
         self.add_arguments(parser)
         return parser
diff --git a/src/skill_seekers/cli/parsers/config_parser.py b/src/skill_seekers/cli/parsers/config_parser.py
index 7d288ba..f78c36e 100644
--- a/src/skill_seekers/cli/parsers/config_parser.py
+++ b/src/skill_seekers/cli/parsers/config_parser.py
@@ -1,4 +1,5 @@
 """Config subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -22,9 +23,7 @@ class ConfigParser(SubcommandParser):
         parser.add_argument(
             "--github", action="store_true", help="Go directly to GitHub token setup"
         )
-        parser.add_argument(
-            "--api-keys", action="store_true", help="Go directly to API keys setup"
-        )
+        parser.add_argument("--api-keys", action="store_true", help="Go directly to API keys setup")
         parser.add_argument(
             "--show", action="store_true", help="Show current configuration and exit"
         )
diff --git a/src/skill_seekers/cli/parsers/enhance_parser.py b/src/skill_seekers/cli/parsers/enhance_parser.py
index 5bc6d4f..a8c0da6 100644
--- a/src/skill_seekers/cli/parsers/enhance_parser.py
+++ b/src/skill_seekers/cli/parsers/enhance_parser.py
@@ -1,4 +1,5 @@
 """Enhance subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/enhance_status_parser.py b/src/skill_seekers/cli/parsers/enhance_status_parser.py
index 299a4ac..229098c 100644
--- a/src/skill_seekers/cli/parsers/enhance_status_parser.py
+++ b/src/skill_seekers/cli/parsers/enhance_status_parser.py
@@ -1,4 +1,5 @@
 """Enhance-status subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -20,10 +21,6 @@ class EnhanceStatusParser(SubcommandParser):
     def add_arguments(self, parser):
         """Add enhance-status-specific arguments."""
         parser.add_argument("skill_directory", help="Skill directory path")
-        parser.add_argument(
-            "--watch", "-w", action="store_true", help="Watch in real-time"
-        )
+        parser.add_argument("--watch", "-w", action="store_true", help="Watch in real-time")
         parser.add_argument("--json", action="store_true", help="JSON output")
-        parser.add_argument(
-            "--interval", type=int, default=2, help="Watch interval in seconds"
-        )
+        parser.add_argument("--interval", type=int, default=2, help="Watch interval in seconds")
diff --git a/src/skill_seekers/cli/parsers/estimate_parser.py b/src/skill_seekers/cli/parsers/estimate_parser.py
index f4c2a14..5a21bdc 100644
--- a/src/skill_seekers/cli/parsers/estimate_parser.py
+++ b/src/skill_seekers/cli/parsers/estimate_parser.py
@@ -1,4 +1,5 @@
 """Estimate subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/github_parser.py b/src/skill_seekers/cli/parsers/github_parser.py
index 1d9801c..ef93342 100644
--- a/src/skill_seekers/cli/parsers/github_parser.py
+++ b/src/skill_seekers/cli/parsers/github_parser.py
@@ -1,4 +1,5 @@
 """GitHub subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -24,9 +25,7 @@ class GitHubParser(SubcommandParser):
         parser.add_argument("--name", help="Skill name")
         parser.add_argument("--description", help="Skill description")
         parser.add_argument("--enhance", action="store_true", help="AI enhancement (API)")
-        parser.add_argument(
-            "--enhance-local", action="store_true", help="AI enhancement (local)"
-        )
+        parser.add_argument("--enhance-local", action="store_true", help="AI enhancement (local)")
         parser.add_argument("--api-key", type=str, help="Anthropic API key for --enhance")
         parser.add_argument(
             "--non-interactive",
diff --git a/src/skill_seekers/cli/parsers/install_agent_parser.py b/src/skill_seekers/cli/parsers/install_agent_parser.py
index c61714f..884d56e 100644
--- a/src/skill_seekers/cli/parsers/install_agent_parser.py
+++ b/src/skill_seekers/cli/parsers/install_agent_parser.py
@@ -1,4 +1,5 @@
 """Install-agent subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -19,9 +20,7 @@ class InstallAgentParser(SubcommandParser):
 
     def add_arguments(self, parser):
         """Add install-agent-specific arguments."""
-        parser.add_argument(
-            "skill_directory", help="Skill directory path (e.g., output/react/)"
-        )
+        parser.add_argument("skill_directory", help="Skill directory path (e.g., output/react/)")
         parser.add_argument(
             "--agent",
             required=True,
diff --git a/src/skill_seekers/cli/parsers/install_parser.py b/src/skill_seekers/cli/parsers/install_parser.py
index f0f58ce..3d48e6d 100644
--- a/src/skill_seekers/cli/parsers/install_parser.py
+++ b/src/skill_seekers/cli/parsers/install_parser.py
@@ -1,4 +1,5 @@
 """Install subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/multilang_parser.py b/src/skill_seekers/cli/parsers/multilang_parser.py
index e92958b..68bdb55 100644
--- a/src/skill_seekers/cli/parsers/multilang_parser.py
+++ b/src/skill_seekers/cli/parsers/multilang_parser.py
@@ -1,4 +1,5 @@
 """Multilang subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/package_parser.py b/src/skill_seekers/cli/parsers/package_parser.py
index 4296437..9c82541 100644
--- a/src/skill_seekers/cli/parsers/package_parser.py
+++ b/src/skill_seekers/cli/parsers/package_parser.py
@@ -1,4 +1,5 @@
 """Package subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -20,27 +21,72 @@ class PackageParser(SubcommandParser):
     def add_arguments(self, parser):
         """Add package-specific arguments."""
         parser.add_argument("skill_directory", help="Skill directory path (e.g., output/react/)")
-        parser.add_argument("--no-open", action="store_true", help="Don't open output folder after packaging")
-        parser.add_argument("--skip-quality-check", action="store_true", help="Skip quality checks before packaging")
+        parser.add_argument(
+            "--no-open", action="store_true", help="Don't open output folder after packaging"
+        )
+        parser.add_argument(
+            "--skip-quality-check", action="store_true", help="Skip quality checks before packaging"
+        )
         parser.add_argument(
             "--target",
             choices=[
-                "claude", "gemini", "openai", "markdown",
-                "langchain", "llama-index", "haystack",
-                "weaviate", "chroma", "faiss", "qdrant"
+                "claude",
+                "gemini",
+                "openai",
+                "markdown",
+                "langchain",
+                "llama-index",
+                "haystack",
+                "weaviate",
+                "chroma",
+                "faiss",
+                "qdrant",
             ],
             default="claude",
             help="Target LLM platform (default: claude)",
         )
-        parser.add_argument("--upload", action="store_true", help="Automatically upload after packaging (requires platform API key)")
+        parser.add_argument(
+            "--upload",
+            action="store_true",
+            help="Automatically upload after packaging (requires platform API key)",
+        )
 
         # Streaming options
-        parser.add_argument("--streaming", action="store_true", help="Use streaming ingestion for large docs (memory-efficient)")
-        parser.add_argument("--chunk-size", type=int, default=4000, help="Maximum characters per chunk (streaming mode, default: 4000)")
-        parser.add_argument("--chunk-overlap", type=int, default=200, help="Overlap between chunks (streaming mode, default: 200)")
-        parser.add_argument("--batch-size", type=int, default=100, help="Number of chunks per batch (streaming mode, default: 100)")
+        parser.add_argument(
+            "--streaming",
+            action="store_true",
+            help="Use streaming ingestion for large docs (memory-efficient)",
+        )
+        parser.add_argument(
+            "--chunk-size",
+            type=int,
+            default=4000,
+            help="Maximum characters per chunk (streaming mode, default: 4000)",
+        )
+        parser.add_argument(
+            "--chunk-overlap",
+            type=int,
+            default=200,
+            help="Overlap between chunks (streaming mode, default: 200)",
+        )
+        parser.add_argument(
+            "--batch-size",
+            type=int,
+            default=100,
+            help="Number of chunks per batch (streaming mode, default: 100)",
+        )
 
         # RAG chunking options
-        parser.add_argument("--chunk", action="store_true", help="Enable intelligent chunking for RAG platforms (auto-enabled for RAG adaptors)")
-        parser.add_argument("--chunk-tokens", type=int, default=512, help="Maximum tokens per chunk (default: 512)")
-        parser.add_argument("--no-preserve-code", action="store_true", help="Allow code block splitting (default: code blocks preserved)")
+        parser.add_argument(
+            "--chunk",
+            action="store_true",
+            help="Enable intelligent chunking for RAG platforms (auto-enabled for RAG adaptors)",
+        )
+        parser.add_argument(
+            "--chunk-tokens", type=int, default=512, help="Maximum tokens per chunk (default: 512)"
+        )
+        parser.add_argument(
+            "--no-preserve-code",
+            action="store_true",
+            help="Allow code block splitting (default: code blocks preserved)",
+        )
diff --git a/src/skill_seekers/cli/parsers/pdf_parser.py b/src/skill_seekers/cli/parsers/pdf_parser.py
index e54242b..6ce91ee 100644
--- a/src/skill_seekers/cli/parsers/pdf_parser.py
+++ b/src/skill_seekers/cli/parsers/pdf_parser.py
@@ -1,4 +1,5 @@
 """PDF subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/quality_parser.py b/src/skill_seekers/cli/parsers/quality_parser.py
index f9750b7..69803fe 100644
--- a/src/skill_seekers/cli/parsers/quality_parser.py
+++ b/src/skill_seekers/cli/parsers/quality_parser.py
@@ -1,4 +1,5 @@
 """Quality subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/resume_parser.py b/src/skill_seekers/cli/parsers/resume_parser.py
index ad8bc2f..9bb5d07 100644
--- a/src/skill_seekers/cli/parsers/resume_parser.py
+++ b/src/skill_seekers/cli/parsers/resume_parser.py
@@ -1,4 +1,5 @@
 """Resume subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/scrape_parser.py b/src/skill_seekers/cli/parsers/scrape_parser.py
index 68fed31..7184802 100644
--- a/src/skill_seekers/cli/parsers/scrape_parser.py
+++ b/src/skill_seekers/cli/parsers/scrape_parser.py
@@ -1,4 +1,5 @@
 """Scrape subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -24,15 +25,16 @@ class ScrapeParser(SubcommandParser):
         parser.add_argument("--name", help="Skill name")
         parser.add_argument("--description", help="Skill description")
         parser.add_argument(
-            "--max-pages", type=int, dest="max_pages", help="Maximum pages to scrape (override config)"
+            "--max-pages",
+            type=int,
+            dest="max_pages",
+            help="Maximum pages to scrape (override config)",
         )
         parser.add_argument(
             "--skip-scrape", action="store_true", help="Skip scraping, use cached data"
         )
         parser.add_argument("--enhance", action="store_true", help="AI enhancement (API)")
-        parser.add_argument(
-            "--enhance-local", action="store_true", help="AI enhancement (local)"
-        )
+        parser.add_argument("--enhance-local", action="store_true", help="AI enhancement (local)")
         parser.add_argument("--dry-run", action="store_true", help="Dry run mode")
         parser.add_argument(
             "--async", dest="async_mode", action="store_true", help="Use async scraping"
diff --git a/src/skill_seekers/cli/parsers/stream_parser.py b/src/skill_seekers/cli/parsers/stream_parser.py
index 0834a49..6ee513a 100644
--- a/src/skill_seekers/cli/parsers/stream_parser.py
+++ b/src/skill_seekers/cli/parsers/stream_parser.py
@@ -1,4 +1,5 @@
 """Stream subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/test_examples_parser.py b/src/skill_seekers/cli/parsers/test_examples_parser.py
index 76971ff..da2bde9 100644
--- a/src/skill_seekers/cli/parsers/test_examples_parser.py
+++ b/src/skill_seekers/cli/parsers/test_examples_parser.py
@@ -1,4 +1,5 @@
 """Extract-test-examples subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -19,9 +20,7 @@ class TestExamplesParser(SubcommandParser):
 
     def add_arguments(self, parser):
         """Add extract-test-examples-specific arguments."""
-        parser.add_argument(
-            "directory", nargs="?", help="Directory containing test files"
-        )
+        parser.add_argument("directory", nargs="?", help="Directory containing test files")
         parser.add_argument("--file", help="Single test file to analyze")
         parser.add_argument(
             "--language", help="Filter by programming language (python, javascript, etc.)"
@@ -36,6 +35,4 @@ class TestExamplesParser(SubcommandParser):
             "--max-per-file", type=int, default=10, help="Maximum examples per file (default: 10)"
         )
         parser.add_argument("--json", action="store_true", help="Output JSON format")
-        parser.add_argument(
-            "--markdown", action="store_true", help="Output Markdown format"
-        )
+        parser.add_argument("--markdown", action="store_true", help="Output Markdown format")
diff --git a/src/skill_seekers/cli/parsers/unified_parser.py b/src/skill_seekers/cli/parsers/unified_parser.py
index 8b1d5f2..97b9377 100644
--- a/src/skill_seekers/cli/parsers/unified_parser.py
+++ b/src/skill_seekers/cli/parsers/unified_parser.py
@@ -1,4 +1,5 @@
 """Unified subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/update_parser.py b/src/skill_seekers/cli/parsers/update_parser.py
index 1a90425..eaa321a 100644
--- a/src/skill_seekers/cli/parsers/update_parser.py
+++ b/src/skill_seekers/cli/parsers/update_parser.py
@@ -1,4 +1,5 @@
 """Update subcommand parser."""
+
 from .base import SubcommandParser
 
 
diff --git a/src/skill_seekers/cli/parsers/upload_parser.py b/src/skill_seekers/cli/parsers/upload_parser.py
index b9c0793..d807b62 100644
--- a/src/skill_seekers/cli/parsers/upload_parser.py
+++ b/src/skill_seekers/cli/parsers/upload_parser.py
@@ -1,4 +1,5 @@
 """Upload subcommand parser."""
+
 from .base import SubcommandParser
 
 
@@ -19,7 +20,9 @@ class UploadParser(SubcommandParser):
 
     def add_arguments(self, parser):
         """Add upload-specific arguments."""
-        parser.add_argument("package_file", help="Path to skill package file (e.g., output/react.zip)")
+        parser.add_argument(
+            "package_file", help="Path to skill package file (e.g., output/react.zip)"
+        )
 
         parser.add_argument(
             "--target",
@@ -33,22 +36,34 @@ class UploadParser(SubcommandParser):
         # ChromaDB upload options
         parser.add_argument(
             "--chroma-url",
-            help="ChromaDB URL (default: http://localhost:8000 for HTTP, or use --persist-directory for local)"
+            help="ChromaDB URL (default: http://localhost:8000 for HTTP, or use --persist-directory for local)",
         )
         parser.add_argument(
             "--persist-directory",
-            help="Local directory for persistent ChromaDB storage (default: ./chroma_db)"
+            help="Local directory for persistent ChromaDB storage (default: ./chroma_db)",
         )
 
         # Embedding options
         parser.add_argument(
             "--embedding-function",
             choices=["openai", "sentence-transformers", "none"],
-            help="Embedding function for ChromaDB/Weaviate (default: platform default)"
+            help="Embedding function for ChromaDB/Weaviate (default: platform default)",
+        )
+        parser.add_argument(
+            "--openai-api-key", help="OpenAI API key for embeddings (or set OPENAI_API_KEY env var)"
         )
-        parser.add_argument("--openai-api-key", help="OpenAI API key for embeddings (or set OPENAI_API_KEY env var)")
 
         # Weaviate upload options
-        parser.add_argument("--weaviate-url", default="http://localhost:8080", help="Weaviate URL (default: http://localhost:8080)")
-        parser.add_argument("--use-cloud", action="store_true", help="Use Weaviate Cloud (requires --api-key and --cluster-url)")
-        parser.add_argument("--cluster-url", help="Weaviate Cloud cluster URL (e.g., https://xxx.weaviate.network)")
+        parser.add_argument(
+            "--weaviate-url",
+            default="http://localhost:8080",
+            help="Weaviate URL (default: http://localhost:8080)",
+        )
+        parser.add_argument(
+            "--use-cloud",
+            action="store_true",
+            help="Use Weaviate Cloud (requires --api-key and --cluster-url)",
+        )
+        parser.add_argument(
+            "--cluster-url", help="Weaviate Cloud cluster URL (e.g., https://xxx.weaviate.network)"
+        )
diff --git a/src/skill_seekers/cli/pattern_recognizer.py b/src/skill_seekers/cli/pattern_recognizer.py
index 5664abd..aed5d88 100644
--- a/src/skill_seekers/cli/pattern_recognizer.py
+++ b/src/skill_seekers/cli/pattern_recognizer.py
@@ -30,14 +30,14 @@ logger = logging.getLogger(__name__)
 
 # Confidence thresholds for pattern filtering (Issue #240)
 CONFIDENCE_THRESHOLDS = {
-    'critical': 0.80,   # High-confidence patterns for ARCHITECTURE.md
-    'high': 0.70,       # Include in detailed analysis
-    'medium': 0.60,     # Include with warning/context
-    'low': 0.50,        # Minimum detection threshold
+    "critical": 0.80,  # High-confidence patterns for ARCHITECTURE.md
+    "high": 0.70,  # Include in detailed analysis
+    "medium": 0.60,  # Include with warning/context
+    "low": 0.50,  # Minimum detection threshold
 }
 
 # Default minimum confidence for pattern detection
-DEFAULT_MIN_CONFIDENCE = CONFIDENCE_THRESHOLDS['low']
+DEFAULT_MIN_CONFIDENCE = CONFIDENCE_THRESHOLDS["low"]
 
 
 @dataclass
@@ -1697,9 +1697,11 @@ def create_multi_level_report(pattern_results: list[dict]) -> dict:
     all_patterns_sorted = sorted(all_patterns, key=lambda p: p.get("confidence", 0.0), reverse=True)
 
     # Filter by confidence levels
-    critical = filter_patterns_by_confidence(all_patterns_sorted, CONFIDENCE_THRESHOLDS['critical'])
-    high_confidence = filter_patterns_by_confidence(all_patterns_sorted, CONFIDENCE_THRESHOLDS['high'])
-    medium = filter_patterns_by_confidence(all_patterns_sorted, CONFIDENCE_THRESHOLDS['medium'])
+    critical = filter_patterns_by_confidence(all_patterns_sorted, CONFIDENCE_THRESHOLDS["critical"])
+    high_confidence = filter_patterns_by_confidence(
+        all_patterns_sorted, CONFIDENCE_THRESHOLDS["high"]
+    )
+    medium = filter_patterns_by_confidence(all_patterns_sorted, CONFIDENCE_THRESHOLDS["medium"])
 
     return {
         "all_patterns": all_patterns_sorted,
diff --git a/src/skill_seekers/cli/presets.py b/src/skill_seekers/cli/presets.py
index 402d31c..55b573c 100644
--- a/src/skill_seekers/cli/presets.py
+++ b/src/skill_seekers/cli/presets.py
@@ -3,6 +3,7 @@
 Provides predefined analysis configurations with clear trade-offs
 between speed and comprehensiveness.
 """
+
 from dataclasses import dataclass
 
 
@@ -13,6 +14,7 @@ class AnalysisPreset:
     Defines a complete analysis configuration including depth,
     feature flags, and AI enhancement level.
     """
+
     name: str
     description: str
     depth: str  # surface, deep, full
@@ -29,54 +31,52 @@ PRESETS = {
         description="Fast basic analysis (1-2 min, essential features only)",
         depth="surface",
         features={
-            "api_reference": True,      # ON - Essential for API docs
+            "api_reference": True,  # ON - Essential for API docs
             "dependency_graph": False,  # OFF - Slow, not critical for quick
-            "patterns": False,          # OFF - Slow pattern detection
-            "test_examples": False,     # OFF - Time-consuming extraction
-            "how_to_guides": False,     # OFF - Requires AI enhancement
-            "config_patterns": False,   # OFF - Not critical for quick scan
-            "docs": True,               # ON - README/docs are essential
+            "patterns": False,  # OFF - Slow pattern detection
+            "test_examples": False,  # OFF - Time-consuming extraction
+            "how_to_guides": False,  # OFF - Requires AI enhancement
+            "config_patterns": False,  # OFF - Not critical for quick scan
+            "docs": True,  # ON - README/docs are essential
         },
         enhance_level=0,  # No AI enhancement (fast)
         estimated_time="1-2 minutes",
-        icon="⚡"
+        icon="⚡",
     ),
-
     "standard": AnalysisPreset(
         name="Standard",
         description="Balanced analysis (5-10 min, core features, DEFAULT)",
         depth="deep",
         features={
-            "api_reference": True,      # ON - Core feature
-            "dependency_graph": True,   # ON - Valuable insights
-            "patterns": True,           # ON - Design pattern detection
-            "test_examples": True,      # ON - Real usage examples
-            "how_to_guides": False,     # OFF - Requires AI (slow)
-            "config_patterns": True,    # ON - Configuration docs
-            "docs": True,               # ON - Project documentation
+            "api_reference": True,  # ON - Core feature
+            "dependency_graph": True,  # ON - Valuable insights
+            "patterns": True,  # ON - Design pattern detection
+            "test_examples": True,  # ON - Real usage examples
+            "how_to_guides": False,  # OFF - Requires AI (slow)
+            "config_patterns": True,  # ON - Configuration docs
+            "docs": True,  # ON - Project documentation
         },
         enhance_level=1,  # SKILL.md enhancement only
         estimated_time="5-10 minutes",
-        icon="🎯"
+        icon="🎯",
     ),
-
     "comprehensive": AnalysisPreset(
         name="Comprehensive",
         description="Full analysis (20-60 min, all features + AI)",
         depth="full",
         features={
-            "api_reference": True,      # ON - Complete API docs
-            "dependency_graph": True,   # ON - Full dependency analysis
-            "patterns": True,           # ON - All design patterns
-            "test_examples": True,      # ON - All test examples
-            "how_to_guides": True,      # ON - AI-generated guides
-            "config_patterns": True,    # ON - All configuration patterns
-            "docs": True,               # ON - All project docs
+            "api_reference": True,  # ON - Complete API docs
+            "dependency_graph": True,  # ON - Full dependency analysis
+            "patterns": True,  # ON - All design patterns
+            "test_examples": True,  # ON - All test examples
+            "how_to_guides": True,  # ON - AI-generated guides
+            "config_patterns": True,  # ON - All configuration patterns
+            "docs": True,  # ON - All project docs
         },
         enhance_level=3,  # Full AI enhancement (all features)
         estimated_time="20-60 minutes",
-        icon="🚀"
-    )
+        icon="🚀",
+    ),
 }
 
 
@@ -142,10 +142,7 @@ class PresetManager:
             raise ValueError(f"Unknown preset: {preset_name}")
 
         # Start with preset defaults
-        updated_args = {
-            'depth': preset.depth,
-            'enhance_level': preset.enhance_level
-        }
+        updated_args = {"depth": preset.depth, "enhance_level": preset.enhance_level}
 
         # Convert feature flags to skip_* arguments
         # feature=False → skip_feature=True (disabled)
diff --git a/src/skill_seekers/cli/quality_metrics.py b/src/skill_seekers/cli/quality_metrics.py
index 915ff47..c8f8d0d 100644
--- a/src/skill_seekers/cli/quality_metrics.py
+++ b/src/skill_seekers/cli/quality_metrics.py
@@ -16,6 +16,7 @@ from enum import Enum
 
 class MetricLevel(Enum):
     """Metric severity level."""
+
     INFO = "info"
     WARNING = "warning"
     ERROR = "error"
@@ -25,6 +26,7 @@ class MetricLevel(Enum):
 @dataclass
 class QualityMetric:
     """Individual quality metric."""
+
     name: str
     value: float  # 0.0-1.0 (or 0-100 percentage)
     level: MetricLevel
@@ -35,6 +37,7 @@ class QualityMetric:
 @dataclass
 class QualityScore:
     """Overall quality score."""
+
     total_score: float  # 0-100
     completeness: float  # 0-100
     accuracy: float  # 0-100
@@ -46,6 +49,7 @@ class QualityScore:
 @dataclass
 class QualityReport:
     """Complete quality report."""
+
     timestamp: str
     skill_name: str
     overall_score: QualityScore
@@ -64,10 +68,17 @@ class QualityAnalyzer:
 
     # Thresholds for quality grades
     GRADE_THRESHOLDS = {
-        'A+': 95, 'A': 90, 'A-': 85,
-        'B+': 80, 'B': 75, 'B-': 70,
-        'C+': 65, 'C': 60, 'C-': 55,
-        'D': 50, 'F': 0
+        "A+": 95,
+        "A": 90,
+        "A-": 85,
+        "B+": 80,
+        "B": 75,
+        "B-": 70,
+        "C+": 65,
+        "C": 60,
+        "C-": 55,
+        "D": 50,
+        "F": 0,
     }
 
     def __init__(self, skill_dir: Path):
@@ -102,7 +113,7 @@ class QualityAnalyzer:
                 score += 10
 
             # Has sections (10 points)
-            if content.count('#') >= 5:
+            if content.count("#") >= 5:
                 score += 10
 
         # References directory (20 points)
@@ -134,13 +145,15 @@ class QualityAnalyzer:
             if len(suggestions) == 0:
                 suggestions.append("Expand documentation coverage")
 
-        self.metrics.append(QualityMetric(
-            name="Completeness",
-            value=completeness,
-            level=level,
-            description=f"Documentation completeness: {completeness:.1f}%",
-            suggestions=suggestions
-        ))
+        self.metrics.append(
+            QualityMetric(
+                name="Completeness",
+                value=completeness,
+                level=level,
+                description=f"Documentation completeness: {completeness:.1f}%",
+                suggestions=suggestions,
+            )
+        )
 
         return completeness
 
@@ -166,14 +179,14 @@ class QualityAnalyzer:
             content = skill_md.read_text(encoding="utf-8")
 
             # Check for TODO markers (deduct 5 points each, max 20)
-            todo_count = content.lower().count('todo')
+            todo_count = content.lower().count("todo")
             if todo_count > 0:
                 deduction = min(todo_count * 5, 20)
                 score -= deduction
                 issues.append(f"Found {todo_count} TODO markers")
 
             # Check for placeholder text (deduct 10)
-            placeholders = ['lorem ipsum', 'placeholder', 'coming soon']
+            placeholders = ["lorem ipsum", "placeholder", "coming soon"]
             for placeholder in placeholders:
                 if placeholder in content.lower():
                     score -= 10
@@ -195,13 +208,15 @@ class QualityAnalyzer:
         if accuracy < 100 and issues:
             suggestions.extend(issues[:3])  # Top 3 issues
 
-        self.metrics.append(QualityMetric(
-            name="Accuracy",
-            value=accuracy,
-            level=level,
-            description=f"Documentation accuracy: {accuracy:.1f}%",
-            suggestions=suggestions
-        ))
+        self.metrics.append(
+            QualityMetric(
+                name="Accuracy",
+                value=accuracy,
+                level=level,
+                description=f"Documentation accuracy: {accuracy:.1f}%",
+                suggestions=suggestions,
+            )
+        )
 
         return accuracy
 
@@ -234,13 +249,13 @@ class QualityAnalyzer:
             # Check for specific types (20 points each)
             ref_names = [f.stem.lower() for f in ref_files]
 
-            if any('getting' in name or 'start' in name for name in ref_names):
+            if any("getting" in name or "start" in name for name in ref_names):
                 score += 20
 
-            if any('api' in name or 'reference' in name for name in ref_names):
+            if any("api" in name or "reference" in name for name in ref_names):
                 score += 20
 
-            if any('example' in name or 'tutorial' in name for name in ref_names):
+            if any("example" in name or "tutorial" in name for name in ref_names):
                 score += 20
 
             # Has diverse content (10 points)
@@ -258,13 +273,15 @@ class QualityAnalyzer:
                 suggestions.append("Add API reference documentation")
             suggestions.append("Expand documentation coverage")
 
-        self.metrics.append(QualityMetric(
-            name="Coverage",
-            value=coverage,
-            level=level,
-            description=f"Documentation coverage: {coverage:.1f}%",
-            suggestions=suggestions
-        ))
+        self.metrics.append(
+            QualityMetric(
+                name="Coverage",
+                value=coverage,
+                level=level,
+                description=f"Documentation coverage: {coverage:.1f}%",
+                suggestions=suggestions,
+            )
+        )
 
         return coverage
 
@@ -308,56 +325,54 @@ class QualityAnalyzer:
         if health < 100:
             suggestions.extend(issues[:3])
 
-        self.metrics.append(QualityMetric(
-            name="Health",
-            value=health,
-            level=level,
-            description=f"Skill health: {health:.1f}%",
-            suggestions=suggestions
-        ))
+        self.metrics.append(
+            QualityMetric(
+                name="Health",
+                value=health,
+                level=level,
+                description=f"Skill health: {health:.1f}%",
+                suggestions=suggestions,
+            )
+        )
 
         return health
 
     def calculate_statistics(self) -> dict[str, Any]:
         """Calculate skill statistics."""
         stats = {
-            'total_files': 0,
-            'total_size_bytes': 0,
-            'markdown_files': 0,
-            'reference_files': 0,
-            'total_characters': 0,
-            'total_words': 0
+            "total_files": 0,
+            "total_size_bytes": 0,
+            "markdown_files": 0,
+            "reference_files": 0,
+            "total_characters": 0,
+            "total_words": 0,
         }
 
         # Count files and sizes
         for md_file in self.skill_dir.rglob("*.md"):
-            stats['total_files'] += 1
-            stats['markdown_files'] += 1
+            stats["total_files"] += 1
+            stats["markdown_files"] += 1
             size = md_file.stat().st_size
-            stats['total_size_bytes'] += size
+            stats["total_size_bytes"] += size
 
             # Count words
             try:
                 content = md_file.read_text(encoding="utf-8")
-                stats['total_characters'] += len(content)
-                stats['total_words'] += len(content.split())
+                stats["total_characters"] += len(content)
+                stats["total_words"] += len(content.split())
             except Exception:
                 pass
 
         # Count references
         refs_dir = self.skill_dir / "references"
         if refs_dir.exists():
-            stats['reference_files'] = len(list(refs_dir.glob("*.md")))
+            stats["reference_files"] = len(list(refs_dir.glob("*.md")))
 
         self.statistics = stats
         return stats
 
     def calculate_overall_score(
-        self,
-        completeness: float,
-        accuracy: float,
-        coverage: float,
-        health: float
+        self, completeness: float, accuracy: float, coverage: float, health: float
     ) -> QualityScore:
         """
         Calculate overall quality score.
@@ -368,15 +383,10 @@ class QualityAnalyzer:
         - Coverage: 25%
         - Health: 20%
         """
-        total = (
-            completeness * 0.30 +
-            accuracy * 0.25 +
-            coverage * 0.25 +
-            health * 0.20
-        )
+        total = completeness * 0.30 + accuracy * 0.25 + coverage * 0.25 + health * 0.20
 
         # Determine grade
-        grade = 'F'
+        grade = "F"
         for g, threshold in self.GRADE_THRESHOLDS.items():
             if total >= threshold:
                 grade = g
@@ -388,7 +398,7 @@ class QualityAnalyzer:
             accuracy=accuracy,
             coverage=coverage,
             health=health,
-            grade=grade
+            grade=grade,
         )
 
     def generate_recommendations(self, score: QualityScore) -> list[str]:
@@ -431,9 +441,7 @@ class QualityAnalyzer:
         health = self.analyze_health()
 
         # Calculate overall score
-        overall_score = self.calculate_overall_score(
-            completeness, accuracy, coverage, health
-        )
+        overall_score = self.calculate_overall_score(completeness, accuracy, coverage, health)
 
         # Calculate statistics
         stats = self.calculate_statistics()
@@ -447,7 +455,7 @@ class QualityAnalyzer:
             overall_score=overall_score,
             metrics=self.metrics,
             statistics=stats,
-            recommendations=recommendations
+            recommendations=recommendations,
         )
 
     def format_report(self, report: QualityReport) -> str:
@@ -484,7 +492,7 @@ class QualityAnalyzer:
                 MetricLevel.INFO: "✅",
                 MetricLevel.WARNING: "⚠️",
                 MetricLevel.ERROR: "❌",
-                MetricLevel.CRITICAL: "🔴"
+                MetricLevel.CRITICAL: "🔴",
             }.get(metric.level, "ℹ️")
 
             lines.append(f"   {icon} {metric.name}: {metric.value:.1f}%")
@@ -553,4 +561,5 @@ def main():
 
 if __name__ == "__main__":
     import sys
+
     sys.exit(main())
diff --git a/src/skill_seekers/cli/rag_chunker.py b/src/skill_seekers/cli/rag_chunker.py
index d2f3c50..23f8340 100644
--- a/src/skill_seekers/cli/rag_chunker.py
+++ b/src/skill_seekers/cli/rag_chunker.py
@@ -75,10 +75,7 @@ class RAGChunker:
         return len(text) // self.chars_per_token
 
     def chunk_document(
-        self,
-        text: str,
-        metadata: dict,
-        source_file: str | None = None
+        self, text: str, metadata: dict, source_file: str | None = None
     ) -> list[dict]:
         """
         Chunk single document into RAG-ready chunks.
@@ -125,11 +122,13 @@ class RAGChunker:
             if source_file:
                 chunk_metadata["source_file"] = source_file
 
-            result.append({
-                "chunk_id": f"{metadata.get('source', 'unknown')}_{i}",
-                "page_content": chunk_text.strip(),
-                "metadata": chunk_metadata
-            })
+            result.append(
+                {
+                    "chunk_id": f"{metadata.get('source', 'unknown')}_{i}",
+                    "page_content": chunk_text.strip(),
+                    "metadata": chunk_metadata,
+                }
+            )
 
         logger.info(
             f"Created {len(result)} chunks from {source_file or 'document'} "
@@ -153,14 +152,10 @@ class RAGChunker:
         # Chunk main SKILL.md
         skill_md = skill_dir / "SKILL.md"
         if skill_md.exists():
-            with open(skill_md, encoding='utf-8') as f:
+            with open(skill_md, encoding="utf-8") as f:
                 content = f.read()
 
-            metadata = {
-                "source": skill_dir.name,
-                "category": "overview",
-                "file_type": "skill_md"
-            }
+            metadata = {"source": skill_dir.name, "category": "overview", "file_type": "skill_md"}
 
             chunks = self.chunk_document(content, metadata, source_file="SKILL.md")
             all_chunks.extend(chunks)
@@ -169,26 +164,21 @@ class RAGChunker:
         references_dir = skill_dir / "references"
         if references_dir.exists():
             for ref_file in references_dir.glob("*.md"):
-                with open(ref_file, encoding='utf-8') as f:
+                with open(ref_file, encoding="utf-8") as f:
                     content = f.read()
 
                 metadata = {
                     "source": skill_dir.name,
                     "category": ref_file.stem,
-                    "file_type": "reference"
+                    "file_type": "reference",
                 }
 
                 chunks = self.chunk_document(
-                    content,
-                    metadata,
-                    source_file=str(ref_file.relative_to(skill_dir))
+                    content, metadata, source_file=str(ref_file.relative_to(skill_dir))
                 )
                 all_chunks.extend(chunks)
 
-        logger.info(
-            f"Chunked skill directory {skill_dir.name}: "
-            f"{len(all_chunks)} total chunks"
-        )
+        logger.info(f"Chunked skill directory {skill_dir.name}: {len(all_chunks)} total chunks")
 
         return all_chunks
 
@@ -207,32 +197,25 @@ class RAGChunker:
 
         # Match code blocks (``` fenced blocks)
         # Use DOTALL flag to match across newlines
-        code_block_pattern = r'```[^\n]*\n.*?```'
+        code_block_pattern = r"```[^\n]*\n.*?```"
 
         def replacer(match):
             idx = len(code_blocks)
-            code_blocks.append({
-                "index": idx,
-                "content": match.group(0),
-                "start": match.start(),
-                "end": match.end()
-            })
+            code_blocks.append(
+                {
+                    "index": idx,
+                    "content": match.group(0),
+                    "start": match.start(),
+                    "end": match.end(),
+                }
+            )
             return placeholder_pattern.format(idx=idx)
 
-        text_with_placeholders = re.sub(
-            code_block_pattern,
-            replacer,
-            text,
-            flags=re.DOTALL
-        )
+        text_with_placeholders = re.sub(code_block_pattern, replacer, text, flags=re.DOTALL)
 
         return text_with_placeholders, code_blocks
 
-    def _reinsert_code_blocks(
-        self,
-        chunks: list[str],
-        code_blocks: list[dict]
-    ) -> list[str]:
+    def _reinsert_code_blocks(self, chunks: list[str], code_blocks: list[dict]) -> list[str]:
         """
         Re-insert code blocks into chunks.
 
@@ -249,7 +232,7 @@ class RAGChunker:
             for block in code_blocks:
                 placeholder = f"<<CODE_BLOCK_{block['index']}>>"
                 if placeholder in chunk:
-                    chunk = chunk.replace(placeholder, block['content'])
+                    chunk = chunk.replace(placeholder, block["content"])
             result.append(chunk)
 
         return result
@@ -268,15 +251,15 @@ class RAGChunker:
 
         # Paragraph boundaries (double newline)
         if self.preserve_paragraphs:
-            for match in re.finditer(r'\n\n+', text):
+            for match in re.finditer(r"\n\n+", text):
                 boundaries.append(match.end())
 
         # Section headers (# Header)
-        for match in re.finditer(r'\n#{1,6}\s+.+\n', text):
+        for match in re.finditer(r"\n#{1,6}\s+.+\n", text):
             boundaries.append(match.start())
 
         # Single newlines (less preferred, but useful)
-        for match in re.finditer(r'\n', text):
+        for match in re.finditer(r"\n", text):
             boundaries.append(match.start())
 
         # Add artificial boundaries for large documents
@@ -352,7 +335,9 @@ class RAGChunker:
 
             # Add chunk if it meets minimum size requirement
             # (unless the entire text is smaller than target size)
-            if chunk_text.strip() and (len(text) <= target_size_chars or len(chunk_text) >= min_size_chars):
+            if chunk_text.strip() and (
+                len(text) <= target_size_chars or len(chunk_text) >= min_size_chars
+            ):
                 chunks.append(chunk_text)
 
             # Move to next chunk with overlap
@@ -383,7 +368,7 @@ class RAGChunker:
         """
         output_path.parent.mkdir(parents=True, exist_ok=True)
 
-        with open(output_path, 'w', encoding='utf-8') as f:
+        with open(output_path, "w", encoding="utf-8") as f:
             json.dump(chunks, f, indent=2, ensure_ascii=False)
 
         logger.info(f"Saved {len(chunks)} chunks to {output_path}")
@@ -393,7 +378,9 @@ def main():
     """CLI entry point for testing RAG chunker."""
     import argparse
 
-    parser = argparse.ArgumentParser(description="RAG Chunker - Semantic chunking for RAG pipelines")
+    parser = argparse.ArgumentParser(
+        description="RAG Chunker - Semantic chunking for RAG pipelines"
+    )
     parser.add_argument("skill_dir", type=Path, help="Path to skill directory")
     parser.add_argument("--output", "-o", type=Path, help="Output JSON file")
     parser.add_argument("--chunk-size", type=int, default=512, help="Target chunk size in tokens")
diff --git a/src/skill_seekers/cli/storage/__init__.py b/src/skill_seekers/cli/storage/__init__.py
index 3689310..123190d 100644
--- a/src/skill_seekers/cli/storage/__init__.py
+++ b/src/skill_seekers/cli/storage/__init__.py
@@ -59,27 +59,26 @@ def get_storage_adaptor(provider: str, **kwargs) -> BaseStorageAdaptor:
                                      account_name='myaccount')
     """
     adaptors = {
-        's3': S3StorageAdaptor,
-        'gcs': GCSStorageAdaptor,
-        'azure': AzureStorageAdaptor,
+        "s3": S3StorageAdaptor,
+        "gcs": GCSStorageAdaptor,
+        "azure": AzureStorageAdaptor,
     }
 
     provider_lower = provider.lower()
     if provider_lower not in adaptors:
-        supported = ', '.join(adaptors.keys())
+        supported = ", ".join(adaptors.keys())
         raise ValueError(
-            f"Unsupported storage provider: {provider}. "
-            f"Supported providers: {supported}"
+            f"Unsupported storage provider: {provider}. Supported providers: {supported}"
         )
 
     return adaptors[provider_lower](**kwargs)
 
 
 __all__ = [
-    'BaseStorageAdaptor',
-    'StorageObject',
-    'S3StorageAdaptor',
-    'GCSStorageAdaptor',
-    'AzureStorageAdaptor',
-    'get_storage_adaptor',
+    "BaseStorageAdaptor",
+    "StorageObject",
+    "S3StorageAdaptor",
+    "GCSStorageAdaptor",
+    "AzureStorageAdaptor",
+    "get_storage_adaptor",
 ]
diff --git a/src/skill_seekers/cli/storage/azure_storage.py b/src/skill_seekers/cli/storage/azure_storage.py
index 55687e1..1588cf9 100644
--- a/src/skill_seekers/cli/storage/azure_storage.py
+++ b/src/skill_seekers/cli/storage/azure_storage.py
@@ -9,6 +9,7 @@ from datetime import datetime, timedelta
 try:
     from azure.storage.blob import BlobServiceClient, BlobSasPermissions, generate_blob_sas
     from azure.core.exceptions import ResourceNotFoundError
+
     AZURE_AVAILABLE = True
 except ImportError:
     AZURE_AVAILABLE = False
@@ -65,38 +66,30 @@ class AzureStorageAdaptor(BaseStorageAdaptor):
                 "Install with: pip install azure-storage-blob"
             )
 
-        if 'container' not in kwargs:
+        if "container" not in kwargs:
             raise ValueError("container parameter is required for Azure storage")
 
-        self.container_name = kwargs['container']
+        self.container_name = kwargs["container"]
 
         # Initialize BlobServiceClient
-        if 'connection_string' in kwargs:
-            connection_string = kwargs['connection_string']
+        if "connection_string" in kwargs:
+            connection_string = kwargs["connection_string"]
         else:
-            connection_string = os.getenv('AZURE_STORAGE_CONNECTION_STRING')
+            connection_string = os.getenv("AZURE_STORAGE_CONNECTION_STRING")
 
         if connection_string:
-            self.blob_service_client = BlobServiceClient.from_connection_string(
-                connection_string
-            )
+            self.blob_service_client = BlobServiceClient.from_connection_string(connection_string)
             # Extract account name from connection string
             self.account_name = None
             self.account_key = None
-            for part in connection_string.split(';'):
-                if part.startswith('AccountName='):
-                    self.account_name = part.split('=', 1)[1]
-                elif part.startswith('AccountKey='):
-                    self.account_key = part.split('=', 1)[1]
+            for part in connection_string.split(";"):
+                if part.startswith("AccountName="):
+                    self.account_name = part.split("=", 1)[1]
+                elif part.startswith("AccountKey="):
+                    self.account_key = part.split("=", 1)[1]
         else:
-            account_name = kwargs.get(
-                'account_name',
-                os.getenv('AZURE_STORAGE_ACCOUNT_NAME')
-            )
-            account_key = kwargs.get(
-                'account_key',
-                os.getenv('AZURE_STORAGE_ACCOUNT_KEY')
-            )
+            account_name = kwargs.get("account_name", os.getenv("AZURE_STORAGE_ACCOUNT_NAME"))
+            account_key = kwargs.get("account_key", os.getenv("AZURE_STORAGE_ACCOUNT_KEY"))
 
             if not account_name or not account_key:
                 raise ValueError(
@@ -108,13 +101,10 @@ class AzureStorageAdaptor(BaseStorageAdaptor):
             self.account_key = account_key
             account_url = f"https://{account_name}.blob.core.windows.net"
             self.blob_service_client = BlobServiceClient(
-                account_url=account_url,
-                credential=account_key
+                account_url=account_url, credential=account_key
             )
 
-        self.container_client = self.blob_service_client.get_container_client(
-            self.container_name
-        )
+        self.container_client = self.blob_service_client.get_container_client(self.container_name)
 
     def upload_file(
         self, local_path: str, remote_path: str, metadata: dict[str, str] | None = None
@@ -128,11 +118,7 @@ class AzureStorageAdaptor(BaseStorageAdaptor):
             blob_client = self.container_client.get_blob_client(remote_path)
 
             with open(local_file, "rb") as data:
-                blob_client.upload_blob(
-                    data,
-                    overwrite=True,
-                    metadata=metadata
-                )
+                blob_client.upload_blob(data, overwrite=True, metadata=metadata)
 
             return f"https://{self.account_name}.blob.core.windows.net/{self.container_name}/{remote_path}"
         except Exception as e:
@@ -164,25 +150,26 @@ class AzureStorageAdaptor(BaseStorageAdaptor):
         except Exception as e:
             raise Exception(f"Azure deletion failed: {e}") from e
 
-    def list_files(
-        self, prefix: str = "", max_results: int = 1000
-    ) -> list[StorageObject]:
+    def list_files(self, prefix: str = "", max_results: int = 1000) -> list[StorageObject]:
         """List files in Azure container."""
         try:
             blobs = self.container_client.list_blobs(
-                name_starts_with=prefix,
-                results_per_page=max_results
+                name_starts_with=prefix, results_per_page=max_results
             )
 
             files = []
             for blob in blobs:
-                files.append(StorageObject(
-                    key=blob.name,
-                    size=blob.size,
-                    last_modified=blob.last_modified.isoformat() if blob.last_modified else None,
-                    etag=blob.etag,
-                    metadata=blob.metadata
-                ))
+                files.append(
+                    StorageObject(
+                        key=blob.name,
+                        size=blob.size,
+                        last_modified=blob.last_modified.isoformat()
+                        if blob.last_modified
+                        else None,
+                        etag=blob.etag,
+                        metadata=blob.metadata,
+                    )
+                )
 
             return files
         except Exception as e:
@@ -205,9 +192,7 @@ class AzureStorageAdaptor(BaseStorageAdaptor):
                 raise FileNotFoundError(f"Remote file not found: {remote_path}")
 
             if not self.account_name or not self.account_key:
-                raise ValueError(
-                    "Account name and key are required for SAS URL generation"
-                )
+                raise ValueError("Account name and key are required for SAS URL generation")
 
             sas_token = generate_blob_sas(
                 account_name=self.account_name,
@@ -215,7 +200,7 @@ class AzureStorageAdaptor(BaseStorageAdaptor):
                 blob_name=remote_path,
                 account_key=self.account_key,
                 permission=BlobSasPermissions(read=True),
-                expiry=datetime.utcnow() + timedelta(seconds=expires_in)
+                expiry=datetime.utcnow() + timedelta(seconds=expires_in),
             )
 
             return f"{blob_client.url}?{sas_token}"
@@ -239,12 +224,13 @@ class AzureStorageAdaptor(BaseStorageAdaptor):
 
             # Wait for copy to complete
             properties = dest_blob.get_blob_properties()
-            while properties.copy.status == 'pending':
+            while properties.copy.status == "pending":
                 import time
+
                 time.sleep(0.1)
                 properties = dest_blob.get_blob_properties()
 
-            if properties.copy.status != 'success':
+            if properties.copy.status != "success":
                 raise Exception(f"Copy failed with status: {properties.copy.status}")
 
         except FileNotFoundError:
diff --git a/src/skill_seekers/cli/storage/base_storage.py b/src/skill_seekers/cli/storage/base_storage.py
index dd64c8b..d2b16cf 100644
--- a/src/skill_seekers/cli/storage/base_storage.py
+++ b/src/skill_seekers/cli/storage/base_storage.py
@@ -95,9 +95,7 @@ class BaseStorageAdaptor(ABC):
         pass
 
     @abstractmethod
-    def list_files(
-        self, prefix: str = "", max_results: int = 1000
-    ) -> list[StorageObject]:
+    def list_files(self, prefix: str = "", max_results: int = 1000) -> list[StorageObject]:
         """
         List files in cloud storage.
 
@@ -191,9 +189,7 @@ class BaseStorageAdaptor(ABC):
 
         return uploaded_files
 
-    def download_directory(
-        self, remote_prefix: str, local_dir: str
-    ) -> list[str]:
+    def download_directory(self, remote_prefix: str, local_dir: str) -> list[str]:
         """
         Download directory from cloud storage.
 
@@ -245,9 +241,7 @@ class BaseStorageAdaptor(ABC):
             raise FileNotFoundError(f"File not found: {remote_path}")
         return files[0].size
 
-    def copy_file(
-        self, source_path: str, dest_path: str
-    ) -> None:
+    def copy_file(self, source_path: str, dest_path: str) -> None:
         """
         Copy file within cloud storage.
 
diff --git a/src/skill_seekers/cli/storage/gcs_storage.py b/src/skill_seekers/cli/storage/gcs_storage.py
index 5e091c5..1330186 100644
--- a/src/skill_seekers/cli/storage/gcs_storage.py
+++ b/src/skill_seekers/cli/storage/gcs_storage.py
@@ -9,6 +9,7 @@ from datetime import timedelta
 try:
     from google.cloud import storage
     from google.cloud.exceptions import NotFound
+
     GCS_AVAILABLE = True
 except ImportError:
     GCS_AVAILABLE = False
@@ -63,19 +64,19 @@ class GCSStorageAdaptor(BaseStorageAdaptor):
                 "Install with: pip install google-cloud-storage"
             )
 
-        if 'bucket' not in kwargs:
+        if "bucket" not in kwargs:
             raise ValueError("bucket parameter is required for GCS storage")
 
-        self.bucket_name = kwargs['bucket']
-        self.project = kwargs.get('project', os.getenv('GOOGLE_CLOUD_PROJECT'))
+        self.bucket_name = kwargs["bucket"]
+        self.project = kwargs.get("project", os.getenv("GOOGLE_CLOUD_PROJECT"))
 
         # Initialize GCS client
         client_kwargs = {}
         if self.project:
-            client_kwargs['project'] = self.project
+            client_kwargs["project"] = self.project
 
-        if 'credentials_path' in kwargs:
-            os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = kwargs['credentials_path']
+        if "credentials_path" in kwargs:
+            os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = kwargs["credentials_path"]
 
         self.storage_client = storage.Client(**client_kwargs)
         self.bucket = self.storage_client.bucket(self.bucket_name)
@@ -122,26 +123,24 @@ class GCSStorageAdaptor(BaseStorageAdaptor):
         except Exception as e:
             raise Exception(f"GCS deletion failed: {e}") from e
 
-    def list_files(
-        self, prefix: str = "", max_results: int = 1000
-    ) -> list[StorageObject]:
+    def list_files(self, prefix: str = "", max_results: int = 1000) -> list[StorageObject]:
         """List files in GCS bucket."""
         try:
             blobs = self.storage_client.list_blobs(
-                self.bucket_name,
-                prefix=prefix,
-                max_results=max_results
+                self.bucket_name, prefix=prefix, max_results=max_results
             )
 
             files = []
             for blob in blobs:
-                files.append(StorageObject(
-                    key=blob.name,
-                    size=blob.size,
-                    last_modified=blob.updated.isoformat() if blob.updated else None,
-                    etag=blob.etag,
-                    metadata=blob.metadata
-                ))
+                files.append(
+                    StorageObject(
+                        key=blob.name,
+                        size=blob.size,
+                        last_modified=blob.updated.isoformat() if blob.updated else None,
+                        etag=blob.etag,
+                        metadata=blob.metadata,
+                    )
+                )
 
             return files
         except Exception as e:
@@ -164,9 +163,7 @@ class GCSStorageAdaptor(BaseStorageAdaptor):
                 raise FileNotFoundError(f"Remote file not found: {remote_path}")
 
             url = blob.generate_signed_url(
-                version="v4",
-                expiration=timedelta(seconds=expires_in),
-                method="GET"
+                version="v4", expiration=timedelta(seconds=expires_in), method="GET"
             )
             return url
         except FileNotFoundError:
@@ -182,11 +179,7 @@ class GCSStorageAdaptor(BaseStorageAdaptor):
             if not source_blob.exists():
                 raise FileNotFoundError(f"Source file not found: {source_path}")
 
-            self.bucket.copy_blob(
-                source_blob,
-                self.bucket,
-                dest_path
-            )
+            self.bucket.copy_blob(source_blob, self.bucket, dest_path)
         except FileNotFoundError:
             raise
         except Exception as e:
diff --git a/src/skill_seekers/cli/storage/s3_storage.py b/src/skill_seekers/cli/storage/s3_storage.py
index 57af41b..40f8d78 100644
--- a/src/skill_seekers/cli/storage/s3_storage.py
+++ b/src/skill_seekers/cli/storage/s3_storage.py
@@ -8,6 +8,7 @@ from pathlib import Path
 try:
     import boto3
     from botocore.exceptions import ClientError
+
     BOTO3_AVAILABLE = True
 except ImportError:
     BOTO3_AVAILABLE = False
@@ -63,33 +64,30 @@ class S3StorageAdaptor(BaseStorageAdaptor):
         super().__init__(**kwargs)
 
         if not BOTO3_AVAILABLE:
-            raise ImportError(
-                "boto3 is required for S3 storage. "
-                "Install with: pip install boto3"
-            )
+            raise ImportError("boto3 is required for S3 storage. Install with: pip install boto3")
 
-        if 'bucket' not in kwargs:
+        if "bucket" not in kwargs:
             raise ValueError("bucket parameter is required for S3 storage")
 
-        self.bucket = kwargs['bucket']
-        self.region = kwargs.get('region', os.getenv('AWS_DEFAULT_REGION', 'us-east-1'))
+        self.bucket = kwargs["bucket"]
+        self.region = kwargs.get("region", os.getenv("AWS_DEFAULT_REGION", "us-east-1"))
 
         # Initialize S3 client
         client_kwargs = {
-            'region_name': self.region,
+            "region_name": self.region,
         }
 
-        if 'endpoint_url' in kwargs:
-            client_kwargs['endpoint_url'] = kwargs['endpoint_url']
+        if "endpoint_url" in kwargs:
+            client_kwargs["endpoint_url"] = kwargs["endpoint_url"]
 
-        if 'aws_access_key_id' in kwargs:
-            client_kwargs['aws_access_key_id'] = kwargs['aws_access_key_id']
+        if "aws_access_key_id" in kwargs:
+            client_kwargs["aws_access_key_id"] = kwargs["aws_access_key_id"]
 
-        if 'aws_secret_access_key' in kwargs:
-            client_kwargs['aws_secret_access_key'] = kwargs['aws_secret_access_key']
+        if "aws_secret_access_key" in kwargs:
+            client_kwargs["aws_secret_access_key"] = kwargs["aws_secret_access_key"]
 
-        self.s3_client = boto3.client('s3', **client_kwargs)
-        self.s3_resource = boto3.resource('s3', **client_kwargs)
+        self.s3_client = boto3.client("s3", **client_kwargs)
+        self.s3_resource = boto3.resource("s3", **client_kwargs)
 
     def upload_file(
         self, local_path: str, remote_path: str, metadata: dict[str, str] | None = None
@@ -101,14 +99,14 @@ class S3StorageAdaptor(BaseStorageAdaptor):
 
         extra_args = {}
         if metadata:
-            extra_args['Metadata'] = metadata
+            extra_args["Metadata"] = metadata
 
         try:
             self.s3_client.upload_file(
                 str(local_file),
                 self.bucket,
                 remote_path,
-                ExtraArgs=extra_args if extra_args else None
+                ExtraArgs=extra_args if extra_args else None,
             )
             return f"s3://{self.bucket}/{remote_path}"
         except ClientError as e:
@@ -120,50 +118,41 @@ class S3StorageAdaptor(BaseStorageAdaptor):
         local_file.parent.mkdir(parents=True, exist_ok=True)
 
         try:
-            self.s3_client.download_file(
-                self.bucket,
-                remote_path,
-                str(local_file)
-            )
+            self.s3_client.download_file(self.bucket, remote_path, str(local_file))
         except ClientError as e:
-            if e.response['Error']['Code'] == '404':
+            if e.response["Error"]["Code"] == "404":
                 raise FileNotFoundError(f"Remote file not found: {remote_path}") from e
             raise Exception(f"S3 download failed: {e}") from e
 
     def delete_file(self, remote_path: str) -> None:
         """Delete file from S3."""
         try:
-            self.s3_client.delete_object(
-                Bucket=self.bucket,
-                Key=remote_path
-            )
+            self.s3_client.delete_object(Bucket=self.bucket, Key=remote_path)
         except ClientError as e:
             raise Exception(f"S3 deletion failed: {e}") from e
 
-    def list_files(
-        self, prefix: str = "", max_results: int = 1000
-    ) -> list[StorageObject]:
+    def list_files(self, prefix: str = "", max_results: int = 1000) -> list[StorageObject]:
         """List files in S3 bucket."""
         try:
-            paginator = self.s3_client.get_paginator('list_objects_v2')
+            paginator = self.s3_client.get_paginator("list_objects_v2")
             page_iterator = paginator.paginate(
-                Bucket=self.bucket,
-                Prefix=prefix,
-                PaginationConfig={'MaxItems': max_results}
+                Bucket=self.bucket, Prefix=prefix, PaginationConfig={"MaxItems": max_results}
             )
 
             files = []
             for page in page_iterator:
-                if 'Contents' not in page:
+                if "Contents" not in page:
                     continue
 
-                for obj in page['Contents']:
-                    files.append(StorageObject(
-                        key=obj['Key'],
-                        size=obj['Size'],
-                        last_modified=obj['LastModified'].isoformat(),
-                        etag=obj.get('ETag', '').strip('"')
-                    ))
+                for obj in page["Contents"]:
+                    files.append(
+                        StorageObject(
+                            key=obj["Key"],
+                            size=obj["Size"],
+                            last_modified=obj["LastModified"].isoformat(),
+                            etag=obj.get("ETag", "").strip('"'),
+                        )
+                    )
 
             return files
         except ClientError as e:
@@ -172,13 +161,10 @@ class S3StorageAdaptor(BaseStorageAdaptor):
     def file_exists(self, remote_path: str) -> bool:
         """Check if file exists in S3."""
         try:
-            self.s3_client.head_object(
-                Bucket=self.bucket,
-                Key=remote_path
-            )
+            self.s3_client.head_object(Bucket=self.bucket, Key=remote_path)
             return True
         except ClientError as e:
-            if e.response['Error']['Code'] == '404':
+            if e.response["Error"]["Code"] == "404":
                 return False
             raise Exception(f"S3 head_object failed: {e}") from e
 
@@ -186,12 +172,9 @@ class S3StorageAdaptor(BaseStorageAdaptor):
         """Generate presigned URL for S3 object."""
         try:
             url = self.s3_client.generate_presigned_url(
-                'get_object',
-                Params={
-                    'Bucket': self.bucket,
-                    'Key': remote_path
-                },
-                ExpiresIn=expires_in
+                "get_object",
+                Params={"Bucket": self.bucket, "Key": remote_path},
+                ExpiresIn=expires_in,
             )
             return url
         except ClientError as e:
@@ -200,16 +183,9 @@ class S3StorageAdaptor(BaseStorageAdaptor):
     def copy_file(self, source_path: str, dest_path: str) -> None:
         """Copy file within S3 bucket (server-side copy)."""
         try:
-            copy_source = {
-                'Bucket': self.bucket,
-                'Key': source_path
-            }
-            self.s3_client.copy_object(
-                CopySource=copy_source,
-                Bucket=self.bucket,
-                Key=dest_path
-            )
+            copy_source = {"Bucket": self.bucket, "Key": source_path}
+            self.s3_client.copy_object(CopySource=copy_source, Bucket=self.bucket, Key=dest_path)
         except ClientError as e:
-            if e.response['Error']['Code'] == '404':
+            if e.response["Error"]["Code"] == "404":
                 raise FileNotFoundError(f"Source file not found: {source_path}") from e
             raise Exception(f"S3 copy failed: {e}") from e
diff --git a/src/skill_seekers/cli/streaming_ingest.py b/src/skill_seekers/cli/streaming_ingest.py
index dd732ad..152e4fc 100644
--- a/src/skill_seekers/cli/streaming_ingest.py
+++ b/src/skill_seekers/cli/streaming_ingest.py
@@ -17,6 +17,7 @@ import time
 @dataclass
 class ChunkMetadata:
     """Metadata for a document chunk."""
+
     chunk_id: str
     source: str
     category: str
@@ -30,6 +31,7 @@ class ChunkMetadata:
 @dataclass
 class IngestionProgress:
     """Progress tracking for streaming ingestion."""
+
     total_documents: int
     processed_documents: int
     total_chunks: int
@@ -81,7 +83,7 @@ class StreamingIngester:
         chunk_size: int = 4000,
         chunk_overlap: int = 200,
         batch_size: int = 100,
-        max_memory_mb: int = 500
+        max_memory_mb: int = 500,
     ):
         """
         Initialize streaming ingester.
@@ -103,7 +105,7 @@ class StreamingIngester:
         content: str,
         metadata: dict,
         chunk_size: int | None = None,
-        chunk_overlap: int | None = None
+        chunk_overlap: int | None = None,
     ) -> Iterator[tuple[str, ChunkMetadata]]:
         """
         Split document into overlapping chunks.
@@ -130,7 +132,7 @@ class StreamingIngester:
                 chunk_index=0,
                 total_chunks=1,
                 char_start=0,
-                char_end=len(content)
+                char_end=len(content),
             )
             yield content, chunk_meta
             return
@@ -162,7 +164,7 @@ class StreamingIngester:
                 chunk_index=i,
                 total_chunks=total_chunks,
                 char_start=start,
-                char_end=end
+                char_end=end,
             )
 
             yield chunk_text, chunk_meta
@@ -170,17 +172,12 @@ class StreamingIngester:
     def _generate_chunk_id(self, content: str, metadata: dict, chunk_index: int) -> str:
         """Generate deterministic chunk ID."""
         id_string = (
-            f"{metadata.get('source', '')}-"
-            f"{metadata.get('file', '')}-"
-            f"{chunk_index}-"
-            f"{content[:50]}"
+            f"{metadata.get('source', '')}-{metadata.get('file', '')}-{chunk_index}-{content[:50]}"
         )
         return hashlib.md5(id_string.encode()).hexdigest()
 
     def stream_skill_directory(
-        self,
-        skill_dir: Path,
-        callback: callable | None = None
+        self, skill_dir: Path, callback: callable | None = None
     ) -> Iterator[tuple[str, dict]]:
         """
         Stream all documents from skill directory.
@@ -218,7 +215,7 @@ class StreamingIngester:
             processed_chunks=0,
             failed_chunks=0,
             bytes_processed=0,
-            start_time=time.time()
+            start_time=time.time(),
         )
 
         # Process each document
@@ -235,11 +232,13 @@ class StreamingIngester:
                     "category": category,
                     "file": filename,
                     "type": "documentation" if filename == "SKILL.md" else "reference",
-                    "version": "1.0.0"
+                    "version": "1.0.0",
                 }
 
                 # Chunk document and yield chunks
-                for chunk_count, (chunk_text, chunk_meta) in enumerate(self.chunk_document(content, metadata), start=1):
+                for chunk_count, (chunk_text, chunk_meta) in enumerate(
+                    self.chunk_document(content, metadata), start=1
+                ):
                     self.progress.total_chunks += 1
 
                     # Convert chunk metadata to dict
@@ -272,9 +271,7 @@ class StreamingIngester:
                 continue
 
     def batch_iterator(
-        self,
-        chunks: Iterator[tuple[str, dict]],
-        batch_size: int | None = None
+        self, chunks: Iterator[tuple[str, dict]], batch_size: int | None = None
     ) -> Iterator[list[tuple[str, dict]]]:
         """
         Group chunks into batches for efficient processing.
@@ -321,7 +318,7 @@ class StreamingIngester:
                 "failed_chunks": self.progress.failed_chunks,
                 "bytes_processed": self.progress.bytes_processed,
             },
-            "state": state
+            "state": state,
         }
 
         checkpoint_path.write_text(json.dumps(checkpoint_data, indent=2))
@@ -384,23 +381,25 @@ def main():
     parser = argparse.ArgumentParser(description="Stream and chunk skill documents")
     parser.add_argument("input", help="Input file or directory path")
     parser.add_argument("--chunk-size", type=int, default=4000, help="Chunk size in characters")
-    parser.add_argument("--chunk-overlap", type=int, default=200, help="Chunk overlap in characters")
+    parser.add_argument(
+        "--chunk-overlap", type=int, default=200, help="Chunk overlap in characters"
+    )
     parser.add_argument("--batch-size", type=int, default=100, help="Batch size for processing")
     parser.add_argument("--checkpoint", help="Checkpoint file path")
     args = parser.parse_args()
 
     # Initialize ingester
     ingester = StreamingIngester(
-        chunk_size=args.chunk_size,
-        chunk_overlap=args.chunk_overlap,
-        batch_size=args.batch_size
+        chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap, batch_size=args.batch_size
     )
 
     # Progress callback
     def on_progress(progress: IngestionProgress):
         if progress.processed_chunks % 10 == 0:
-            print(f"Progress: {progress.progress_percent:.1f}% - "
-                  f"{progress.processed_chunks}/{progress.total_chunks} chunks")
+            print(
+                f"Progress: {progress.progress_percent:.1f}% - "
+                f"{progress.processed_chunks}/{progress.total_chunks} chunks"
+            )
 
     # Stream input
     input_path = Path(args.input)
@@ -416,17 +415,23 @@ def main():
         metadata = {"source": input_path.stem, "file": input_path.name}
         file_chunks = ingester.chunk_document(content, metadata)
         # Convert to generator format matching stream_skill_directory
-        chunks = ((text, {
-            "content": text,
-            "chunk_id": meta.chunk_id,
-            "source": meta.source,
-            "category": meta.category,
-            "file": meta.file,
-            "chunk_index": meta.chunk_index,
-            "total_chunks": meta.total_chunks,
-            "char_start": meta.char_start,
-            "char_end": meta.char_end,
-        }) for text, meta in file_chunks)
+        chunks = (
+            (
+                text,
+                {
+                    "content": text,
+                    "chunk_id": meta.chunk_id,
+                    "source": meta.source,
+                    "category": meta.category,
+                    "file": meta.file,
+                    "chunk_index": meta.chunk_index,
+                    "total_chunks": meta.total_chunks,
+                    "char_start": meta.char_start,
+                    "char_end": meta.char_end,
+                },
+            )
+            for text, meta in file_chunks
+        )
 
     # Process in batches
     all_chunks = []
@@ -437,8 +442,7 @@ def main():
         # Save checkpoint if specified
         if args.checkpoint:
             ingester.save_checkpoint(
-                Path(args.checkpoint),
-                {"processed_batches": len(all_chunks) // args.batch_size}
+                Path(args.checkpoint), {"processed_batches": len(all_chunks) // args.batch_size}
             )
 
     # Final progress
@@ -449,4 +453,5 @@ def main():
 
 if __name__ == "__main__":
     import sys
+
     sys.exit(main())
diff --git a/src/skill_seekers/cli/sync_cli.py b/src/skill_seekers/cli/sync_cli.py
index ff54d8c..616c307 100644
--- a/src/skill_seekers/cli/sync_cli.py
+++ b/src/skill_seekers/cli/sync_cli.py
@@ -22,9 +22,7 @@ def handle_signal(_signum, _frame):
 def start_command(args):
     """Start monitoring."""
     monitor = SyncMonitor(
-        config_path=args.config,
-        check_interval=args.interval,
-        auto_update=args.auto_update
+        config_path=args.config, check_interval=args.interval, auto_update=args.auto_update
     )
 
     # Register signal handlers
@@ -42,6 +40,7 @@ def start_command(args):
         # Keep running
         while True:
             import time
+
             time.sleep(1)
 
     except KeyboardInterrupt:
@@ -53,7 +52,7 @@ def check_command(args):
     """Check for changes once."""
     monitor = SyncMonitor(
         config_path=args.config,
-        check_interval=3600  # Not used for single check
+        check_interval=3600,  # Not used for single check
     )
 
     print(f"🔍 Checking {args.config} for changes...")
@@ -82,7 +81,7 @@ def check_command(args):
                     print(f"   • {change.url}")
                     if change.diff and args.diff:
                         print(f"      Diff preview (first 5 lines):")
-                        for line in change.diff.split('\n')[:5]:
+                        for line in change.diff.split("\n")[:5]:
                             print(f"        {line}")
 
             if report.deleted:
@@ -95,10 +94,7 @@ def check_command(args):
 
 def stats_command(args):
     """Show monitoring statistics."""
-    monitor = SyncMonitor(
-        config_path=args.config,
-        check_interval=3600
-    )
+    monitor = SyncMonitor(config_path=args.config, check_interval=3600)
 
     stats = monitor.stats()
 
@@ -117,7 +113,7 @@ def reset_command(args):
     state_file = Path(f"{args.skill_name}_sync.json")
 
     if state_file.exists():
-        if args.force or input(f"⚠️  Reset state for {args.skill_name}? [y/N]: ").lower() == 'y':
+        if args.force or input(f"⚠️  Reset state for {args.skill_name}? [y/N]: ").lower() == "y":
             state_file.unlink()
             print(f"✅ State reset for {args.skill_name}")
         else:
@@ -129,7 +125,7 @@ def reset_command(args):
 def main():
     """Main entry point."""
     parser = argparse.ArgumentParser(
-        description='Monitor documentation for changes and update skills',
+        description="Monitor documentation for changes and update skills",
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
 Examples:
@@ -153,52 +149,39 @@ Examples:
 
   # Reset state
   skill-seekers-sync reset --skill-name react
-        """
+        """,
     )
 
-    subparsers = parser.add_subparsers(dest='command', help='Command to execute')
+    subparsers = parser.add_subparsers(dest="command", help="Command to execute")
 
     # Start command
-    start_parser = subparsers.add_parser('start', help='Start continuous monitoring')
-    start_parser.add_argument('--config', required=True, help='Path to skill config file')
+    start_parser = subparsers.add_parser("start", help="Start continuous monitoring")
+    start_parser.add_argument("--config", required=True, help="Path to skill config file")
     start_parser.add_argument(
-        '--interval', '-i',
+        "--interval",
+        "-i",
         type=int,
         default=3600,
-        help='Check interval in seconds (default: 3600 = 1 hour)'
+        help="Check interval in seconds (default: 3600 = 1 hour)",
     )
     start_parser.add_argument(
-        '--auto-update',
-        action='store_true',
-        help='Automatically rebuild skill on changes'
+        "--auto-update", action="store_true", help="Automatically rebuild skill on changes"
     )
 
     # Check command
-    check_parser = subparsers.add_parser('check', help='Check for changes once')
-    check_parser.add_argument('--config', required=True, help='Path to skill config file')
-    check_parser.add_argument(
-        '--diff', '-d',
-        action='store_true',
-        help='Generate content diffs'
-    )
-    check_parser.add_argument(
-        '--verbose', '-v',
-        action='store_true',
-        help='Show detailed output'
-    )
+    check_parser = subparsers.add_parser("check", help="Check for changes once")
+    check_parser.add_argument("--config", required=True, help="Path to skill config file")
+    check_parser.add_argument("--diff", "-d", action="store_true", help="Generate content diffs")
+    check_parser.add_argument("--verbose", "-v", action="store_true", help="Show detailed output")
 
     # Stats command
-    stats_parser = subparsers.add_parser('stats', help='Show monitoring statistics')
-    stats_parser.add_argument('--config', required=True, help='Path to skill config file')
+    stats_parser = subparsers.add_parser("stats", help="Show monitoring statistics")
+    stats_parser.add_argument("--config", required=True, help="Path to skill config file")
 
     # Reset command
-    reset_parser = subparsers.add_parser('reset', help='Reset monitoring state')
-    reset_parser.add_argument('--skill-name', required=True, help='Skill name')
-    reset_parser.add_argument(
-        '--force', '-f',
-        action='store_true',
-        help='Skip confirmation'
-    )
+    reset_parser = subparsers.add_parser("reset", help="Reset monitoring state")
+    reset_parser.add_argument("--skill-name", required=True, help="Skill name")
+    reset_parser.add_argument("--force", "-f", action="store_true", help="Skip confirmation")
 
     args = parser.parse_args()
 
@@ -207,18 +190,18 @@ Examples:
         sys.exit(1)
 
     try:
-        if args.command == 'start':
+        if args.command == "start":
             start_command(args)
-        elif args.command == 'check':
+        elif args.command == "check":
             check_command(args)
-        elif args.command == 'stats':
+        elif args.command == "stats":
             stats_command(args)
-        elif args.command == 'reset':
+        elif args.command == "reset":
             reset_command(args)
     except Exception as e:
         print(f"\n❌ Error: {e}", file=sys.stderr)
         sys.exit(1)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/src/skill_seekers/cli/upload_skill.py b/src/skill_seekers/cli/upload_skill.py
index bd245dd..5ade937 100755
--- a/src/skill_seekers/cli/upload_skill.py
+++ b/src/skill_seekers/cli/upload_skill.py
@@ -59,7 +59,7 @@ def upload_skill_api(package_path, target="claude", api_key=None, **kwargs):
         api_key = os.environ.get(adaptor.get_env_var_name(), "").strip()
 
     # API key validation only for platforms that require it
-    if target in ['claude', 'gemini', 'openai']:
+    if target in ["claude", "gemini", "openai"]:
         if not api_key:
             return False, f"{adaptor.get_env_var_name()} not set. Export your API key first."
 
@@ -172,41 +172,39 @@ Examples:
     # ChromaDB upload options
     parser.add_argument(
         "--chroma-url",
-        help="ChromaDB URL (default: http://localhost:8000 for HTTP, or use --persist-directory for local)"
+        help="ChromaDB URL (default: http://localhost:8000 for HTTP, or use --persist-directory for local)",
     )
 
     parser.add_argument(
         "--persist-directory",
-        help="Local directory for persistent ChromaDB storage (default: ./chroma_db)"
+        help="Local directory for persistent ChromaDB storage (default: ./chroma_db)",
     )
 
     parser.add_argument(
         "--embedding-function",
         choices=["openai", "sentence-transformers", "none"],
-        help="Embedding function for ChromaDB/Weaviate (default: platform default)"
+        help="Embedding function for ChromaDB/Weaviate (default: platform default)",
     )
 
     parser.add_argument(
-        "--openai-api-key",
-        help="OpenAI API key for embeddings (or set OPENAI_API_KEY env var)"
+        "--openai-api-key", help="OpenAI API key for embeddings (or set OPENAI_API_KEY env var)"
     )
 
     # Weaviate upload options
     parser.add_argument(
         "--weaviate-url",
         default="http://localhost:8080",
-        help="Weaviate URL (default: http://localhost:8080)"
+        help="Weaviate URL (default: http://localhost:8080)",
     )
 
     parser.add_argument(
         "--use-cloud",
         action="store_true",
-        help="Use Weaviate Cloud (requires --api-key and --cluster-url)"
+        help="Use Weaviate Cloud (requires --api-key and --cluster-url)",
     )
 
     parser.add_argument(
-        "--cluster-url",
-        help="Weaviate Cloud cluster URL (e.g., https://xxx.weaviate.network)"
+        "--cluster-url", help="Weaviate Cloud cluster URL (e.g., https://xxx.weaviate.network)"
     )
 
     args = parser.parse_args()
@@ -214,28 +212,30 @@ Examples:
     # Build kwargs for vector DB upload
     upload_kwargs = {}
 
-    if args.target == 'chroma':
+    if args.target == "chroma":
         if args.chroma_url:
-            upload_kwargs['chroma_url'] = args.chroma_url
+            upload_kwargs["chroma_url"] = args.chroma_url
         if args.persist_directory:
-            upload_kwargs['persist_directory'] = args.persist_directory
+            upload_kwargs["persist_directory"] = args.persist_directory
         if args.embedding_function:
-            upload_kwargs['embedding_function'] = args.embedding_function
+            upload_kwargs["embedding_function"] = args.embedding_function
         if args.openai_api_key:
-            upload_kwargs['openai_api_key'] = args.openai_api_key
+            upload_kwargs["openai_api_key"] = args.openai_api_key
 
-    elif args.target == 'weaviate':
-        upload_kwargs['weaviate_url'] = args.weaviate_url
-        upload_kwargs['use_cloud'] = args.use_cloud
+    elif args.target == "weaviate":
+        upload_kwargs["weaviate_url"] = args.weaviate_url
+        upload_kwargs["use_cloud"] = args.use_cloud
         if args.cluster_url:
-            upload_kwargs['cluster_url'] = args.cluster_url
+            upload_kwargs["cluster_url"] = args.cluster_url
         if args.embedding_function:
-            upload_kwargs['embedding_function'] = args.embedding_function
+            upload_kwargs["embedding_function"] = args.embedding_function
         if args.openai_api_key:
-            upload_kwargs['openai_api_key'] = args.openai_api_key
+            upload_kwargs["openai_api_key"] = args.openai_api_key
 
     # Upload skill
-    success, message = upload_skill_api(args.package_file, args.target, args.api_key, **upload_kwargs)
+    success, message = upload_skill_api(
+        args.package_file, args.target, args.api_key, **upload_kwargs
+    )
 
     if success:
         sys.exit(0)
diff --git a/src/skill_seekers/embedding/__init__.py b/src/skill_seekers/embedding/__init__.py
index a37b71e..d2cedaf 100644
--- a/src/skill_seekers/embedding/__init__.py
+++ b/src/skill_seekers/embedding/__init__.py
@@ -23,9 +23,9 @@ from .generator import EmbeddingGenerator
 from .cache import EmbeddingCache
 
 __all__ = [
-    'EmbeddingRequest',
-    'EmbeddingResponse',
-    'BatchEmbeddingRequest',
-    'EmbeddingGenerator',
-    'EmbeddingCache',
+    "EmbeddingRequest",
+    "EmbeddingResponse",
+    "BatchEmbeddingRequest",
+    "EmbeddingGenerator",
+    "EmbeddingCache",
 ]
diff --git a/src/skill_seekers/embedding/cache.py b/src/skill_seekers/embedding/cache.py
index fcb4bda..fcb31a0 100644
--- a/src/skill_seekers/embedding/cache.py
+++ b/src/skill_seekers/embedding/cache.py
@@ -74,12 +74,7 @@ class EmbeddingCache:
 
         self.conn.commit()
 
-    def set(
-        self,
-        hash_key: str,
-        embedding: list[float],
-        model: str
-    ) -> None:
+    def set(self, hash_key: str, embedding: list[float], model: str) -> None:
         """
         Store embedding in cache.
 
@@ -94,11 +89,14 @@ class EmbeddingCache:
         embedding_json = json.dumps(embedding)
         dimensions = len(embedding)
 
-        cursor.execute("""
+        cursor.execute(
+            """
             INSERT OR REPLACE INTO embeddings
             (hash, embedding, model, dimensions, created_at, accessed_at, access_count)
             VALUES (?, ?, ?, ?, ?, ?, 1)
-        """, (hash_key, embedding_json, model, dimensions, now, now))
+        """,
+            (hash_key, embedding_json, model, dimensions, now, now),
+        )
 
         self.conn.commit()
 
@@ -115,11 +113,14 @@ class EmbeddingCache:
         cursor = self.conn.cursor()
 
         # Get embedding
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT embedding, created_at
             FROM embeddings
             WHERE hash = ?
-        """, (hash_key,))
+        """,
+            (hash_key,),
+        )
 
         row = cursor.fetchone()
         if not row:
@@ -136,11 +137,14 @@ class EmbeddingCache:
 
         # Update access stats
         now = datetime.utcnow().isoformat()
-        cursor.execute("""
+        cursor.execute(
+            """
             UPDATE embeddings
             SET accessed_at = ?, access_count = access_count + 1
             WHERE hash = ?
-        """, (now, hash_key))
+        """,
+            (now, hash_key),
+        )
         self.conn.commit()
 
         return json.loads(embedding_json)
@@ -178,11 +182,14 @@ class EmbeddingCache:
         """
         cursor = self.conn.cursor()
 
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT created_at
             FROM embeddings
             WHERE hash = ?
-        """, (hash_key,))
+        """,
+            (hash_key,),
+        )
 
         row = cursor.fetchone()
         if not row:
@@ -206,10 +213,13 @@ class EmbeddingCache:
         """
         cursor = self.conn.cursor()
 
-        cursor.execute("""
+        cursor.execute(
+            """
             DELETE FROM embeddings
             WHERE hash = ?
-        """, (hash_key,))
+        """,
+            (hash_key,),
+        )
 
         self.conn.commit()
 
@@ -226,10 +236,13 @@ class EmbeddingCache:
         cursor = self.conn.cursor()
 
         if model:
-            cursor.execute("""
+            cursor.execute(
+                """
                 DELETE FROM embeddings
                 WHERE model = ?
-            """, (model,))
+            """,
+                (model,),
+            )
         else:
             cursor.execute("DELETE FROM embeddings")
 
@@ -249,10 +262,13 @@ class EmbeddingCache:
 
         cutoff = (datetime.utcnow() - timedelta(days=self.ttl_days)).isoformat()
 
-        cursor.execute("""
+        cursor.execute(
+            """
             DELETE FROM embeddings
             WHERE created_at < ?
-        """, (cutoff,))
+        """,
+            (cutoff,),
+        )
 
         deleted = cursor.rowcount
         self.conn.commit()
@@ -300,17 +316,19 @@ class EmbeddingCache:
             LIMIT 10
         """)
         top_accessed = [
-            {"hash": row[0], "model": row[1], "access_count": row[2]}
-            for row in cursor.fetchall()
+            {"hash": row[0], "model": row[1], "access_count": row[2]} for row in cursor.fetchall()
         ]
 
         # Expired entries
         cutoff = (datetime.utcnow() - timedelta(days=self.ttl_days)).isoformat()
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT COUNT(*)
             FROM embeddings
             WHERE created_at < ?
-        """, (cutoff,))
+        """,
+            (cutoff,),
+        )
         expired = cursor.fetchone()[0]
 
         return {
@@ -318,7 +336,7 @@ class EmbeddingCache:
             "by_model": by_model,
             "top_accessed": top_accessed,
             "expired": expired,
-            "ttl_days": self.ttl_days
+            "ttl_days": self.ttl_days,
         }
 
     def close(self):
diff --git a/src/skill_seekers/embedding/generator.py b/src/skill_seekers/embedding/generator.py
index e7e3528..c65f965 100644
--- a/src/skill_seekers/embedding/generator.py
+++ b/src/skill_seekers/embedding/generator.py
@@ -9,6 +9,7 @@ import numpy as np
 # OpenAI support
 try:
     from openai import OpenAI
+
     OPENAI_AVAILABLE = True
 except ImportError:
     OPENAI_AVAILABLE = False
@@ -16,6 +17,7 @@ except ImportError:
 # Sentence transformers support
 try:
     from sentence_transformers import SentenceTransformer
+
     SENTENCE_TRANSFORMERS_AVAILABLE = True
 except ImportError:
     SENTENCE_TRANSFORMERS_AVAILABLE = False
@@ -23,6 +25,7 @@ except ImportError:
 # Voyage AI support (recommended by Anthropic for embeddings)
 try:
     import voyageai
+
     VOYAGE_AVAILABLE = True
 except ImportError:
     VOYAGE_AVAILABLE = False
@@ -129,7 +132,7 @@ class EmbeddingGenerator:
         self,
         api_key: str | None = None,
         voyage_api_key: str | None = None,
-        cache_dir: str | None = None
+        cache_dir: str | None = None,
     ):
         """
         Initialize embedding generator.
@@ -162,8 +165,7 @@ class EmbeddingGenerator:
         """Get information about a model."""
         if model not in self.MODELS:
             raise ValueError(
-                f"Unknown model: {model}. "
-                f"Available models: {', '.join(self.MODELS.keys())}"
+                f"Unknown model: {model}. Available models: {', '.join(self.MODELS.keys())}"
             )
         return self.MODELS[model]
 
@@ -171,20 +173,19 @@ class EmbeddingGenerator:
         """List all available models."""
         models = []
         for name, info in self.MODELS.items():
-            models.append({
-                "name": name,
-                "provider": info["provider"],
-                "dimensions": info["dimensions"],
-                "max_tokens": info["max_tokens"],
-                "cost_per_million": info.get("cost_per_million", 0.0),
-            })
+            models.append(
+                {
+                    "name": name,
+                    "provider": info["provider"],
+                    "dimensions": info["dimensions"],
+                    "max_tokens": info["max_tokens"],
+                    "cost_per_million": info.get("cost_per_million", 0.0),
+                }
+            )
         return models
 
     def generate(
-        self,
-        text: str,
-        model: str = "text-embedding-3-small",
-        normalize: bool = True
+        self, text: str, model: str = "text-embedding-3-small", normalize: bool = True
     ) -> list[float]:
         """
         Generate embedding for a single text.
@@ -218,7 +219,7 @@ class EmbeddingGenerator:
         texts: list[str],
         model: str = "text-embedding-3-small",
         normalize: bool = True,
-        batch_size: int = 32
+        batch_size: int = 32,
     ) -> tuple[list[list[float]], int]:
         """
         Generate embeddings for multiple texts.
@@ -248,24 +249,18 @@ class EmbeddingGenerator:
         else:
             raise ValueError(f"Unsupported provider: {provider}")
 
-    def _generate_openai(
-        self, text: str, model: str, normalize: bool
-    ) -> list[float]:
+    def _generate_openai(self, text: str, model: str, normalize: bool) -> list[float]:
         """Generate embedding using OpenAI API."""
         if not OPENAI_AVAILABLE:
             raise ImportError(
-                "OpenAI is required for OpenAI embeddings. "
-                "Install with: pip install openai"
+                "OpenAI is required for OpenAI embeddings. Install with: pip install openai"
             )
 
         if not self.openai_client:
             raise ValueError("OpenAI API key not provided")
 
         try:
-            response = self.openai_client.embeddings.create(
-                input=text,
-                model=model
-            )
+            response = self.openai_client.embeddings.create(input=text, model=model)
             embedding = response.data[0].embedding
 
             if normalize:
@@ -281,8 +276,7 @@ class EmbeddingGenerator:
         """Generate embeddings using OpenAI API in batches."""
         if not OPENAI_AVAILABLE:
             raise ImportError(
-                "OpenAI is required for OpenAI embeddings. "
-                "Install with: pip install openai"
+                "OpenAI is required for OpenAI embeddings. Install with: pip install openai"
             )
 
         if not self.openai_client:
@@ -292,13 +286,10 @@ class EmbeddingGenerator:
 
         # Process in batches
         for i in range(0, len(texts), batch_size):
-            batch = texts[i:i + batch_size]
+            batch = texts[i : i + batch_size]
 
             try:
-                response = self.openai_client.embeddings.create(
-                    input=batch,
-                    model=model
-                )
+                response = self.openai_client.embeddings.create(input=batch, model=model)
 
                 batch_embeddings = [item.embedding for item in response.data]
 
@@ -313,24 +304,18 @@ class EmbeddingGenerator:
         dimensions = len(all_embeddings[0]) if all_embeddings else 0
         return all_embeddings, dimensions
 
-    def _generate_voyage(
-        self, text: str, model: str, normalize: bool
-    ) -> list[float]:
+    def _generate_voyage(self, text: str, model: str, normalize: bool) -> list[float]:
         """Generate embedding using Voyage AI API."""
         if not VOYAGE_AVAILABLE:
             raise ImportError(
-                "voyageai is required for Voyage AI embeddings. "
-                "Install with: pip install voyageai"
+                "voyageai is required for Voyage AI embeddings. Install with: pip install voyageai"
             )
 
         if not self.voyage_client:
             raise ValueError("Voyage API key not provided")
 
         try:
-            result = self.voyage_client.embed(
-                texts=[text],
-                model=model
-            )
+            result = self.voyage_client.embed(texts=[text], model=model)
             embedding = result.embeddings[0]
 
             if normalize:
@@ -346,8 +331,7 @@ class EmbeddingGenerator:
         """Generate embeddings using Voyage AI API in batches."""
         if not VOYAGE_AVAILABLE:
             raise ImportError(
-                "voyageai is required for Voyage AI embeddings. "
-                "Install with: pip install voyageai"
+                "voyageai is required for Voyage AI embeddings. Install with: pip install voyageai"
             )
 
         if not self.voyage_client:
@@ -357,13 +341,10 @@ class EmbeddingGenerator:
 
         # Process in batches (Voyage AI supports up to 128 texts per request)
         for i in range(0, len(texts), batch_size):
-            batch = texts[i:i + batch_size]
+            batch = texts[i : i + batch_size]
 
             try:
-                result = self.voyage_client.embed(
-                    texts=batch,
-                    model=model
-                )
+                result = self.voyage_client.embed(texts=batch, model=model)
 
                 batch_embeddings = result.embeddings
 
@@ -378,9 +359,7 @@ class EmbeddingGenerator:
         dimensions = len(all_embeddings[0]) if all_embeddings else 0
         return all_embeddings, dimensions
 
-    def _generate_sentence_transformer(
-        self, text: str, model: str, normalize: bool
-    ) -> list[float]:
+    def _generate_sentence_transformer(self, text: str, model: str, normalize: bool) -> list[float]:
         """Generate embedding using sentence-transformers."""
         if not SENTENCE_TRANSFORMERS_AVAILABLE:
             raise ImportError(
@@ -417,10 +396,7 @@ class EmbeddingGenerator:
 
         # Generate embeddings in batches
         embeddings = st_model.encode(
-            texts,
-            batch_size=batch_size,
-            normalize_embeddings=normalize,
-            show_progress_bar=False
+            texts, batch_size=batch_size, normalize_embeddings=normalize, show_progress_bar=False
         )
 
         dimensions = len(embeddings[0]) if len(embeddings) > 0 else 0
diff --git a/src/skill_seekers/embedding/models.py b/src/skill_seekers/embedding/models.py
index 0f91862..aa88fae 100644
--- a/src/skill_seekers/embedding/models.py
+++ b/src/skill_seekers/embedding/models.py
@@ -14,20 +14,14 @@ class EmbeddingRequest(BaseModel):
             "example": {
                 "text": "This is a test document about Python programming.",
                 "model": "text-embedding-3-small",
-                "normalize": True
+                "normalize": True,
             }
         }
     )
 
     text: str = Field(..., description="Text to generate embedding for")
-    model: str = Field(
-        default="text-embedding-3-small",
-        description="Embedding model to use"
-    )
-    normalize: bool = Field(
-        default=True,
-        description="Normalize embeddings to unit length"
-    )
+    model: str = Field(default="text-embedding-3-small", description="Embedding model to use")
+    normalize: bool = Field(default=True, description="Normalize embeddings to unit length")
 
 
 class BatchEmbeddingRequest(BaseModel):
@@ -39,27 +33,20 @@ class BatchEmbeddingRequest(BaseModel):
                 "texts": [
                     "First document about Python",
                     "Second document about JavaScript",
-                    "Third document about Rust"
+                    "Third document about Rust",
                 ],
                 "model": "text-embedding-3-small",
                 "normalize": True,
-                "batch_size": 32
+                "batch_size": 32,
             }
         }
     )
 
     texts: list[str] = Field(..., description="List of texts to embed")
-    model: str = Field(
-        default="text-embedding-3-small",
-        description="Embedding model to use"
-    )
-    normalize: bool = Field(
-        default=True,
-        description="Normalize embeddings to unit length"
-    )
+    model: str = Field(default="text-embedding-3-small", description="Embedding model to use")
+    normalize: bool = Field(default=True, description="Normalize embeddings to unit length")
     batch_size: int | None = Field(
-        default=32,
-        description="Batch size for processing (default: 32)"
+        default=32, description="Batch size for processing (default: 32)"
     )
 
 
@@ -69,10 +56,7 @@ class EmbeddingResponse(BaseModel):
     embedding: list[float] = Field(..., description="Generated embedding vector")
     model: str = Field(..., description="Model used for generation")
     dimensions: int = Field(..., description="Embedding dimensions")
-    cached: bool = Field(
-        default=False,
-        description="Whether embedding was retrieved from cache"
-    )
+    cached: bool = Field(default=False, description="Whether embedding was retrieved from cache")
 
 
 class BatchEmbeddingResponse(BaseModel):
@@ -82,10 +66,7 @@ class BatchEmbeddingResponse(BaseModel):
     model: str = Field(..., description="Model used for generation")
     dimensions: int = Field(..., description="Embedding dimensions")
     count: int = Field(..., description="Number of embeddings generated")
-    cached_count: int = Field(
-        default=0,
-        description="Number of embeddings retrieved from cache"
-    )
+    cached_count: int = Field(default=0, description="Number of embeddings retrieved from cache")
 
 
 class SkillEmbeddingRequest(BaseModel):
@@ -97,24 +78,15 @@ class SkillEmbeddingRequest(BaseModel):
                 "skill_path": "/path/to/skill/react",
                 "model": "text-embedding-3-small",
                 "chunk_size": 512,
-                "overlap": 50
+                "overlap": 50,
             }
         }
     )
 
     skill_path: str = Field(..., description="Path to skill directory")
-    model: str = Field(
-        default="text-embedding-3-small",
-        description="Embedding model to use"
-    )
-    chunk_size: int = Field(
-        default=512,
-        description="Chunk size for splitting documents (tokens)"
-    )
-    overlap: int = Field(
-        default=50,
-        description="Overlap between chunks (tokens)"
-    )
+    model: str = Field(default="text-embedding-3-small", description="Embedding model to use")
+    chunk_size: int = Field(default=512, description="Chunk size for splitting documents (tokens)")
+    overlap: int = Field(default=50, description="Overlap between chunks (tokens)")
 
 
 class SkillEmbeddingResponse(BaseModel):
@@ -124,10 +96,7 @@ class SkillEmbeddingResponse(BaseModel):
     total_chunks: int = Field(..., description="Total number of chunks embedded")
     model: str = Field(..., description="Model used for generation")
     dimensions: int = Field(..., description="Embedding dimensions")
-    metadata: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Skill metadata"
-    )
+    metadata: dict[str, Any] = Field(default_factory=dict, description="Skill metadata")
 
 
 class HealthResponse(BaseModel):
@@ -144,12 +113,13 @@ class ModelInfo(BaseModel):
     """Information about an embedding model."""
 
     name: str = Field(..., description="Model name")
-    provider: str = Field(..., description="Model provider (openai, anthropic, sentence-transformers)")
+    provider: str = Field(
+        ..., description="Model provider (openai, anthropic, sentence-transformers)"
+    )
     dimensions: int = Field(..., description="Embedding dimensions")
     max_tokens: int = Field(..., description="Maximum input tokens")
     cost_per_million: float | None = Field(
-        None,
-        description="Cost per million tokens (if applicable)"
+        None, description="Cost per million tokens (if applicable)"
     )
 
 
diff --git a/src/skill_seekers/embedding/server.py b/src/skill_seekers/embedding/server.py
index f01ad16..cb7fb6c 100644
--- a/src/skill_seekers/embedding/server.py
+++ b/src/skill_seekers/embedding/server.py
@@ -25,6 +25,7 @@ try:
     from fastapi import FastAPI, HTTPException, Query
     from fastapi.middleware.cors import CORSMiddleware
     import uvicorn
+
     FASTAPI_AVAILABLE = True
 except ImportError:
     FASTAPI_AVAILABLE = False
@@ -51,7 +52,7 @@ if FASTAPI_AVAILABLE:
         description="Generate embeddings for text and skill content",
         version="1.0.0",
         docs_url="/docs",
-        redoc_url="/redoc"
+        redoc_url="/redoc",
     )
 
     # Add CORS middleware
@@ -64,13 +65,14 @@ if FASTAPI_AVAILABLE:
     )
 
     # Initialize generator and cache
-    cache_dir = os.getenv("EMBEDDING_CACHE_DIR", os.path.expanduser("~/.cache/skill-seekers/embeddings"))
+    cache_dir = os.getenv(
+        "EMBEDDING_CACHE_DIR", os.path.expanduser("~/.cache/skill-seekers/embeddings")
+    )
     cache_db = os.path.join(cache_dir, "embeddings.db")
     cache_enabled = os.getenv("EMBEDDING_CACHE_ENABLED", "true").lower() == "true"
 
     generator = EmbeddingGenerator(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        voyage_api_key=os.getenv("VOYAGE_API_KEY")
+        api_key=os.getenv("OPENAI_API_KEY"), voyage_api_key=os.getenv("VOYAGE_API_KEY")
     )
     cache = EmbeddingCache(cache_db) if cache_enabled else None
 
@@ -81,7 +83,7 @@ if FASTAPI_AVAILABLE:
             "service": "Skill Seekers Embedding API",
             "version": "1.0.0",
             "docs": "/docs",
-            "health": "/health"
+            "health": "/health",
         }
 
     @app.get("/health", response_model=HealthResponse)
@@ -95,7 +97,7 @@ if FASTAPI_AVAILABLE:
             version="1.0.0",
             models=models,
             cache_enabled=cache_enabled,
-            cache_size=cache_size
+            cache_size=cache_size,
         )
 
     @app.get("/models", response_model=ModelsResponse)
@@ -109,15 +111,12 @@ if FASTAPI_AVAILABLE:
                 provider=m["provider"],
                 dimensions=m["dimensions"],
                 max_tokens=m["max_tokens"],
-                cost_per_million=m.get("cost_per_million")
+                cost_per_million=m.get("cost_per_million"),
             )
             for m in models_list
         ]
 
-        return ModelsResponse(
-            models=model_infos,
-            count=len(model_infos)
-        )
+        return ModelsResponse(models=model_infos, count=len(model_infos))
 
     @app.post("/embed", response_model=EmbeddingResponse)
     async def embed_text(request: EmbeddingRequest):
@@ -144,9 +143,7 @@ if FASTAPI_AVAILABLE:
             else:
                 # Generate embedding
                 embedding = generator.generate(
-                    request.text,
-                    model=request.model,
-                    normalize=request.normalize
+                    request.text, model=request.model, normalize=request.normalize
                 )
 
                 # Store in cache
@@ -154,10 +151,7 @@ if FASTAPI_AVAILABLE:
                     cache.set(hash_key, embedding, request.model)
 
             return EmbeddingResponse(
-                embedding=embedding,
-                model=request.model,
-                dimensions=len(embedding),
-                cached=cached
+                embedding=embedding, model=request.model, dimensions=len(embedding), cached=cached
             )
 
         except Exception as e:
@@ -202,11 +196,13 @@ if FASTAPI_AVAILABLE:
                     texts_to_generate,
                     model=request.model,
                     normalize=request.normalize,
-                    batch_size=request.batch_size
+                    batch_size=request.batch_size,
                 )
 
                 # Fill in placeholders and cache
-                for idx, text, embedding in zip(text_indices, texts_to_generate, generated_embeddings, strict=False):
+                for idx, text, embedding in zip(
+                    text_indices, texts_to_generate, generated_embeddings, strict=False
+                ):
                     embeddings[idx] = embedding
 
                     if cache:
@@ -220,7 +216,7 @@ if FASTAPI_AVAILABLE:
                 model=request.model,
                 dimensions=dimensions,
                 count=len(embeddings),
-                cached_count=cached_count
+                cached_count=cached_count,
             )
 
         except Exception as e:
@@ -244,12 +240,16 @@ if FASTAPI_AVAILABLE:
             skill_path = Path(request.skill_path)
 
             if not skill_path.exists():
-                raise HTTPException(status_code=404, detail=f"Skill path not found: {request.skill_path}")
+                raise HTTPException(
+                    status_code=404, detail=f"Skill path not found: {request.skill_path}"
+                )
 
             # Read SKILL.md
             skill_md = skill_path / "SKILL.md"
             if not skill_md.exists():
-                raise HTTPException(status_code=404, detail=f"SKILL.md not found in {request.skill_path}")
+                raise HTTPException(
+                    status_code=404, detail=f"SKILL.md not found in {request.skill_path}"
+                )
 
             skill_content = skill_md.read_text()
 
@@ -262,10 +262,7 @@ if FASTAPI_AVAILABLE:
 
             # Generate embeddings for chunks
             embeddings, dimensions = generator.generate_batch(
-                chunks,
-                model=request.model,
-                normalize=True,
-                batch_size=32
+                chunks, model=request.model, normalize=True, batch_size=32
             )
 
             # TODO: Store embeddings in vector database
@@ -279,8 +276,8 @@ if FASTAPI_AVAILABLE:
                 metadata={
                     "skill_path": str(skill_path),
                     "chunks": len(chunks),
-                    "content_length": len(skill_content)
-                }
+                    "content_length": len(skill_content),
+                },
             )
 
         except HTTPException:
@@ -298,7 +295,7 @@ if FASTAPI_AVAILABLE:
 
     @app.post("/cache/clear", response_model=dict)
     async def clear_cache(
-        model: str | None = Query(None, description="Model to clear (all if not specified)")
+        model: str | None = Query(None, description="Model to clear (all if not specified)"),
     ):
         """Clear cache entries."""
         if not cache:
@@ -306,11 +303,7 @@ if FASTAPI_AVAILABLE:
 
         deleted = cache.clear(model=model)
 
-        return {
-            "status": "ok",
-            "deleted": deleted,
-            "model": model or "all"
-        }
+        return {"status": "ok", "deleted": deleted, "model": model or "all"}
 
     @app.post("/cache/clear-expired", response_model=dict)
     async def clear_expired():
@@ -320,10 +313,7 @@ if FASTAPI_AVAILABLE:
 
         deleted = cache.clear_expired()
 
-        return {
-            "status": "ok",
-            "deleted": deleted
-        }
+        return {"status": "ok", "deleted": deleted}
 
 else:
     print("Error: FastAPI not available. Install with: pip install fastapi uvicorn")
@@ -348,12 +338,7 @@ def main():
     if cache_enabled:
         print(f"💾 Cache database: {cache_db}")
 
-    uvicorn.run(
-        "skill_seekers.embedding.server:app",
-        host=host,
-        port=port,
-        reload=reload
-    )
+    uvicorn.run("skill_seekers.embedding.server:app", host=host, port=port, reload=reload)
 
 
 if __name__ == "__main__":
diff --git a/src/skill_seekers/mcp/tools/config_tools.py b/src/skill_seekers/mcp/tools/config_tools.py
index 7495275..67d363d 100644
--- a/src/skill_seekers/mcp/tools/config_tools.py
+++ b/src/skill_seekers/mcp/tools/config_tools.py
@@ -69,15 +69,17 @@ async def generate_config(args: dict) -> list[TextContent]:
     config = {
         "name": name,
         "description": description,
-        "sources": [{
-            "type": "documentation",
-            "base_url": url,
-            "selectors": {"main_content": "article", "title": "h1", "code_blocks": "pre code"},
-            "url_patterns": {"include": [], "exclude": []},
-            "categories": {},
-            "rate_limit": rate_limit,
-            "max_pages": max_pages,
-        }],
+        "sources": [
+            {
+                "type": "documentation",
+                "base_url": url,
+                "selectors": {"main_content": "article", "title": "h1", "code_blocks": "pre code"},
+                "url_patterns": {"include": [], "exclude": []},
+                "categories": {},
+                "rate_limit": rate_limit,
+                "max_pages": max_pages,
+            }
+        ],
     }
 
     # Save to configs directory
diff --git a/src/skill_seekers/sync/__init__.py b/src/skill_seekers/sync/__init__.py
index f237ba6..9edc69b 100644
--- a/src/skill_seekers/sync/__init__.py
+++ b/src/skill_seekers/sync/__init__.py
@@ -32,9 +32,9 @@ from .detector import ChangeDetector
 from .models import SyncConfig, ChangeReport, PageChange
 
 __all__ = [
-    'SyncMonitor',
-    'ChangeDetector',
-    'SyncConfig',
-    'ChangeReport',
-    'PageChange',
+    "SyncMonitor",
+    "ChangeDetector",
+    "SyncConfig",
+    "ChangeReport",
+    "PageChange",
 ]
diff --git a/src/skill_seekers/sync/detector.py b/src/skill_seekers/sync/detector.py
index 381850c..4eb91d2 100644
--- a/src/skill_seekers/sync/detector.py
+++ b/src/skill_seekers/sync/detector.py
@@ -55,7 +55,7 @@ class ChangeDetector:
         Returns:
             Hexadecimal hash string
         """
-        return hashlib.sha256(content.encode('utf-8')).hexdigest()
+        return hashlib.sha256(content.encode("utf-8")).hexdigest()
 
     def fetch_page(self, url: str) -> tuple[str, dict[str, str]]:
         """
@@ -72,17 +72,15 @@ class ChangeDetector:
             requests.RequestException: If fetch fails
         """
         response = requests.get(
-            url,
-            timeout=self.timeout,
-            headers={'User-Agent': 'SkillSeekers-Sync/1.0'}
+            url, timeout=self.timeout, headers={"User-Agent": "SkillSeekers-Sync/1.0"}
         )
         response.raise_for_status()
 
         metadata = {
-            'last-modified': response.headers.get('Last-Modified'),
-            'etag': response.headers.get('ETag'),
-            'content-type': response.headers.get('Content-Type'),
-            'content-length': response.headers.get('Content-Length'),
+            "last-modified": response.headers.get("Last-Modified"),
+            "etag": response.headers.get("ETag"),
+            "content-type": response.headers.get("Content-Type"),
+            "content-length": response.headers.get("Content-Length"),
         }
 
         return response.text, metadata
@@ -92,7 +90,7 @@ class ChangeDetector:
         url: str,
         old_hash: str | None = None,
         generate_diff: bool = False,
-        old_content: str | None = None
+        old_content: str | None = None,
     ) -> PageChange:
         """
         Check if page has changed.
@@ -132,7 +130,7 @@ class ChangeDetector:
                 old_hash=old_hash,
                 new_hash=new_hash,
                 diff=diff,
-                detected_at=datetime.utcnow()
+                detected_at=datetime.utcnow(),
             )
 
         except requests.RequestException:
@@ -142,14 +140,11 @@ class ChangeDetector:
                 change_type=ChangeType.DELETED,
                 old_hash=old_hash,
                 new_hash=None,
-                detected_at=datetime.utcnow()
+                detected_at=datetime.utcnow(),
             )
 
     def check_pages(
-        self,
-        urls: list[str],
-        previous_hashes: dict[str, str],
-        generate_diffs: bool = False
+        self, urls: list[str], previous_hashes: dict[str, str], generate_diffs: bool = False
     ) -> ChangeReport:
         """
         Check multiple pages for changes.
@@ -185,13 +180,15 @@ class ChangeDetector:
         # Check for deleted pages (in previous state but not in current)
         for url, old_hash in previous_hashes.items():
             if url not in checked_urls:
-                deleted.append(PageChange(
-                    url=url,
-                    change_type=ChangeType.DELETED,
-                    old_hash=old_hash,
-                    new_hash=None,
-                    detected_at=datetime.utcnow()
-                ))
+                deleted.append(
+                    PageChange(
+                        url=url,
+                        change_type=ChangeType.DELETED,
+                        old_hash=old_hash,
+                        new_hash=None,
+                        detected_at=datetime.utcnow(),
+                    )
+                )
 
         return ChangeReport(
             skill_name="unknown",  # To be set by caller
@@ -200,7 +197,7 @@ class ChangeDetector:
             modified=modified,
             deleted=deleted,
             unchanged=unchanged_count,
-            checked_at=datetime.utcnow()
+            checked_at=datetime.utcnow(),
         )
 
     def generate_diff(self, old_content: str, new_content: str) -> str:
@@ -217,15 +214,9 @@ class ChangeDetector:
         old_lines = old_content.splitlines(keepends=True)
         new_lines = new_content.splitlines(keepends=True)
 
-        diff = difflib.unified_diff(
-            old_lines,
-            new_lines,
-            fromfile='old',
-            tofile='new',
-            lineterm=''
-        )
+        diff = difflib.unified_diff(old_lines, new_lines, fromfile="old", tofile="new", lineterm="")
 
-        return ''.join(diff)
+        return "".join(diff)
 
     def generate_summary_diff(self, old_content: str, new_content: str) -> str:
         """
@@ -244,16 +235,15 @@ class ChangeDetector:
         diff = difflib.unified_diff(old_lines, new_lines)
         diff_lines = list(diff)
 
-        added = sum(1 for line in diff_lines if line.startswith('+') and not line.startswith('+++'))
-        removed = sum(1 for line in diff_lines if line.startswith('-') and not line.startswith('---'))
+        added = sum(1 for line in diff_lines if line.startswith("+") and not line.startswith("+++"))
+        removed = sum(
+            1 for line in diff_lines if line.startswith("-") and not line.startswith("---")
+        )
 
         return f"+{added} -{removed} lines"
 
     def check_header_changes(
-        self,
-        url: str,
-        old_modified: str | None = None,
-        old_etag: str | None = None
+        self, url: str, old_modified: str | None = None, old_etag: str | None = None
     ) -> bool:
         """
         Quick check using HTTP headers (no content download).
@@ -269,14 +259,12 @@ class ChangeDetector:
         try:
             # Use HEAD request for efficiency
             response = requests.head(
-                url,
-                timeout=self.timeout,
-                headers={'User-Agent': 'SkillSeekers-Sync/1.0'}
+                url, timeout=self.timeout, headers={"User-Agent": "SkillSeekers-Sync/1.0"}
             )
             response.raise_for_status()
 
-            new_modified = response.headers.get('Last-Modified')
-            new_etag = response.headers.get('ETag')
+            new_modified = response.headers.get("Last-Modified")
+            new_etag = response.headers.get("ETag")
 
             # Check if headers indicate change
             if old_modified and new_modified and old_modified != new_modified:
@@ -289,9 +277,7 @@ class ChangeDetector:
             return True
 
     def batch_check_headers(
-        self,
-        urls: list[str],
-        previous_metadata: dict[str, dict[str, str]]
+        self, urls: list[str], previous_metadata: dict[str, dict[str, str]]
     ) -> list[str]:
         """
         Batch check URLs using headers only.
@@ -307,8 +293,8 @@ class ChangeDetector:
 
         for url in urls:
             old_meta = previous_metadata.get(url, {})
-            old_modified = old_meta.get('last-modified')
-            old_etag = old_meta.get('etag')
+            old_modified = old_meta.get("last-modified")
+            old_etag = old_meta.get("etag")
 
             if self.check_header_changes(url, old_modified, old_etag):
                 changed_urls.append(url)
diff --git a/src/skill_seekers/sync/models.py b/src/skill_seekers/sync/models.py
index bacf6b1..e67943c 100644
--- a/src/skill_seekers/sync/models.py
+++ b/src/skill_seekers/sync/models.py
@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field
 
 class ChangeType(str, Enum):
     """Type of change detected."""
+
     ADDED = "added"
     MODIFIED = "modified"
     DELETED = "deleted"
@@ -25,8 +26,7 @@ class PageChange(BaseModel):
     new_hash: str | None = Field(None, description="New content hash")
     diff: str | None = Field(None, description="Content diff (if available)")
     detected_at: datetime = Field(
-        default_factory=datetime.utcnow,
-        description="When change was detected"
+        default_factory=datetime.utcnow, description="When change was detected"
     )
 
     class Config:
@@ -37,7 +37,7 @@ class PageChange(BaseModel):
                 "old_hash": "abc123",
                 "new_hash": "def456",
                 "diff": "@@ -10,3 +10,4 @@\n+New content here",
-                "detected_at": "2024-01-15T10:30:00Z"
+                "detected_at": "2024-01-15T10:30:00Z",
             }
         }
 
@@ -52,8 +52,7 @@ class ChangeReport(BaseModel):
     deleted: list[PageChange] = Field(default_factory=list, description="Deleted pages")
     unchanged: int = Field(0, description="Number of unchanged pages")
     checked_at: datetime = Field(
-        default_factory=datetime.utcnow,
-        description="When check was performed"
+        default_factory=datetime.utcnow, description="When check was performed"
     )
 
     @property
@@ -72,34 +71,19 @@ class SyncConfig(BaseModel):
 
     skill_config: str = Field(..., description="Path to skill config file")
     check_interval: int = Field(
-        default=3600,
-        description="Check interval in seconds (default: 1 hour)"
+        default=3600, description="Check interval in seconds (default: 1 hour)"
     )
     enabled: bool = Field(default=True, description="Whether sync is enabled")
-    auto_update: bool = Field(
-        default=False,
-        description="Automatically rebuild skill on changes"
-    )
-    notify_on_change: bool = Field(
-        default=True,
-        description="Send notifications on changes"
-    )
+    auto_update: bool = Field(default=False, description="Automatically rebuild skill on changes")
+    notify_on_change: bool = Field(default=True, description="Send notifications on changes")
     notification_channels: list[str] = Field(
-        default_factory=list,
-        description="Notification channels (email, slack, webhook)"
-    )
-    webhook_url: str | None = Field(
-        None,
-        description="Webhook URL for change notifications"
+        default_factory=list, description="Notification channels (email, slack, webhook)"
     )
+    webhook_url: str | None = Field(None, description="Webhook URL for change notifications")
     email_recipients: list[str] = Field(
-        default_factory=list,
-        description="Email recipients for notifications"
-    )
-    slack_webhook: str | None = Field(
-        None,
-        description="Slack webhook URL"
+        default_factory=list, description="Email recipients for notifications"
     )
+    slack_webhook: str | None = Field(None, description="Slack webhook URL")
 
     class Config:
         json_schema_extra = {
@@ -111,7 +95,7 @@ class SyncConfig(BaseModel):
                 "notify_on_change": True,
                 "notification_channels": ["slack", "webhook"],
                 "webhook_url": "https://example.com/webhook",
-                "slack_webhook": "https://hooks.slack.com/services/..."
+                "slack_webhook": "https://hooks.slack.com/services/...",
             }
         }
 
@@ -125,8 +109,7 @@ class SyncState(BaseModel):
     total_checks: int = Field(default=0, description="Total checks performed")
     total_changes: int = Field(default=0, description="Total changes detected")
     page_hashes: dict[str, str] = Field(
-        default_factory=dict,
-        description="URL -> content hash mapping"
+        default_factory=dict, description="URL -> content hash mapping"
     )
     status: str = Field(default="idle", description="Current status")
     error: str | None = Field(None, description="Last error message")
@@ -137,15 +120,9 @@ class WebhookPayload(BaseModel):
 
     event: str = Field(..., description="Event type (change_detected, sync_complete)")
     skill_name: str = Field(..., description="Skill name")
-    timestamp: datetime = Field(
-        default_factory=datetime.utcnow,
-        description="Event timestamp"
-    )
+    timestamp: datetime = Field(default_factory=datetime.utcnow, description="Event timestamp")
     changes: ChangeReport | None = Field(None, description="Change report")
-    metadata: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Additional metadata"
-    )
+    metadata: dict[str, Any] = Field(default_factory=dict, description="Additional metadata")
 
     class Config:
         json_schema_extra = {
@@ -157,8 +134,8 @@ class WebhookPayload(BaseModel):
                     "total_pages": 150,
                     "added": [],
                     "modified": [{"url": "https://react.dev/learn"}],
-                    "deleted": []
+                    "deleted": [],
                 },
-                "metadata": {"source": "periodic_check"}
+                "metadata": {"source": "periodic_check"},
             }
         }
diff --git a/src/skill_seekers/sync/monitor.py b/src/skill_seekers/sync/monitor.py
index 26aea11..f2c3f15 100644
--- a/src/skill_seekers/sync/monitor.py
+++ b/src/skill_seekers/sync/monitor.py
@@ -51,7 +51,7 @@ class SyncMonitor:
         check_interval: int = 3600,
         auto_update: bool = False,
         state_file: str | None = None,
-        on_change: Callable[[ChangeReport], None] | None = None
+        on_change: Callable[[ChangeReport], None] | None = None,
     ):
         """
         Initialize sync monitor.
@@ -72,7 +72,7 @@ class SyncMonitor:
         with open(self.config_path) as f:
             self.skill_config = json.load(f)
 
-        self.skill_name = self.skill_config.get('name', 'unknown')
+        self.skill_name = self.skill_config.get("name", "unknown")
 
         # State file
         if state_file:
@@ -97,10 +97,10 @@ class SyncMonitor:
             with open(self.state_file) as f:
                 data = json.load(f)
                 # Convert datetime strings back
-                if data.get('last_check'):
-                    data['last_check'] = datetime.fromisoformat(data['last_check'])
-                if data.get('last_change'):
-                    data['last_change'] = datetime.fromisoformat(data['last_change'])
+                if data.get("last_check"):
+                    data["last_check"] = datetime.fromisoformat(data["last_check"])
+                if data.get("last_change"):
+                    data["last_change"] = datetime.fromisoformat(data["last_change"])
                 return SyncState(**data)
         else:
             return SyncState(skill_name=self.skill_name)
@@ -109,12 +109,12 @@ class SyncMonitor:
         """Save current state to file."""
         # Convert datetime to ISO format
         data = self.state.dict()
-        if data.get('last_check'):
-            data['last_check'] = data['last_check'].isoformat()
-        if data.get('last_change'):
-            data['last_change'] = data['last_change'].isoformat()
+        if data.get("last_check"):
+            data["last_check"] = data["last_check"].isoformat()
+        if data.get("last_change"):
+            data["last_change"] = data["last_change"].isoformat()
 
-        with open(self.state_file, 'w') as f:
+        with open(self.state_file, "w") as f:
             json.dump(data, f, indent=2)
 
     def check_now(self, generate_diffs: bool = False) -> ChangeReport:
@@ -132,7 +132,7 @@ class SyncMonitor:
 
         try:
             # Get URLs to check from config
-            base_url = self.skill_config.get('base_url')
+            base_url = self.skill_config.get("base_url")
             # TODO: In real implementation, get actual URLs from scraper
 
             # For now, simulate with base URL only
@@ -140,9 +140,7 @@ class SyncMonitor:
 
             # Check for changes
             report = self.detector.check_pages(
-                urls=urls,
-                previous_hashes=self.state.page_hashes,
-                generate_diffs=generate_diffs
+                urls=urls, previous_hashes=self.state.page_hashes, generate_diffs=generate_diffs
             )
             report.skill_name = self.skill_name
 
@@ -192,7 +190,7 @@ class SyncMonitor:
             event="change_detected",
             skill_name=self.skill_name,
             changes=report,
-            metadata={"auto_update": self.auto_update}
+            metadata={"auto_update": self.auto_update},
         )
 
         self.notifier.send(payload)
@@ -214,9 +212,7 @@ class SyncMonitor:
         self._running = True
 
         # Schedule checks
-        schedule.every(self.check_interval).seconds.do(
-            lambda: self.check_now()
-        )
+        schedule.every(self.check_interval).seconds.do(lambda: self.check_now())
 
         # Run in thread
         def run_schedule():
diff --git a/src/skill_seekers/sync/notifier.py b/src/skill_seekers/sync/notifier.py
index c581a8c..7157267 100644
--- a/src/skill_seekers/sync/notifier.py
+++ b/src/skill_seekers/sync/notifier.py
@@ -34,7 +34,7 @@ class Notifier:
         webhook_url: str | None = None,
         slack_webhook: str | None = None,
         email_recipients: list[str] | None = None,
-        console: bool = True
+        console: bool = True,
     ):
         """
         Initialize notifier.
@@ -45,8 +45,8 @@ class Notifier:
             email_recipients: List of email recipients
             console: Whether to print to console
         """
-        self.webhook_url = webhook_url or os.getenv('SYNC_WEBHOOK_URL')
-        self.slack_webhook = slack_webhook or os.getenv('SLACK_WEBHOOK_URL')
+        self.webhook_url = webhook_url or os.getenv("SYNC_WEBHOOK_URL")
+        self.slack_webhook = slack_webhook or os.getenv("SLACK_WEBHOOK_URL")
         self.email_recipients = email_recipients or []
         self.console = console
 
@@ -92,8 +92,8 @@ class Notifier:
             response = requests.post(
                 self.webhook_url,
                 json=payload.dict(),
-                headers={'Content-Type': 'application/json'},
-                timeout=10
+                headers={"Content-Type": "application/json"},
+                timeout=10,
             )
             response.raise_for_status()
             print(f"✅ Webhook notification sent to {self.webhook_url}")
@@ -124,14 +124,10 @@ class Notifier:
             slack_payload = {
                 "text": text,
                 "username": "Skill Seekers Sync",
-                "icon_emoji": ":books:"
+                "icon_emoji": ":books:",
             }
 
-            response = requests.post(
-                self.slack_webhook,
-                json=slack_payload,
-                timeout=10
-            )
+            response = requests.post(self.slack_webhook, json=slack_payload, timeout=10)
             response.raise_for_status()
             print("✅ Slack notification sent")
         except Exception as e:
diff --git a/tests/test_adaptor_benchmarks.py b/tests/test_adaptor_benchmarks.py
index bd3f362..fe07fee 100644
--- a/tests/test_adaptor_benchmarks.py
+++ b/tests/test_adaptor_benchmarks.py
@@ -85,9 +85,17 @@ class TestAdaptorBenchmarks(unittest.TestCase):
 
         # Platforms to benchmark
         platforms = [
-            "claude", "gemini", "openai", "markdown",  # IDE integrations
-            "langchain", "llama-index", "haystack",     # RAG frameworks
-            "weaviate", "chroma", "faiss", "qdrant"     # Vector DBs
+            "claude",
+            "gemini",
+            "openai",
+            "markdown",  # IDE integrations
+            "langchain",
+            "llama-index",
+            "haystack",  # RAG frameworks
+            "weaviate",
+            "chroma",
+            "faiss",
+            "qdrant",  # Vector DBs
         ]
 
         results = {}
@@ -115,20 +123,19 @@ class TestAdaptorBenchmarks(unittest.TestCase):
             min_time = min(times)
             max_time = max(times)
 
-            results[platform] = {
-                "avg": avg_time,
-                "min": min_time,
-                "max": max_time
-            }
+            results[platform] = {"avg": avg_time, "min": min_time, "max": max_time}
 
-            print(f"{platform:15} - Avg: {avg_time*1000:6.2f}ms | "
-                  f"Min: {min_time*1000:6.2f}ms | Max: {max_time*1000:6.2f}ms")
+            print(
+                f"{platform:15} - Avg: {avg_time * 1000:6.2f}ms | "
+                f"Min: {min_time * 1000:6.2f}ms | Max: {max_time * 1000:6.2f}ms"
+            )
 
         # Performance assertions (should complete in reasonable time)
         for platform, metrics in results.items():
             self.assertLess(
-                metrics["avg"], 0.5,  # Should average < 500ms
-                f"{platform} format_skill_md too slow: {metrics['avg']*1000:.2f}ms"
+                metrics["avg"],
+                0.5,  # Should average < 500ms
+                f"{platform} format_skill_md too slow: {metrics['avg'] * 1000:.2f}ms",
             )
 
     def test_benchmark_package_operations(self):
@@ -158,12 +165,9 @@ class TestAdaptorBenchmarks(unittest.TestCase):
             # Get file size
             file_size_kb = package_path.stat().st_size / 1024
 
-            results[platform] = {
-                "time": elapsed,
-                "size_kb": file_size_kb
-            }
+            results[platform] = {"time": elapsed, "size_kb": file_size_kb}
 
-            print(f"{platform:15} - Time: {elapsed*1000:7.2f}ms | Size: {file_size_kb:7.1f} KB")
+            print(f"{platform:15} - Time: {elapsed * 1000:7.2f}ms | Size: {file_size_kb:7.1f} KB")
 
             # Validate output
             self.assertTrue(package_path.exists())
@@ -171,12 +175,14 @@ class TestAdaptorBenchmarks(unittest.TestCase):
         # Performance assertions
         for platform, metrics in results.items():
             self.assertLess(
-                metrics["time"], 1.0,  # Should complete < 1 second
-                f"{platform} packaging too slow: {metrics['time']*1000:.2f}ms"
+                metrics["time"],
+                1.0,  # Should complete < 1 second
+                f"{platform} packaging too slow: {metrics['time'] * 1000:.2f}ms",
             )
             self.assertLess(
-                metrics["size_kb"], 1000,  # Should be < 1MB for 10 refs
-                f"{platform} package too large: {metrics['size_kb']:.1f}KB"
+                metrics["size_kb"],
+                1000,  # Should be < 1MB for 10 refs
+                f"{platform} package too large: {metrics['size_kb']:.1f}KB",
             )
 
     def test_benchmark_scaling_with_reference_count(self):
@@ -210,14 +216,18 @@ class TestAdaptorBenchmarks(unittest.TestCase):
             json.loads(formatted)
             size_kb = len(formatted) / 1024
 
-            results.append({
-                "count": ref_count,
-                "time": elapsed,
-                "time_per_ref": time_per_ref,
-                "size_kb": size_kb
-            })
+            results.append(
+                {
+                    "count": ref_count,
+                    "time": elapsed,
+                    "time_per_ref": time_per_ref,
+                    "size_kb": size_kb,
+                }
+            )
 
-            print(f"{ref_count:4} | {elapsed*1000:10.2f} | {time_per_ref*1000:10.3f} | {size_kb:10.1f}")
+            print(
+                f"{ref_count:4} | {elapsed * 1000:10.2f} | {time_per_ref * 1000:10.3f} | {size_kb:10.1f}"
+            )
 
         # Analyze scaling behavior
         # Time per ref should not increase significantly (linear scaling)
@@ -230,10 +240,7 @@ class TestAdaptorBenchmarks(unittest.TestCase):
         print(f"(Time per ref at 50 refs / Time per ref at 1 ref)")
 
         # Assert linear or sub-linear scaling (not exponential)
-        self.assertLess(
-            scaling_factor, 3.0,
-            f"Non-linear scaling detected: {scaling_factor:.2f}x"
-        )
+        self.assertLess(scaling_factor, 3.0, f"Non-linear scaling detected: {scaling_factor:.2f}x")
 
     def test_benchmark_json_vs_zip_size_comparison(self):
         """Compare output sizes: JSON vs ZIP/tar.gz"""
@@ -263,16 +270,15 @@ class TestAdaptorBenchmarks(unittest.TestCase):
 
             size_kb = package_path.stat().st_size / 1024
 
-            results[platform] = {
-                "format": format_name,
-                "size_kb": size_kb
-            }
+            results[platform] = {"format": format_name, "size_kb": size_kb}
 
             print(f"{platform:15} | {format_name:8} | {size_kb:10.1f}")
 
         # Analyze results
         json_sizes = [v["size_kb"] for k, v in results.items() if v["format"] == "JSON"]
-        compressed_sizes = [v["size_kb"] for k, v in results.items() if v["format"] in ["ZIP", "tar.gz"]]
+        compressed_sizes = [
+            v["size_kb"] for k, v in results.items() if v["format"] in ["ZIP", "tar.gz"]
+        ]
 
         if json_sizes and compressed_sizes:
             avg_json = sum(json_sizes) / len(json_sizes)
@@ -280,7 +286,7 @@ class TestAdaptorBenchmarks(unittest.TestCase):
 
             print(f"\nAverage JSON size: {avg_json:.1f} KB")
             print(f"Average compressed size: {avg_compressed:.1f} KB")
-            print(f"Compression ratio: {avg_json/avg_compressed:.2f}x")
+            print(f"Compression ratio: {avg_json / avg_compressed:.2f}x")
 
     def test_benchmark_metadata_overhead(self):
         """Measure metadata processing overhead"""
@@ -299,7 +305,7 @@ class TestAdaptorBenchmarks(unittest.TestCase):
             description="A comprehensive test skill for benchmarking purposes",
             version="2.5.0",
             author="Benchmark Suite",
-            tags=["test", "benchmark", "performance", "validation", "quality"]
+            tags=["test", "benchmark", "performance", "validation", "quality"],
         )
 
         adaptor = get_adaptor("langchain")
@@ -326,15 +332,12 @@ class TestAdaptorBenchmarks(unittest.TestCase):
         overhead = avg_rich - avg_minimal
         overhead_pct = (overhead / avg_minimal) * 100
 
-        print(f"\nMinimal metadata: {avg_minimal*1000:.2f}ms")
-        print(f"Rich metadata:    {avg_rich*1000:.2f}ms")
-        print(f"Overhead:         {overhead*1000:.2f}ms ({overhead_pct:.1f}%)")
+        print(f"\nMinimal metadata: {avg_minimal * 1000:.2f}ms")
+        print(f"Rich metadata:    {avg_rich * 1000:.2f}ms")
+        print(f"Overhead:         {overhead * 1000:.2f}ms ({overhead_pct:.1f}%)")
 
         # Overhead should be negligible (< 10%)
-        self.assertLess(
-            overhead_pct, 10.0,
-            f"Metadata overhead too high: {overhead_pct:.1f}%"
-        )
+        self.assertLess(overhead_pct, 10.0, f"Metadata overhead too high: {overhead_pct:.1f}%")
 
     def test_benchmark_empty_vs_full_skill(self):
         """Compare performance: empty skill vs full skill"""
@@ -360,9 +363,9 @@ class TestAdaptorBenchmarks(unittest.TestCase):
         adaptor.format_skill_md(full_dir, metadata)
         full_time = time.perf_counter() - start
 
-        print(f"\nEmpty skill: {empty_time*1000:.2f}ms")
-        print(f"Full skill (50 refs): {full_time*1000:.2f}ms")
-        print(f"Ratio: {full_time/empty_time:.1f}x")
+        print(f"\nEmpty skill: {empty_time * 1000:.2f}ms")
+        print(f"Full skill (50 refs): {full_time * 1000:.2f}ms")
+        print(f"Ratio: {full_time / empty_time:.1f}x")
 
         # Empty should be very fast
         self.assertLess(empty_time, 0.01, "Empty skill processing too slow")
diff --git a/tests/test_adaptors/test_adaptors_e2e.py b/tests/test_adaptors/test_adaptors_e2e.py
index 619a604..62f441d 100644
--- a/tests/test_adaptors/test_adaptors_e2e.py
+++ b/tests/test_adaptors/test_adaptors_e2e.py
@@ -662,8 +662,13 @@ export default {
     def test_e2e_all_rag_adaptors_from_same_skill(self):
         """Test all 7 RAG adaptors can package the same skill"""
         rag_platforms = [
-            "langchain", "llama-index", "haystack",
-            "weaviate", "chroma", "faiss", "qdrant"
+            "langchain",
+            "llama-index",
+            "haystack",
+            "weaviate",
+            "chroma",
+            "faiss",
+            "qdrant",
         ]
         packages = {}
 
@@ -674,15 +679,11 @@ export default {
             package_path = adaptor.package(self.skill_dir, self.output_dir)
 
             # Verify package was created
-            self.assertTrue(
-                package_path.exists(),
-                f"Package not created for {platform}"
-            )
+            self.assertTrue(package_path.exists(), f"Package not created for {platform}")
 
             # Verify it's a JSON file
             self.assertTrue(
-                str(package_path).endswith(".json"),
-                f"{platform} should produce JSON file"
+                str(package_path).endswith(".json"), f"{platform} should produce JSON file"
             )
 
             # Store for later verification
@@ -696,10 +697,7 @@ export default {
             with open(path) as f:
                 data = json.load(f)
                 # Should be valid JSON (dict or list)
-                self.assertIsInstance(
-                    data, (dict, list),
-                    f"{platform} should produce valid JSON"
-                )
+                self.assertIsInstance(data, (dict, list), f"{platform} should produce valid JSON")
 
     def test_e2e_rag_adaptors_preserve_metadata(self):
         """Test that metadata is preserved across RAG adaptors"""
@@ -708,7 +706,7 @@ export default {
             description="Vue.js framework skill",
             version="2.0.0",
             author="Test Author",
-            tags=["vue", "javascript", "frontend"]
+            tags=["vue", "javascript", "frontend"],
         )
 
         # Test subset of platforms (representative sample)
@@ -758,33 +756,30 @@ export default {
         # Define expected structure for each platform
         validations = {
             "langchain": lambda d: (
-                isinstance(d, list) and
-                all("page_content" in item and "metadata" in item for item in d)
+                isinstance(d, list)
+                and all("page_content" in item and "metadata" in item for item in d)
             ),
             "llama-index": lambda d: (
-                isinstance(d, list) and
-                all("text" in item and "metadata" in item for item in d)
+                isinstance(d, list) and all("text" in item and "metadata" in item for item in d)
             ),
             "haystack": lambda d: (
-                isinstance(d, list) and
-                all("content" in item and "meta" in item for item in d)
+                isinstance(d, list) and all("content" in item and "meta" in item for item in d)
             ),
             "weaviate": lambda d: (
-                isinstance(d, dict) and
-                "schema" in d and "objects" in d and "class_name" in d
+                isinstance(d, dict) and "schema" in d and "objects" in d and "class_name" in d
             ),
             "chroma": lambda d: (
-                isinstance(d, dict) and
-                "documents" in d and "metadatas" in d and "ids" in d and
-                "collection_name" in d
+                isinstance(d, dict)
+                and "documents" in d
+                and "metadatas" in d
+                and "ids" in d
+                and "collection_name" in d
             ),
             "faiss": lambda d: (
-                isinstance(d, dict) and
-                "documents" in d and "metadatas" in d and "ids" in d
+                isinstance(d, dict) and "documents" in d and "metadatas" in d and "ids" in d
             ),
             "qdrant": lambda d: (
-                isinstance(d, dict) and
-                "collection_name" in d and "points" in d and "config" in d
+                isinstance(d, dict) and "collection_name" in d and "points" in d and "config" in d
             ),
         }
 
@@ -795,8 +790,7 @@ export default {
 
             # Validate structure
             self.assertTrue(
-                validate_func(data),
-                f"{platform} validation failed: incorrect JSON structure"
+                validate_func(data), f"{platform} validation failed: incorrect JSON structure"
             )
 
     def test_e2e_rag_empty_skill_handling(self):
@@ -838,9 +832,7 @@ export default {
             if platform == "langchain":
                 categories = {item["metadata"]["category"] for item in data}
             elif platform == "weaviate":
-                categories = {
-                    obj["properties"]["category"] for obj in data["objects"]
-                }
+                categories = {obj["properties"]["category"] for obj in data["objects"]}
             elif platform == "chroma":
                 categories = {meta["category"] for meta in data["metadatas"]}
 
@@ -854,8 +846,7 @@ export default {
             # Check that at least one reference category exists
             ref_categories = categories - {"overview"}
             self.assertGreater(
-                len(ref_categories), 0,
-                f"{platform}: Should have at least one reference category"
+                len(ref_categories), 0, f"{platform}: Should have at least one reference category"
             )
 
     def test_e2e_rag_integration_workflow_chromadb(self):
@@ -878,17 +869,10 @@ export default {
 
         # Create collection and add documents
         collection = client.create_collection(data["collection_name"])
-        collection.add(
-            documents=data["documents"],
-            metadatas=data["metadatas"],
-            ids=data["ids"]
-        )
+        collection.add(documents=data["documents"], metadatas=data["metadatas"], ids=data["ids"])
 
         # Query
-        results = collection.query(
-            query_texts=["reactivity"],
-            n_results=2
-        )
+        results = collection.query(query_texts=["reactivity"], n_results=2)
 
         # Verify results
         self.assertGreater(len(results["documents"][0]), 0, "Should return results")
diff --git a/tests/test_adaptors/test_chroma_adaptor.py b/tests/test_adaptors/test_chroma_adaptor.py
index e36e61e..8d07a78 100644
--- a/tests/test_adaptors/test_chroma_adaptor.py
+++ b/tests/test_adaptors/test_chroma_adaptor.py
@@ -28,9 +28,7 @@ class TestChromaAdaptor:
 
         # Create SKILL.md
         skill_md = skill_dir / "SKILL.md"
-        skill_md.write_text(
-            "# Test Skill\n\nThis is a test skill for Chroma format."
-        )
+        skill_md.write_text("# Test Skill\n\nThis is a test skill for Chroma format.")
 
         # Create references directory with files
         refs_dir = skill_dir / "references"
@@ -40,9 +38,7 @@ class TestChromaAdaptor:
 
         # Format as Chroma collection
         adaptor = get_adaptor("chroma")
-        metadata = SkillMetadata(
-            name="test_skill", description="Test skill", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="test_skill", description="Test skill", version="1.0.0")
 
         collection_json = adaptor.format_skill_md(skill_dir, metadata)
 
@@ -124,7 +120,10 @@ class TestChromaAdaptor:
         # Upload may fail if chromadb not installed (expected)
         assert "message" in result
         # Either chromadb not installed or connection error
-        assert ("chromadb not installed" in result["message"] or "Failed to connect" in result["message"])
+        assert (
+            "chromadb not installed" in result["message"]
+            or "Failed to connect" in result["message"]
+        )
 
     def test_validate_api_key_returns_false(self):
         """Test that API key validation returns False (no API needed)."""
@@ -157,9 +156,7 @@ class TestChromaAdaptor:
         skill_dir.mkdir()
 
         adaptor = get_adaptor("chroma")
-        metadata = SkillMetadata(
-            name="empty_skill", description="Empty", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="empty_skill", description="Empty", version="1.0.0")
 
         collection_json = adaptor.format_skill_md(skill_dir, metadata)
         collection = json.loads(collection_json)
@@ -179,9 +176,7 @@ class TestChromaAdaptor:
         (refs_dir / "test.md").write_text("# Test\n\nTest content.")
 
         adaptor = get_adaptor("chroma")
-        metadata = SkillMetadata(
-            name="refs_only", description="Refs only", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="refs_only", description="Refs only", version="1.0.0")
 
         collection_json = adaptor.format_skill_md(skill_dir, metadata)
         collection = json.loads(collection_json)
diff --git a/tests/test_adaptors/test_faiss_adaptor.py b/tests/test_adaptors/test_faiss_adaptor.py
index d0993a3..e70b8de 100644
--- a/tests/test_adaptors/test_faiss_adaptor.py
+++ b/tests/test_adaptors/test_faiss_adaptor.py
@@ -28,9 +28,7 @@ class TestFAISSAdaptor:
 
         # Create SKILL.md
         skill_md = skill_dir / "SKILL.md"
-        skill_md.write_text(
-            "# Test Skill\n\nThis is a test skill for FAISS format."
-        )
+        skill_md.write_text("# Test Skill\n\nThis is a test skill for FAISS format.")
 
         # Create references directory with files
         refs_dir = skill_dir / "references"
@@ -40,9 +38,7 @@ class TestFAISSAdaptor:
 
         # Format as FAISS index data
         adaptor = get_adaptor("faiss")
-        metadata = SkillMetadata(
-            name="test_skill", description="Test skill", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="test_skill", description="Test skill", version="1.0.0")
 
         index_json = adaptor.format_skill_md(skill_dir, metadata)
 
@@ -158,9 +154,7 @@ class TestFAISSAdaptor:
         skill_dir.mkdir()
 
         adaptor = get_adaptor("faiss")
-        metadata = SkillMetadata(
-            name="empty_skill", description="Empty", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="empty_skill", description="Empty", version="1.0.0")
 
         index_json = adaptor.format_skill_md(skill_dir, metadata)
         index_data = json.loads(index_json)
@@ -180,9 +174,7 @@ class TestFAISSAdaptor:
         (refs_dir / "test.md").write_text("# Test\n\nTest content.")
 
         adaptor = get_adaptor("faiss")
-        metadata = SkillMetadata(
-            name="refs_only", description="Refs only", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="refs_only", description="Refs only", version="1.0.0")
 
         index_json = adaptor.format_skill_md(skill_dir, metadata)
         index_data = json.loads(index_json)
diff --git a/tests/test_adaptors/test_haystack_adaptor.py b/tests/test_adaptors/test_haystack_adaptor.py
index e0278d6..6bcbd87 100644
--- a/tests/test_adaptors/test_haystack_adaptor.py
+++ b/tests/test_adaptors/test_haystack_adaptor.py
@@ -28,9 +28,7 @@ class TestHaystackAdaptor:
 
         # Create SKILL.md
         skill_md = skill_dir / "SKILL.md"
-        skill_md.write_text(
-            "# Test Skill\n\nThis is a test skill for Haystack format."
-        )
+        skill_md.write_text("# Test Skill\n\nThis is a test skill for Haystack format.")
 
         # Create references directory with files
         refs_dir = skill_dir / "references"
@@ -40,9 +38,7 @@ class TestHaystackAdaptor:
 
         # Format as Haystack Documents
         adaptor = get_adaptor("haystack")
-        metadata = SkillMetadata(
-            name="test_skill", description="Test skill", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="test_skill", description="Test skill", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
 
@@ -112,7 +108,7 @@ class TestHaystackAdaptor:
         """Test upload returns instructions (no actual upload)."""
         # Create test package
         package_path = tmp_path / "test-haystack.json"
-        package_path.write_text('[]')
+        package_path.write_text("[]")
 
         adaptor = get_adaptor("haystack")
         result = adaptor.upload(package_path, "fake-key")
@@ -154,9 +150,7 @@ class TestHaystackAdaptor:
         skill_dir.mkdir()
 
         adaptor = get_adaptor("haystack")
-        metadata = SkillMetadata(
-            name="empty_skill", description="Empty", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="empty_skill", description="Empty", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
         documents = json.loads(documents_json)
@@ -174,9 +168,7 @@ class TestHaystackAdaptor:
         (refs_dir / "test.md").write_text("# Test\n\nTest content.")
 
         adaptor = get_adaptor("haystack")
-        metadata = SkillMetadata(
-            name="refs_only", description="Refs only", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="refs_only", description="Refs only", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
         documents = json.loads(documents_json)
diff --git a/tests/test_adaptors/test_langchain_adaptor.py b/tests/test_adaptors/test_langchain_adaptor.py
index 9919bc3..76dd5b3 100644
--- a/tests/test_adaptors/test_langchain_adaptor.py
+++ b/tests/test_adaptors/test_langchain_adaptor.py
@@ -28,9 +28,7 @@ class TestLangChainAdaptor:
 
         # Create SKILL.md
         skill_md = skill_dir / "SKILL.md"
-        skill_md.write_text(
-            "# Test Skill\n\nThis is a test skill for LangChain format."
-        )
+        skill_md.write_text("# Test Skill\n\nThis is a test skill for LangChain format.")
 
         # Create references directory with files
         refs_dir = skill_dir / "references"
@@ -40,9 +38,7 @@ class TestLangChainAdaptor:
 
         # Format as LangChain Documents
         adaptor = get_adaptor("langchain")
-        metadata = SkillMetadata(
-            name="test_skill", description="Test skill", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="test_skill", description="Test skill", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
 
@@ -112,7 +108,7 @@ class TestLangChainAdaptor:
         """Test upload returns instructions (no actual upload)."""
         # Create test package
         package_path = tmp_path / "test-langchain.json"
-        package_path.write_text('[]')
+        package_path.write_text("[]")
 
         adaptor = get_adaptor("langchain")
         result = adaptor.upload(package_path, "fake-key")
@@ -153,9 +149,7 @@ class TestLangChainAdaptor:
         skill_dir.mkdir()
 
         adaptor = get_adaptor("langchain")
-        metadata = SkillMetadata(
-            name="empty_skill", description="Empty", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="empty_skill", description="Empty", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
         documents = json.loads(documents_json)
@@ -173,9 +167,7 @@ class TestLangChainAdaptor:
         (refs_dir / "test.md").write_text("# Test\n\nTest content.")
 
         adaptor = get_adaptor("langchain")
-        metadata = SkillMetadata(
-            name="refs_only", description="Refs only", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="refs_only", description="Refs only", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
         documents = json.loads(documents_json)
diff --git a/tests/test_adaptors/test_llama_index_adaptor.py b/tests/test_adaptors/test_llama_index_adaptor.py
index ab01f21..80357a8 100644
--- a/tests/test_adaptors/test_llama_index_adaptor.py
+++ b/tests/test_adaptors/test_llama_index_adaptor.py
@@ -28,9 +28,7 @@ class TestLlamaIndexAdaptor:
 
         # Create SKILL.md
         skill_md = skill_dir / "SKILL.md"
-        skill_md.write_text(
-            "# Test Skill\n\nThis is a test skill for LlamaIndex format."
-        )
+        skill_md.write_text("# Test Skill\n\nThis is a test skill for LlamaIndex format.")
 
         # Create references directory with files
         refs_dir = skill_dir / "references"
@@ -40,9 +38,7 @@ class TestLlamaIndexAdaptor:
 
         # Format as LlamaIndex Documents
         adaptor = get_adaptor("llama-index")
-        metadata = SkillMetadata(
-            name="test_skill", description="Test skill", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="test_skill", description="Test skill", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
 
@@ -112,7 +108,7 @@ class TestLlamaIndexAdaptor:
         """Test upload returns instructions (no actual upload)."""
         # Create test package
         package_path = tmp_path / "test-llama-index.json"
-        package_path.write_text('[]')
+        package_path.write_text("[]")
 
         adaptor = get_adaptor("llama-index")
         result = adaptor.upload(package_path, "fake-key")
@@ -153,9 +149,7 @@ class TestLlamaIndexAdaptor:
         skill_dir.mkdir()
 
         adaptor = get_adaptor("llama-index")
-        metadata = SkillMetadata(
-            name="empty_skill", description="Empty", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="empty_skill", description="Empty", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
         documents = json.loads(documents_json)
@@ -173,9 +167,7 @@ class TestLlamaIndexAdaptor:
         (refs_dir / "test.md").write_text("# Test\n\nTest content.")
 
         adaptor = get_adaptor("llama-index")
-        metadata = SkillMetadata(
-            name="refs_only", description="Refs only", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="refs_only", description="Refs only", version="1.0.0")
 
         documents_json = adaptor.format_skill_md(skill_dir, metadata)
         documents = json.loads(documents_json)
diff --git a/tests/test_adaptors/test_qdrant_adaptor.py b/tests/test_adaptors/test_qdrant_adaptor.py
index 019f926..672bdf3 100644
--- a/tests/test_adaptors/test_qdrant_adaptor.py
+++ b/tests/test_adaptors/test_qdrant_adaptor.py
@@ -28,9 +28,7 @@ class TestQdrantAdaptor:
 
         # Create SKILL.md
         skill_md = skill_dir / "SKILL.md"
-        skill_md.write_text(
-            "# Test Skill\n\nThis is a test skill for Qdrant format."
-        )
+        skill_md.write_text("# Test Skill\n\nThis is a test skill for Qdrant format.")
 
         # Create references directory with files
         refs_dir = skill_dir / "references"
@@ -40,9 +38,7 @@ class TestQdrantAdaptor:
 
         # Format as Qdrant points
         adaptor = get_adaptor("qdrant")
-        metadata = SkillMetadata(
-            name="test_skill", description="Test skill", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="test_skill", description="Test skill", version="1.0.0")
 
         points_json = adaptor.format_skill_md(skill_dir, metadata)
 
@@ -119,7 +115,7 @@ class TestQdrantAdaptor:
         """Test upload returns instructions (no actual upload)."""
         # Create test package
         package_path = tmp_path / "test-qdrant.json"
-        package_path.write_text('[]')
+        package_path.write_text("[]")
 
         adaptor = get_adaptor("qdrant")
         result = adaptor.upload(package_path, "fake-key")
@@ -160,9 +156,7 @@ class TestQdrantAdaptor:
         skill_dir.mkdir()
 
         adaptor = get_adaptor("qdrant")
-        metadata = SkillMetadata(
-            name="empty_skill", description="Empty", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="empty_skill", description="Empty", version="1.0.0")
 
         points_json = adaptor.format_skill_md(skill_dir, metadata)
         result = json.loads(points_json)
@@ -181,9 +175,7 @@ class TestQdrantAdaptor:
         (refs_dir / "test.md").write_text("# Test\n\nTest content.")
 
         adaptor = get_adaptor("qdrant")
-        metadata = SkillMetadata(
-            name="refs_only", description="Refs only", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="refs_only", description="Refs only", version="1.0.0")
 
         points_json = adaptor.format_skill_md(skill_dir, metadata)
         result = json.loads(points_json)
diff --git a/tests/test_adaptors/test_weaviate_adaptor.py b/tests/test_adaptors/test_weaviate_adaptor.py
index 6a50e11..66d485d 100644
--- a/tests/test_adaptors/test_weaviate_adaptor.py
+++ b/tests/test_adaptors/test_weaviate_adaptor.py
@@ -28,9 +28,7 @@ class TestWeaviateAdaptor:
 
         # Create SKILL.md
         skill_md = skill_dir / "SKILL.md"
-        skill_md.write_text(
-            "# Test Skill\n\nThis is a test skill for Weaviate format."
-        )
+        skill_md.write_text("# Test Skill\n\nThis is a test skill for Weaviate format.")
 
         # Create references directory with files
         refs_dir = skill_dir / "references"
@@ -40,9 +38,7 @@ class TestWeaviateAdaptor:
 
         # Format as Weaviate objects
         adaptor = get_adaptor("weaviate")
-        metadata = SkillMetadata(
-            name="test_skill", description="Test skill", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="test_skill", description="Test skill", version="1.0.0")
 
         objects_json = adaptor.format_skill_md(skill_dir, metadata)
 
@@ -119,7 +115,7 @@ class TestWeaviateAdaptor:
         """Test upload returns instructions (no actual upload)."""
         # Create test package
         package_path = tmp_path / "test-weaviate.json"
-        package_path.write_text('[]')
+        package_path.write_text("[]")
 
         adaptor = get_adaptor("weaviate")
         result = adaptor.upload(package_path, "fake-key")
@@ -127,7 +123,11 @@ class TestWeaviateAdaptor:
         # Upload may fail if weaviate not installed (expected)
         assert "message" in result
         # Either weaviate not installed, invalid JSON, or connection error
-        assert ("import weaviate" in result["message"] or "Failed to connect" in result["message"] or result["success"] is False)
+        assert (
+            "import weaviate" in result["message"]
+            or "Failed to connect" in result["message"]
+            or result["success"] is False
+        )
 
     def test_validate_api_key_returns_false(self):
         """Test that API key validation returns False (no API needed)."""
@@ -160,9 +160,7 @@ class TestWeaviateAdaptor:
         skill_dir.mkdir()
 
         adaptor = get_adaptor("weaviate")
-        metadata = SkillMetadata(
-            name="empty_skill", description="Empty", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="empty_skill", description="Empty", version="1.0.0")
 
         objects_json = adaptor.format_skill_md(skill_dir, metadata)
         result = json.loads(objects_json)
@@ -181,9 +179,7 @@ class TestWeaviateAdaptor:
         (refs_dir / "test.md").write_text("# Test\n\nTest content.")
 
         adaptor = get_adaptor("weaviate")
-        metadata = SkillMetadata(
-            name="refs_only", description="Refs only", version="1.0.0"
-        )
+        metadata = SkillMetadata(name="refs_only", description="Refs only", version="1.0.0")
 
         objects_json = adaptor.format_skill_md(skill_dir, metadata)
         result = json.loads(objects_json)
diff --git a/tests/test_benchmark.py b/tests/test_benchmark.py
index b0f5a8c..9163584 100644
--- a/tests/test_benchmark.py
+++ b/tests/test_benchmark.py
@@ -12,7 +12,7 @@ from skill_seekers.benchmark import (
     BenchmarkResult,
     BenchmarkRunner,
     BenchmarkReport,
-    Metric
+    Metric,
 )
 from skill_seekers.benchmark.models import TimingResult, MemoryUsage
 
@@ -37,12 +37,7 @@ class TestBenchmarkResult:
         """Test adding timing result."""
         result = BenchmarkResult("test")
 
-        timing = TimingResult(
-            operation="test_op",
-            duration=1.5,
-            iterations=1,
-            avg_duration=1.5
-        )
+        timing = TimingResult(operation="test_op", duration=1.5, iterations=1, avg_duration=1.5)
 
         result.add_timing(timing)
 
@@ -55,11 +50,7 @@ class TestBenchmarkResult:
         result = BenchmarkResult("test")
 
         usage = MemoryUsage(
-            operation="test_op",
-            before_mb=100.0,
-            after_mb=150.0,
-            peak_mb=160.0,
-            allocated_mb=50.0
+            operation="test_op", before_mb=100.0, after_mb=150.0, peak_mb=160.0, allocated_mb=50.0
         )
 
         result.add_memory(usage)
@@ -72,11 +63,7 @@ class TestBenchmarkResult:
         """Test adding custom metric."""
         result = BenchmarkResult("test")
 
-        metric = Metric(
-            name="pages_per_sec",
-            value=12.5,
-            unit="pages/sec"
-        )
+        metric = Metric(name="pages_per_sec", value=12.5, unit="pages/sec")
 
         result.add_metric(metric)
 
@@ -107,12 +94,7 @@ class TestBenchmarkResult:
         """Test report generation."""
         result = BenchmarkResult("test")
 
-        timing = TimingResult(
-            operation="test_op",
-            duration=1.0,
-            iterations=1,
-            avg_duration=1.0
-        )
+        timing = TimingResult(operation="test_op", duration=1.0, iterations=1, avg_duration=1.0)
         result.add_timing(timing)
 
         report = result.to_report()
@@ -303,7 +285,7 @@ class TestBenchmark:
             before_mb=100.0,
             after_mb=1200.0,
             peak_mb=1500.0,
-            allocated_mb=1100.0
+            allocated_mb=1100.0,
         )
         benchmark.result.add_memory(usage)
 
@@ -370,10 +352,7 @@ class TestBenchmarkRunner:
             with bench.timer("op2"):
                 time.sleep(0.03)
 
-        reports = runner.run_suite({
-            "test1": bench1,
-            "test2": bench2
-        })
+        reports = runner.run_suite({"test1": bench1, "test2": bench2})
 
         assert len(reports) == 2
         assert "test1" in reports
@@ -405,6 +384,7 @@ class TestBenchmarkRunner:
 
         # Compare
         from skill_seekers.benchmark.models import ComparisonReport
+
         comparison = runner.compare(baseline_path, improved_path)
 
         assert isinstance(comparison, ComparisonReport)
@@ -458,6 +438,7 @@ class TestBenchmarkRunner:
     def test_cleanup_old(self, tmp_path):
         """Test cleaning up old benchmarks."""
         import os
+
         runner = BenchmarkRunner(output_dir=tmp_path)
 
         # Create 10 benchmark files with different timestamps
@@ -476,10 +457,10 @@ class TestBenchmarkRunner:
                 "memory": [],
                 "metrics": [],
                 "system_info": {},
-                "recommendations": []
+                "recommendations": [],
             }
 
-            with open(file_path, 'w') as f:
+            with open(file_path, "w") as f:
                 json.dump(report_data, f)
 
             # Set different modification times
@@ -505,12 +486,7 @@ class TestBenchmarkModels:
 
     def test_timing_result_model(self):
         """Test TimingResult model."""
-        timing = TimingResult(
-            operation="test",
-            duration=1.5,
-            iterations=10,
-            avg_duration=0.15
-        )
+        timing = TimingResult(operation="test", duration=1.5, iterations=10, avg_duration=0.15)
 
         assert timing.operation == "test"
         assert timing.duration == 1.5
@@ -520,11 +496,7 @@ class TestBenchmarkModels:
     def test_memory_usage_model(self):
         """Test MemoryUsage model."""
         usage = MemoryUsage(
-            operation="allocate",
-            before_mb=100.0,
-            after_mb=200.0,
-            peak_mb=250.0,
-            allocated_mb=100.0
+            operation="allocate", before_mb=100.0, after_mb=200.0, peak_mb=250.0, allocated_mb=100.0
         )
 
         assert usage.operation == "allocate"
@@ -533,11 +505,7 @@ class TestBenchmarkModels:
 
     def test_metric_model(self):
         """Test Metric model."""
-        metric = Metric(
-            name="throughput",
-            value=125.5,
-            unit="ops/sec"
-        )
+        metric = Metric(name="throughput", value=125.5, unit="ops/sec")
 
         assert metric.name == "throughput"
         assert metric.value == 125.5
@@ -551,26 +519,19 @@ class TestBenchmarkModels:
             started_at=datetime.utcnow(),
             finished_at=datetime.utcnow(),
             total_duration=5.0,
-            timings=[
-                TimingResult(
-                    operation="op1",
-                    duration=2.0,
-                    iterations=1,
-                    avg_duration=2.0
-                )
-            ],
+            timings=[TimingResult(operation="op1", duration=2.0, iterations=1, avg_duration=2.0)],
             memory=[
                 MemoryUsage(
                     operation="op1",
                     before_mb=100.0,
                     after_mb=200.0,
                     peak_mb=250.0,
-                    allocated_mb=100.0
+                    allocated_mb=100.0,
                 )
             ],
             metrics=[],
             system_info={},
-            recommendations=[]
+            recommendations=[],
         )
 
         summary = report.summary
@@ -592,7 +553,7 @@ class TestBenchmarkModels:
             memory=[],
             metrics=[],
             system_info={},
-            recommendations=[]
+            recommendations=[],
         )
 
         current = BenchmarkReport(
@@ -604,7 +565,7 @@ class TestBenchmarkModels:
             memory=[],
             metrics=[],
             system_info={},
-            recommendations=[]
+            recommendations=[],
         )
 
         comparison = ComparisonReport(
@@ -614,7 +575,7 @@ class TestBenchmarkModels:
             improvements=[],
             regressions=["Slower performance"],
             speedup_factor=0.5,
-            memory_change_mb=0.0
+            memory_change_mb=0.0,
         )
 
         assert comparison.has_regressions is True
@@ -632,7 +593,7 @@ class TestBenchmarkModels:
             memory=[],
             metrics=[],
             system_info={},
-            recommendations=[]
+            recommendations=[],
         )
 
         current = BenchmarkReport(
@@ -644,7 +605,7 @@ class TestBenchmarkModels:
             memory=[],
             metrics=[],
             system_info={},
-            recommendations=[]
+            recommendations=[],
         )
 
         comparison = ComparisonReport(
@@ -654,7 +615,7 @@ class TestBenchmarkModels:
             improvements=[],
             regressions=[],
             speedup_factor=2.0,
-            memory_change_mb=0.0
+            memory_change_mb=0.0,
         )
 
         improvement = comparison.overall_improvement
diff --git a/tests/test_chunking_integration.py b/tests/test_chunking_integration.py
index 7bdd029..9a19128 100644
--- a/tests/test_chunking_integration.py
+++ b/tests/test_chunking_integration.py
@@ -60,7 +60,7 @@ class TestChunkingDisabledByDefault:
         """Test that LangChain doesn't chunk by default."""
         skill_dir = create_test_skill(tmp_path, large_doc=True)
 
-        adaptor = get_adaptor('langchain')
+        adaptor = get_adaptor("langchain")
         package_path = adaptor.package(skill_dir, tmp_path)
 
         with open(package_path) as f:
@@ -71,8 +71,8 @@ class TestChunkingDisabledByDefault:
 
         # No chunking metadata
         for doc in data:
-            assert 'is_chunked' not in doc['metadata']
-            assert 'chunk_index' not in doc['metadata']
+            assert "is_chunked" not in doc["metadata"]
+            assert "chunk_index" not in doc["metadata"]
 
 
 class TestChunkingEnabled:
@@ -82,12 +82,9 @@ class TestChunkingEnabled:
         """Test that LangChain chunks large documents when enabled."""
         skill_dir = create_test_skill(tmp_path, large_doc=True)
 
-        adaptor = get_adaptor('langchain')
+        adaptor = get_adaptor("langchain")
         package_path = adaptor.package(
-            skill_dir,
-            tmp_path,
-            enable_chunking=True,
-            chunk_max_tokens=512
+            skill_dir, tmp_path, enable_chunking=True, chunk_max_tokens=512
         )
 
         with open(package_path) as f:
@@ -97,25 +94,22 @@ class TestChunkingEnabled:
         assert len(data) > 2, f"Large doc should be chunked, got {len(data)} docs"
 
         # Check for chunking metadata
-        chunked_docs = [doc for doc in data if doc['metadata'].get('is_chunked')]
+        chunked_docs = [doc for doc in data if doc["metadata"].get("is_chunked")]
         assert len(chunked_docs) > 0, "Should have chunked documents"
 
         # Verify chunk metadata structure
         for doc in chunked_docs:
-            assert 'chunk_index' in doc['metadata']
-            assert 'total_chunks' in doc['metadata']
-            assert 'chunk_id' in doc['metadata']
+            assert "chunk_index" in doc["metadata"]
+            assert "total_chunks" in doc["metadata"]
+            assert "chunk_id" in doc["metadata"]
 
     def test_chunking_preserves_small_docs(self, tmp_path):
         """Test that small documents are not chunked."""
         skill_dir = create_test_skill(tmp_path, large_doc=False)
 
-        adaptor = get_adaptor('langchain')
+        adaptor = get_adaptor("langchain")
         package_path = adaptor.package(
-            skill_dir,
-            tmp_path,
-            enable_chunking=True,
-            chunk_max_tokens=512
+            skill_dir, tmp_path, enable_chunking=True, chunk_max_tokens=512
         )
 
         with open(package_path) as f:
@@ -125,7 +119,7 @@ class TestChunkingEnabled:
         assert len(data) == 2, "Small docs should not be chunked"
 
         for doc in data:
-            assert 'is_chunked' not in doc['metadata']
+            assert "is_chunked" not in doc["metadata"]
 
 
 class TestCodeBlockPreservation:
@@ -158,43 +152,43 @@ More content after code block.
         # Create references dir (required)
         (skill_dir / "references").mkdir()
 
-        adaptor = get_adaptor('langchain')
+        adaptor = get_adaptor("langchain")
         package_path = adaptor.package(
             skill_dir,
             tmp_path,
             enable_chunking=True,
             chunk_max_tokens=200,  # Small chunks to force splitting
-            preserve_code_blocks=True
+            preserve_code_blocks=True,
         )
 
         with open(package_path) as f:
             data = json.load(f)
 
         # Find chunks with code block
-        code_chunks = [
-            doc for doc in data
-            if '```python' in doc['page_content']
-        ]
+        code_chunks = [doc for doc in data if "```python" in doc["page_content"]]
 
         # Code block should be in at least one chunk
         assert len(code_chunks) >= 1, "Code block should be preserved"
 
         # Code block should be complete (opening and closing backticks)
         for chunk in code_chunks:
-            content = chunk['page_content']
-            if '```python' in content:
+            content = chunk["page_content"]
+            if "```python" in content:
                 # Should also have closing backticks
-                assert content.count('```') >= 2, "Code block should be complete"
+                assert content.count("```") >= 2, "Code block should be complete"
 
 
 class TestAutoChunkingForRAGPlatforms:
     """Test that chunking is auto-enabled for RAG platforms."""
 
-    @pytest.mark.parametrize("platform", [
-        'langchain',
-        # Add others after they're updated:
-        # 'llama-index', 'haystack', 'weaviate', 'chroma', 'faiss', 'qdrant'
-    ])
+    @pytest.mark.parametrize(
+        "platform",
+        [
+            "langchain",
+            # Add others after they're updated:
+            # 'llama-index', 'haystack', 'weaviate', 'chroma', 'faiss', 'qdrant'
+        ],
+    )
     def test_rag_platforms_auto_chunk(self, platform, tmp_path):
         """Test that RAG platforms auto-enable chunking."""
         skill_dir = create_test_skill(tmp_path, large_doc=True)
@@ -208,7 +202,7 @@ class TestAutoChunkingForRAGPlatforms:
             open_folder_after=False,
             skip_quality_check=True,
             target=platform,
-            enable_chunking=False  # Explicitly disabled, but should be auto-enabled
+            enable_chunking=False,  # Explicitly disabled, but should be auto-enabled
         )
 
         assert success, f"Packaging failed for {platform}"
@@ -221,8 +215,8 @@ class TestAutoChunkingForRAGPlatforms:
         # Should have multiple documents/chunks
         if isinstance(data, list):
             assert len(data) > 2, f"{platform}: Should auto-chunk large docs"
-        elif isinstance(data, dict) and 'documents' in data:
-            assert len(data['documents']) > 2, f"{platform}: Should auto-chunk large docs"
+        elif isinstance(data, dict) and "documents" in data:
+            assert len(data["documents"]) > 2, f"{platform}: Should auto-chunk large docs"
 
 
 class TestBaseAdaptorChunkingHelper:
@@ -237,11 +231,7 @@ class TestBaseAdaptorChunkingHelper:
         content = "Test content " * 1000  # Large content
         metadata = {"source": "test"}
 
-        chunks = adaptor._maybe_chunk_content(
-            content,
-            metadata,
-            enable_chunking=False
-        )
+        chunks = adaptor._maybe_chunk_content(content, metadata, enable_chunking=False)
 
         # Should return single chunk
         assert len(chunks) == 1
@@ -258,10 +248,7 @@ class TestBaseAdaptorChunkingHelper:
         metadata = {"source": "test"}
 
         chunks = adaptor._maybe_chunk_content(
-            content,
-            metadata,
-            enable_chunking=True,
-            chunk_max_tokens=512
+            content, metadata, enable_chunking=True, chunk_max_tokens=512
         )
 
         # Should return single chunk
@@ -282,7 +269,7 @@ class TestBaseAdaptorChunkingHelper:
             enable_chunking=True,
             chunk_max_tokens=512,
             preserve_code_blocks=True,
-            source_file="test.md"
+            source_file="test.md",
         )
 
         # Should return multiple chunks
@@ -292,12 +279,12 @@ class TestBaseAdaptorChunkingHelper:
         for chunk_text, chunk_meta in chunks:
             assert isinstance(chunk_text, str)
             assert isinstance(chunk_meta, dict)
-            assert chunk_meta['is_chunked']
-            assert 'chunk_index' in chunk_meta
-            assert 'chunk_id' in chunk_meta
+            assert chunk_meta["is_chunked"]
+            assert "chunk_index" in chunk_meta
+            assert "chunk_id" in chunk_meta
             # Original metadata preserved
-            assert chunk_meta['source'] == 'test'
-            assert chunk_meta['file'] == 'test.md'
+            assert chunk_meta["source"] == "test"
+            assert chunk_meta["file"] == "test.md"
 
 
 class TestChunkingCLIIntegration:
@@ -313,10 +300,10 @@ class TestChunkingCLIIntegration:
             skill_dir=skill_dir,
             open_folder_after=False,
             skip_quality_check=True,
-            target='langchain',
+            target="langchain",
             enable_chunking=True,  # --chunk flag
             chunk_max_tokens=512,
-            preserve_code_blocks=True
+            preserve_code_blocks=True,
         )
 
         assert success
@@ -339,10 +326,10 @@ class TestChunkingCLIIntegration:
             skill_dir=skill_dir,
             open_folder_after=False,
             skip_quality_check=True,
-            target='langchain',
+            target="langchain",
             enable_chunking=True,
             chunk_max_tokens=256,  # Small chunks
-            preserve_code_blocks=True
+            preserve_code_blocks=True,
         )
 
         assert success
@@ -355,10 +342,10 @@ class TestChunkingCLIIntegration:
             skill_dir=skill_dir,
             open_folder_after=False,
             skip_quality_check=True,
-            target='langchain',
+            target="langchain",
             enable_chunking=True,
             chunk_max_tokens=1024,  # Large chunks
-            preserve_code_blocks=True
+            preserve_code_blocks=True,
         )
 
         assert success
@@ -367,9 +354,10 @@ class TestChunkingCLIIntegration:
             data_large = json.load(f)
 
         # Small chunk size should produce more chunks
-        assert len(data_small) > len(data_large), \
+        assert len(data_small) > len(data_large), (
             f"Small chunks ({len(data_small)}) should be more than large chunks ({len(data_large)})"
+        )
 
 
-if __name__ == '__main__':
-    pytest.main([__file__, '-v'])
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/tests/test_cli_parsers.py b/tests/test_cli_parsers.py
index 46c1827..d379e21 100644
--- a/tests/test_cli_parsers.py
+++ b/tests/test_cli_parsers.py
@@ -30,12 +30,12 @@ class TestParserRegistry:
         """Test getting list of parser names."""
         names = get_parser_names()
         assert len(names) == 19
-        assert 'scrape' in names
-        assert 'github' in names
-        assert 'package' in names
-        assert 'upload' in names
-        assert 'analyze' in names
-        assert 'config' in names
+        assert "scrape" in names
+        assert "github" in names
+        assert "package" in names
+        assert "upload" in names
+        assert "analyze" in names
+        assert "config" in names
 
     def test_all_parsers_are_subcommand_parsers(self):
         """Test that all parsers inherit from SubcommandParser."""
@@ -45,9 +45,9 @@ class TestParserRegistry:
     def test_all_parsers_have_required_properties(self):
         """Test that all parsers have name, help, description."""
         for parser in PARSERS:
-            assert hasattr(parser, 'name')
-            assert hasattr(parser, 'help')
-            assert hasattr(parser, 'description')
+            assert hasattr(parser, "name")
+            assert hasattr(parser, "help")
+            assert hasattr(parser, "description")
             assert isinstance(parser.name, str)
             assert isinstance(parser.help, str)
             assert isinstance(parser.description, str)
@@ -57,7 +57,7 @@ class TestParserRegistry:
     def test_all_parsers_have_add_arguments_method(self):
         """Test that all parsers implement add_arguments."""
         for parser in PARSERS:
-            assert hasattr(parser, 'add_arguments')
+            assert hasattr(parser, "add_arguments")
             assert callable(parser.add_arguments)
 
     def test_no_duplicate_parser_names(self):
@@ -106,21 +106,21 @@ class TestParserCreation:
     def test_register_parsers_creates_all_subcommands(self):
         """Test that register_parsers creates all 19 subcommands."""
         main_parser = argparse.ArgumentParser()
-        subparsers = main_parser.add_subparsers(dest='command')
+        subparsers = main_parser.add_subparsers(dest="command")
 
         # Register all parsers
         register_parsers(subparsers)
 
         # Test that all commands can be parsed
         test_commands = [
-            'config --show',
-            'scrape --config test.json',
-            'github --repo owner/repo',
-            'package output/test/',
-            'upload test.zip',
-            'analyze --directory .',
-            'enhance output/test/',
-            'estimate test.json',
+            "config --show",
+            "scrape --config test.json",
+            "github --repo owner/repo",
+            "package output/test/",
+            "upload test.zip",
+            "analyze --directory .",
+            "enhance output/test/",
+            "estimate test.json",
         ]
 
         for cmd in test_commands:
@@ -134,75 +134,76 @@ class TestSpecificParsers:
     def test_scrape_parser_arguments(self):
         """Test ScrapeParser has correct arguments."""
         main_parser = argparse.ArgumentParser()
-        subparsers = main_parser.add_subparsers(dest='command')
+        subparsers = main_parser.add_subparsers(dest="command")
 
         scrape_parser = ScrapeParser()
         scrape_parser.create_parser(subparsers)
 
         # Test various argument combinations
-        args = main_parser.parse_args(['scrape', '--config', 'test.json'])
-        assert args.command == 'scrape'
-        assert args.config == 'test.json'
+        args = main_parser.parse_args(["scrape", "--config", "test.json"])
+        assert args.command == "scrape"
+        assert args.config == "test.json"
 
-        args = main_parser.parse_args(['scrape', '--config', 'test.json', '--max-pages', '100'])
+        args = main_parser.parse_args(["scrape", "--config", "test.json", "--max-pages", "100"])
         assert args.max_pages == 100
 
-        args = main_parser.parse_args(['scrape', '--enhance'])
+        args = main_parser.parse_args(["scrape", "--enhance"])
         assert args.enhance is True
 
     def test_github_parser_arguments(self):
         """Test GitHubParser has correct arguments."""
         main_parser = argparse.ArgumentParser()
-        subparsers = main_parser.add_subparsers(dest='command')
+        subparsers = main_parser.add_subparsers(dest="command")
 
         github_parser = GitHubParser()
         github_parser.create_parser(subparsers)
 
-        args = main_parser.parse_args(['github', '--repo', 'owner/repo'])
-        assert args.command == 'github'
-        assert args.repo == 'owner/repo'
+        args = main_parser.parse_args(["github", "--repo", "owner/repo"])
+        assert args.command == "github"
+        assert args.repo == "owner/repo"
 
-        args = main_parser.parse_args(['github', '--repo', 'owner/repo', '--non-interactive'])
+        args = main_parser.parse_args(["github", "--repo", "owner/repo", "--non-interactive"])
         assert args.non_interactive is True
 
     def test_package_parser_arguments(self):
         """Test PackageParser has correct arguments."""
         main_parser = argparse.ArgumentParser()
-        subparsers = main_parser.add_subparsers(dest='command')
+        subparsers = main_parser.add_subparsers(dest="command")
 
         package_parser = PackageParser()
         package_parser.create_parser(subparsers)
 
-        args = main_parser.parse_args(['package', 'output/test/'])
-        assert args.command == 'package'
-        assert args.skill_directory == 'output/test/'
+        args = main_parser.parse_args(["package", "output/test/"])
+        assert args.command == "package"
+        assert args.skill_directory == "output/test/"
 
-        args = main_parser.parse_args(['package', 'output/test/', '--target', 'gemini'])
-        assert args.target == 'gemini'
+        args = main_parser.parse_args(["package", "output/test/", "--target", "gemini"])
+        assert args.target == "gemini"
 
-        args = main_parser.parse_args(['package', 'output/test/', '--no-open'])
+        args = main_parser.parse_args(["package", "output/test/", "--no-open"])
         assert args.no_open is True
 
     def test_analyze_parser_arguments(self):
         """Test AnalyzeParser has correct arguments."""
         main_parser = argparse.ArgumentParser()
-        subparsers = main_parser.add_subparsers(dest='command')
+        subparsers = main_parser.add_subparsers(dest="command")
 
         from skill_seekers.cli.parsers.analyze_parser import AnalyzeParser
+
         analyze_parser = AnalyzeParser()
         analyze_parser.create_parser(subparsers)
 
-        args = main_parser.parse_args(['analyze', '--directory', '.'])
-        assert args.command == 'analyze'
-        assert args.directory == '.'
+        args = main_parser.parse_args(["analyze", "--directory", "."])
+        assert args.command == "analyze"
+        assert args.directory == "."
 
-        args = main_parser.parse_args(['analyze', '--directory', '.', '--quick'])
+        args = main_parser.parse_args(["analyze", "--directory", ".", "--quick"])
         assert args.quick is True
 
-        args = main_parser.parse_args(['analyze', '--directory', '.', '--comprehensive'])
+        args = main_parser.parse_args(["analyze", "--directory", ".", "--comprehensive"])
         assert args.comprehensive is True
 
-        args = main_parser.parse_args(['analyze', '--directory', '.', '--skip-patterns'])
+        args = main_parser.parse_args(["analyze", "--directory", ".", "--skip-patterns"])
         assert args.skip_patterns is True
 
 
@@ -215,11 +216,25 @@ class TestBackwardCompatibility:
 
         # Original commands from old main.py
         original_commands = [
-            'config', 'scrape', 'github', 'pdf', 'unified',
-            'enhance', 'enhance-status', 'package', 'upload',
-            'estimate', 'extract-test-examples', 'install-agent',
-            'analyze', 'install', 'resume', 'stream',
-            'update', 'multilang', 'quality'
+            "config",
+            "scrape",
+            "github",
+            "pdf",
+            "unified",
+            "enhance",
+            "enhance-status",
+            "package",
+            "upload",
+            "estimate",
+            "extract-test-examples",
+            "install-agent",
+            "analyze",
+            "install",
+            "resume",
+            "stream",
+            "update",
+            "multilang",
+            "quality",
         ]
 
         for cmd in original_commands:
diff --git a/tests/test_cloud_storage.py b/tests/test_cloud_storage.py
index 91ff4a0..ed26c40 100644
--- a/tests/test_cloud_storage.py
+++ b/tests/test_cloud_storage.py
@@ -20,18 +20,21 @@ from skill_seekers.cli.storage import (
 # Check if cloud storage dependencies are available
 try:
     import boto3  # noqa: F401
+
     BOTO3_AVAILABLE = True
 except ImportError:
     BOTO3_AVAILABLE = False
 
 try:
     from google.cloud import storage  # noqa: F401
+
     GCS_AVAILABLE = True
 except ImportError:
     GCS_AVAILABLE = False
 
 try:
     from azure.storage.blob import BlobServiceClient  # noqa: F401
+
     AZURE_AVAILABLE = True
 except ImportError:
     AZURE_AVAILABLE = False
@@ -41,12 +44,13 @@ except ImportError:
 # Factory Tests
 # ========================================
 
+
 def test_get_storage_adaptor_s3():
     """Test S3 adaptor factory."""
     if not BOTO3_AVAILABLE:
         pytest.skip("boto3 not installed")
-    with patch('skill_seekers.cli.storage.s3_storage.boto3'):
-        adaptor = get_storage_adaptor('s3', bucket='test-bucket')
+    with patch("skill_seekers.cli.storage.s3_storage.boto3"):
+        adaptor = get_storage_adaptor("s3", bucket="test-bucket")
         assert isinstance(adaptor, S3StorageAdaptor)
 
 
@@ -54,8 +58,8 @@ def test_get_storage_adaptor_gcs():
     """Test GCS adaptor factory."""
     if not GCS_AVAILABLE:
         pytest.skip("google-cloud-storage not installed")
-    with patch('skill_seekers.cli.storage.gcs_storage.storage'):
-        adaptor = get_storage_adaptor('gcs', bucket='test-bucket')
+    with patch("skill_seekers.cli.storage.gcs_storage.storage"):
+        adaptor = get_storage_adaptor("gcs", bucket="test-bucket")
         assert isinstance(adaptor, GCSStorageAdaptor)
 
 
@@ -63,11 +67,11 @@ def test_get_storage_adaptor_azure():
     """Test Azure adaptor factory."""
     if not AZURE_AVAILABLE:
         pytest.skip("azure-storage-blob not installed")
-    with patch('skill_seekers.cli.storage.azure_storage.BlobServiceClient'):
+    with patch("skill_seekers.cli.storage.azure_storage.BlobServiceClient"):
         adaptor = get_storage_adaptor(
-            'azure',
-            container='test-container',
-            connection_string='DefaultEndpointsProtocol=https;AccountName=test;AccountKey=key'
+            "azure",
+            container="test-container",
+            connection_string="DefaultEndpointsProtocol=https;AccountName=test;AccountKey=key",
         )
         assert isinstance(adaptor, AzureStorageAdaptor)
 
@@ -75,36 +79,37 @@ def test_get_storage_adaptor_azure():
 def test_get_storage_adaptor_invalid_provider():
     """Test invalid provider raises error."""
     with pytest.raises(ValueError, match="Unsupported storage provider"):
-        get_storage_adaptor('invalid', bucket='test')
+        get_storage_adaptor("invalid", bucket="test")
 
 
 # ========================================
 # S3 Storage Tests
 # ========================================
 
+
 def test_s3_upload_file():
     """Test S3 file upload."""
     if not BOTO3_AVAILABLE:
         pytest.skip("boto3 not installed")
 
-    with patch('skill_seekers.cli.storage.s3_storage.boto3') as mock_boto3:
+    with patch("skill_seekers.cli.storage.s3_storage.boto3") as mock_boto3:
         # Setup mocks
         mock_client = Mock()
         mock_boto3.client.return_value = mock_client
         mock_boto3.resource.return_value = Mock()
 
-        adaptor = S3StorageAdaptor(bucket='test-bucket')
+        adaptor = S3StorageAdaptor(bucket="test-bucket")
 
         # Create temporary file
         with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-            tmp_file.write(b'test content')
+            tmp_file.write(b"test content")
             tmp_path = tmp_file.name
 
         try:
             # Test upload
-            result = adaptor.upload_file(tmp_path, 'test.txt')
+            result = adaptor.upload_file(tmp_path, "test.txt")
 
-            assert result == 's3://test-bucket/test.txt'
+            assert result == "s3://test-bucket/test.txt"
             mock_client.upload_file.assert_called_once()
         finally:
             Path(tmp_path).unlink()
@@ -115,23 +120,21 @@ def test_s3_download_file():
     if not BOTO3_AVAILABLE:
         pytest.skip("boto3 not installed")
 
-    with patch('skill_seekers.cli.storage.s3_storage.boto3') as mock_boto3:
+    with patch("skill_seekers.cli.storage.s3_storage.boto3") as mock_boto3:
         # Setup mocks
         mock_client = Mock()
         mock_boto3.client.return_value = mock_client
         mock_boto3.resource.return_value = Mock()
 
-        adaptor = S3StorageAdaptor(bucket='test-bucket')
+        adaptor = S3StorageAdaptor(bucket="test-bucket")
 
         with tempfile.TemporaryDirectory() as tmp_dir:
-            local_path = os.path.join(tmp_dir, 'downloaded.txt')
+            local_path = os.path.join(tmp_dir, "downloaded.txt")
 
             # Test download
-            adaptor.download_file('test.txt', local_path)
+            adaptor.download_file("test.txt", local_path)
 
-            mock_client.download_file.assert_called_once_with(
-                'test-bucket', 'test.txt', local_path
-            )
+            mock_client.download_file.assert_called_once_with("test-bucket", "test.txt", local_path)
 
 
 def test_s3_list_files():
@@ -139,18 +142,18 @@ def test_s3_list_files():
     if not BOTO3_AVAILABLE:
         pytest.skip("boto3 not installed")
 
-    with patch('skill_seekers.cli.storage.s3_storage.boto3') as mock_boto3:
+    with patch("skill_seekers.cli.storage.s3_storage.boto3") as mock_boto3:
         # Setup mocks
         mock_client = Mock()
         mock_paginator = Mock()
         mock_page_iterator = [
             {
-                'Contents': [
+                "Contents": [
                     {
-                        'Key': 'file1.txt',
-                        'Size': 100,
-                        'LastModified': Mock(isoformat=lambda: '2024-01-01T00:00:00'),
-                        'ETag': '"abc123"'
+                        "Key": "file1.txt",
+                        "Size": 100,
+                        "LastModified": Mock(isoformat=lambda: "2024-01-01T00:00:00"),
+                        "ETag": '"abc123"',
                     }
                 ]
             }
@@ -161,15 +164,15 @@ def test_s3_list_files():
         mock_boto3.client.return_value = mock_client
         mock_boto3.resource.return_value = Mock()
 
-        adaptor = S3StorageAdaptor(bucket='test-bucket')
+        adaptor = S3StorageAdaptor(bucket="test-bucket")
 
         # Test list
-        files = adaptor.list_files('prefix/')
+        files = adaptor.list_files("prefix/")
 
         assert len(files) == 1
-        assert files[0].key == 'file1.txt'
+        assert files[0].key == "file1.txt"
         assert files[0].size == 100
-        assert files[0].etag == 'abc123'
+        assert files[0].etag == "abc123"
 
 
 def test_s3_file_exists():
@@ -177,17 +180,17 @@ def test_s3_file_exists():
     if not BOTO3_AVAILABLE:
         pytest.skip("boto3 not installed")
 
-    with patch('skill_seekers.cli.storage.s3_storage.boto3') as mock_boto3:
+    with patch("skill_seekers.cli.storage.s3_storage.boto3") as mock_boto3:
         # Setup mocks
         mock_client = Mock()
         mock_client.head_object.return_value = {}
         mock_boto3.client.return_value = mock_client
         mock_boto3.resource.return_value = Mock()
 
-        adaptor = S3StorageAdaptor(bucket='test-bucket')
+        adaptor = S3StorageAdaptor(bucket="test-bucket")
 
         # Test exists
-        assert adaptor.file_exists('test.txt') is True
+        assert adaptor.file_exists("test.txt") is True
 
 
 def test_s3_get_file_url():
@@ -195,19 +198,19 @@ def test_s3_get_file_url():
     if not BOTO3_AVAILABLE:
         pytest.skip("boto3 not installed")
 
-    with patch('skill_seekers.cli.storage.s3_storage.boto3') as mock_boto3:
+    with patch("skill_seekers.cli.storage.s3_storage.boto3") as mock_boto3:
         # Setup mocks
         mock_client = Mock()
-        mock_client.generate_presigned_url.return_value = 'https://s3.amazonaws.com/signed-url'
+        mock_client.generate_presigned_url.return_value = "https://s3.amazonaws.com/signed-url"
         mock_boto3.client.return_value = mock_client
         mock_boto3.resource.return_value = Mock()
 
-        adaptor = S3StorageAdaptor(bucket='test-bucket')
+        adaptor = S3StorageAdaptor(bucket="test-bucket")
 
         # Test URL generation
-        url = adaptor.get_file_url('test.txt', expires_in=7200)
+        url = adaptor.get_file_url("test.txt", expires_in=7200)
 
-        assert url == 'https://s3.amazonaws.com/signed-url'
+        assert url == "https://s3.amazonaws.com/signed-url"
         mock_client.generate_presigned_url.assert_called_once()
 
 
@@ -215,12 +218,13 @@ def test_s3_get_file_url():
 # GCS Storage Tests
 # ========================================
 
+
 def test_gcs_upload_file():
     """Test GCS file upload."""
     if not GCS_AVAILABLE:
         pytest.skip("google-cloud-storage not installed")
 
-    with patch('skill_seekers.cli.storage.gcs_storage.storage') as mock_storage:
+    with patch("skill_seekers.cli.storage.gcs_storage.storage") as mock_storage:
         # Setup mocks
         mock_client = Mock()
         mock_bucket = Mock()
@@ -230,18 +234,18 @@ def test_gcs_upload_file():
         mock_bucket.blob.return_value = mock_blob
         mock_storage.Client.return_value = mock_client
 
-        adaptor = GCSStorageAdaptor(bucket='test-bucket')
+        adaptor = GCSStorageAdaptor(bucket="test-bucket")
 
         # Create temporary file
         with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-            tmp_file.write(b'test content')
+            tmp_file.write(b"test content")
             tmp_path = tmp_file.name
 
         try:
             # Test upload
-            result = adaptor.upload_file(tmp_path, 'test.txt')
+            result = adaptor.upload_file(tmp_path, "test.txt")
 
-            assert result == 'gs://test-bucket/test.txt'
+            assert result == "gs://test-bucket/test.txt"
             mock_blob.upload_from_filename.assert_called_once()
         finally:
             Path(tmp_path).unlink()
@@ -252,7 +256,7 @@ def test_gcs_download_file():
     if not GCS_AVAILABLE:
         pytest.skip("google-cloud-storage not installed")
 
-    with patch('skill_seekers.cli.storage.gcs_storage.storage') as mock_storage:
+    with patch("skill_seekers.cli.storage.gcs_storage.storage") as mock_storage:
         # Setup mocks
         mock_client = Mock()
         mock_bucket = Mock()
@@ -262,13 +266,13 @@ def test_gcs_download_file():
         mock_bucket.blob.return_value = mock_blob
         mock_storage.Client.return_value = mock_client
 
-        adaptor = GCSStorageAdaptor(bucket='test-bucket')
+        adaptor = GCSStorageAdaptor(bucket="test-bucket")
 
         with tempfile.TemporaryDirectory() as tmp_dir:
-            local_path = os.path.join(tmp_dir, 'downloaded.txt')
+            local_path = os.path.join(tmp_dir, "downloaded.txt")
 
             # Test download
-            adaptor.download_file('test.txt', local_path)
+            adaptor.download_file("test.txt", local_path)
 
             mock_blob.download_to_filename.assert_called_once()
 
@@ -278,27 +282,27 @@ def test_gcs_list_files():
     if not GCS_AVAILABLE:
         pytest.skip("google-cloud-storage not installed")
 
-    with patch('skill_seekers.cli.storage.gcs_storage.storage') as mock_storage:
+    with patch("skill_seekers.cli.storage.gcs_storage.storage") as mock_storage:
         # Setup mocks
         mock_client = Mock()
         mock_blob = Mock()
-        mock_blob.name = 'file1.txt'
+        mock_blob.name = "file1.txt"
         mock_blob.size = 100
-        mock_blob.updated = Mock(isoformat=lambda: '2024-01-01T00:00:00')
-        mock_blob.etag = 'abc123'
+        mock_blob.updated = Mock(isoformat=lambda: "2024-01-01T00:00:00")
+        mock_blob.etag = "abc123"
         mock_blob.metadata = {}
 
         mock_client.list_blobs.return_value = [mock_blob]
         mock_storage.Client.return_value = mock_client
         mock_client.bucket.return_value = Mock()
 
-        adaptor = GCSStorageAdaptor(bucket='test-bucket')
+        adaptor = GCSStorageAdaptor(bucket="test-bucket")
 
         # Test list
-        files = adaptor.list_files('prefix/')
+        files = adaptor.list_files("prefix/")
 
         assert len(files) == 1
-        assert files[0].key == 'file1.txt'
+        assert files[0].key == "file1.txt"
         assert files[0].size == 100
 
 
@@ -306,12 +310,13 @@ def test_gcs_list_files():
 # Azure Storage Tests
 # ========================================
 
+
 def test_azure_upload_file():
     """Test Azure file upload."""
     if not AZURE_AVAILABLE:
         pytest.skip("azure-storage-blob not installed")
 
-    with patch('skill_seekers.cli.storage.azure_storage.BlobServiceClient') as mock_blob_service:
+    with patch("skill_seekers.cli.storage.azure_storage.BlobServiceClient") as mock_blob_service:
         # Setup mocks
         mock_service_client = Mock()
         mock_container_client = Mock()
@@ -321,19 +326,21 @@ def test_azure_upload_file():
         mock_container_client.get_blob_client.return_value = mock_blob_client
         mock_blob_service.from_connection_string.return_value = mock_service_client
 
-        connection_string = 'DefaultEndpointsProtocol=https;AccountName=test;AccountKey=key'
-        adaptor = AzureStorageAdaptor(container='test-container', connection_string=connection_string)
+        connection_string = "DefaultEndpointsProtocol=https;AccountName=test;AccountKey=key"
+        adaptor = AzureStorageAdaptor(
+            container="test-container", connection_string=connection_string
+        )
 
         # Create temporary file
         with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-            tmp_file.write(b'test content')
+            tmp_file.write(b"test content")
             tmp_path = tmp_file.name
 
         try:
             # Test upload
-            result = adaptor.upload_file(tmp_path, 'test.txt')
+            result = adaptor.upload_file(tmp_path, "test.txt")
 
-            assert 'test.blob.core.windows.net' in result
+            assert "test.blob.core.windows.net" in result
             mock_blob_client.upload_blob.assert_called_once()
         finally:
             Path(tmp_path).unlink()
@@ -344,30 +351,32 @@ def test_azure_download_file():
     if not AZURE_AVAILABLE:
         pytest.skip("azure-storage-blob not installed")
 
-    with patch('skill_seekers.cli.storage.azure_storage.BlobServiceClient') as mock_blob_service:
+    with patch("skill_seekers.cli.storage.azure_storage.BlobServiceClient") as mock_blob_service:
         # Setup mocks
         mock_service_client = Mock()
         mock_container_client = Mock()
         mock_blob_client = Mock()
         mock_download_stream = Mock()
-        mock_download_stream.readall.return_value = b'test content'
+        mock_download_stream.readall.return_value = b"test content"
 
         mock_service_client.get_container_client.return_value = mock_container_client
         mock_container_client.get_blob_client.return_value = mock_blob_client
         mock_blob_client.download_blob.return_value = mock_download_stream
         mock_blob_service.from_connection_string.return_value = mock_service_client
 
-        connection_string = 'DefaultEndpointsProtocol=https;AccountName=test;AccountKey=key'
-        adaptor = AzureStorageAdaptor(container='test-container', connection_string=connection_string)
+        connection_string = "DefaultEndpointsProtocol=https;AccountName=test;AccountKey=key"
+        adaptor = AzureStorageAdaptor(
+            container="test-container", connection_string=connection_string
+        )
 
         with tempfile.TemporaryDirectory() as tmp_dir:
-            local_path = os.path.join(tmp_dir, 'downloaded.txt')
+            local_path = os.path.join(tmp_dir, "downloaded.txt")
 
             # Test download
-            adaptor.download_file('test.txt', local_path)
+            adaptor.download_file("test.txt", local_path)
 
             assert Path(local_path).exists()
-            assert Path(local_path).read_bytes() == b'test content'
+            assert Path(local_path).read_bytes() == b"test content"
 
 
 def test_azure_list_files():
@@ -375,29 +384,31 @@ def test_azure_list_files():
     if not AZURE_AVAILABLE:
         pytest.skip("azure-storage-blob not installed")
 
-    with patch('skill_seekers.cli.storage.azure_storage.BlobServiceClient') as mock_blob_service:
+    with patch("skill_seekers.cli.storage.azure_storage.BlobServiceClient") as mock_blob_service:
         # Setup mocks
         mock_service_client = Mock()
         mock_container_client = Mock()
         mock_blob = Mock()
-        mock_blob.name = 'file1.txt'
+        mock_blob.name = "file1.txt"
         mock_blob.size = 100
-        mock_blob.last_modified = Mock(isoformat=lambda: '2024-01-01T00:00:00')
-        mock_blob.etag = 'abc123'
+        mock_blob.last_modified = Mock(isoformat=lambda: "2024-01-01T00:00:00")
+        mock_blob.etag = "abc123"
         mock_blob.metadata = {}
 
         mock_container_client.list_blobs.return_value = [mock_blob]
         mock_service_client.get_container_client.return_value = mock_container_client
         mock_blob_service.from_connection_string.return_value = mock_service_client
 
-        connection_string = 'DefaultEndpointsProtocol=https;AccountName=test;AccountKey=key'
-        adaptor = AzureStorageAdaptor(container='test-container', connection_string=connection_string)
+        connection_string = "DefaultEndpointsProtocol=https;AccountName=test;AccountKey=key"
+        adaptor = AzureStorageAdaptor(
+            container="test-container", connection_string=connection_string
+        )
 
         # Test list
-        files = adaptor.list_files('prefix/')
+        files = adaptor.list_files("prefix/")
 
         assert len(files) == 1
-        assert files[0].key == 'file1.txt'
+        assert files[0].key == "file1.txt"
         assert files[0].size == 100
 
 
@@ -405,53 +416,55 @@ def test_azure_list_files():
 # Base Adaptor Tests
 # ========================================
 
+
 def test_storage_object():
     """Test StorageObject dataclass."""
     obj = StorageObject(
-        key='test.txt',
+        key="test.txt",
         size=100,
-        last_modified='2024-01-01T00:00:00',
-        etag='abc123',
-        metadata={'key': 'value'}
+        last_modified="2024-01-01T00:00:00",
+        etag="abc123",
+        metadata={"key": "value"},
     )
 
-    assert obj.key == 'test.txt'
+    assert obj.key == "test.txt"
     assert obj.size == 100
-    assert obj.metadata == {'key': 'value'}
+    assert obj.metadata == {"key": "value"}
 
 
 def test_base_adaptor_abstract():
     """Test that BaseStorageAdaptor cannot be instantiated."""
     with pytest.raises(TypeError):
-        BaseStorageAdaptor(bucket='test')
+        BaseStorageAdaptor(bucket="test")
 
 
 # ========================================
 # Integration-style Tests
 # ========================================
 
+
 def test_upload_directory():
     """Test directory upload."""
     if not BOTO3_AVAILABLE:
         pytest.skip("boto3 not installed")
 
-    with patch('skill_seekers.cli.storage.s3_storage.boto3') as mock_boto3:
+    with patch("skill_seekers.cli.storage.s3_storage.boto3") as mock_boto3:
         # Setup mocks
         mock_client = Mock()
         mock_boto3.client.return_value = mock_client
         mock_boto3.resource.return_value = Mock()
 
-        adaptor = S3StorageAdaptor(bucket='test-bucket')
+        adaptor = S3StorageAdaptor(bucket="test-bucket")
 
         # Create temporary directory with files
         with tempfile.TemporaryDirectory() as tmp_dir:
-            (Path(tmp_dir) / 'file1.txt').write_text('content1')
-            (Path(tmp_dir) / 'file2.txt').write_text('content2')
-            (Path(tmp_dir) / 'subdir').mkdir()
-            (Path(tmp_dir) / 'subdir' / 'file3.txt').write_text('content3')
+            (Path(tmp_dir) / "file1.txt").write_text("content1")
+            (Path(tmp_dir) / "file2.txt").write_text("content2")
+            (Path(tmp_dir) / "subdir").mkdir()
+            (Path(tmp_dir) / "subdir" / "file3.txt").write_text("content3")
 
             # Test upload directory
-            uploaded_files = adaptor.upload_directory(tmp_dir, 'skills/')
+            uploaded_files = adaptor.upload_directory(tmp_dir, "skills/")
 
             assert len(uploaded_files) == 3
             assert mock_client.upload_file.call_count == 3
@@ -462,25 +475,25 @@ def test_download_directory():
     if not BOTO3_AVAILABLE:
         pytest.skip("boto3 not installed")
 
-    with patch('skill_seekers.cli.storage.s3_storage.boto3') as mock_boto3:
+    with patch("skill_seekers.cli.storage.s3_storage.boto3") as mock_boto3:
         # Setup mocks
         mock_client = Mock()
         mock_paginator = Mock()
         mock_page_iterator = [
             {
-                'Contents': [
+                "Contents": [
                     {
-                        'Key': 'skills/file1.txt',
-                        'Size': 100,
-                        'LastModified': Mock(isoformat=lambda: '2024-01-01T00:00:00'),
-                        'ETag': '"abc"'
+                        "Key": "skills/file1.txt",
+                        "Size": 100,
+                        "LastModified": Mock(isoformat=lambda: "2024-01-01T00:00:00"),
+                        "ETag": '"abc"',
                     },
                     {
-                        'Key': 'skills/file2.txt',
-                        'Size': 200,
-                        'LastModified': Mock(isoformat=lambda: '2024-01-01T00:00:00'),
-                        'ETag': '"def"'
-                    }
+                        "Key": "skills/file2.txt",
+                        "Size": 200,
+                        "LastModified": Mock(isoformat=lambda: "2024-01-01T00:00:00"),
+                        "ETag": '"def"',
+                    },
                 ]
             }
         ]
@@ -490,11 +503,11 @@ def test_download_directory():
         mock_boto3.client.return_value = mock_client
         mock_boto3.resource.return_value = Mock()
 
-        adaptor = S3StorageAdaptor(bucket='test-bucket')
+        adaptor = S3StorageAdaptor(bucket="test-bucket")
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             # Test download directory
-            downloaded_files = adaptor.download_directory('skills/', tmp_dir)
+            downloaded_files = adaptor.download_directory("skills/", tmp_dir)
 
             assert len(downloaded_files) == 2
             assert mock_client.download_file.call_count == 2
diff --git a/tests/test_embedding.py b/tests/test_embedding.py
index b54e664..703734a 100644
--- a/tests/test_embedding.py
+++ b/tests/test_embedding.py
@@ -23,6 +23,7 @@ from skill_seekers.embedding.cache import EmbeddingCache
 # Cache Tests
 # ========================================
 
+
 def test_cache_init():
     """Test cache initialization."""
     cache = EmbeddingCache(":memory:")
@@ -121,6 +122,7 @@ def test_cache_context_manager():
 # Generator Tests
 # ========================================
 
+
 def test_generator_init():
     """Test generator initialization."""
     generator = EmbeddingGenerator()
@@ -174,7 +176,7 @@ def test_generator_compute_hash():
     assert hash1 != hash4
 
 
-@patch('skill_seekers.embedding.generator.SENTENCE_TRANSFORMERS_AVAILABLE', False)
+@patch("skill_seekers.embedding.generator.SENTENCE_TRANSFORMERS_AVAILABLE", False)
 def test_generator_sentence_transformers_not_available():
     """Test sentence-transformers not available."""
     generator = EmbeddingGenerator()
@@ -183,7 +185,7 @@ def test_generator_sentence_transformers_not_available():
         generator.generate("test", model="all-MiniLM-L6-v2")
 
 
-@patch('skill_seekers.embedding.generator.OPENAI_AVAILABLE', False)
+@patch("skill_seekers.embedding.generator.OPENAI_AVAILABLE", False)
 def test_generator_openai_not_available():
     """Test OpenAI not available."""
     generator = EmbeddingGenerator()
@@ -192,7 +194,7 @@ def test_generator_openai_not_available():
         generator.generate("test", model="text-embedding-3-small")
 
 
-@patch('skill_seekers.embedding.generator.VOYAGE_AVAILABLE', False)
+@patch("skill_seekers.embedding.generator.VOYAGE_AVAILABLE", False)
 def test_generator_voyage_not_available():
     """Test Voyage AI not available."""
     generator = EmbeddingGenerator()
@@ -227,13 +229,10 @@ def test_generator_voyage_large_2_model_info():
 # Model Tests
 # ========================================
 
+
 def test_embedding_request():
     """Test EmbeddingRequest model."""
-    request = EmbeddingRequest(
-        text="Hello world",
-        model="text-embedding-3-small",
-        normalize=True
-    )
+    request = EmbeddingRequest(text="Hello world", model="text-embedding-3-small", normalize=True)
 
     assert request.text == "Hello world"
     assert request.model == "text-embedding-3-small"
@@ -243,9 +242,7 @@ def test_embedding_request():
 def test_batch_embedding_request():
     """Test BatchEmbeddingRequest model."""
     request = BatchEmbeddingRequest(
-        texts=["text1", "text2", "text3"],
-        model="text-embedding-3-small",
-        batch_size=32
+        texts=["text1", "text2", "text3"], model="text-embedding-3-small", batch_size=32
     )
 
     assert len(request.texts) == 3
@@ -255,10 +252,7 @@ def test_batch_embedding_request():
 def test_embedding_response():
     """Test EmbeddingResponse model."""
     response = EmbeddingResponse(
-        embedding=[0.1, 0.2, 0.3],
-        model="test-model",
-        dimensions=3,
-        cached=False
+        embedding=[0.1, 0.2, 0.3], model="test-model", dimensions=3, cached=False
     )
 
     assert len(response.embedding) == 3
@@ -273,7 +267,7 @@ def test_batch_embedding_response():
         model="test-model",
         dimensions=2,
         count=2,
-        cached_count=1
+        cached_count=1,
     )
 
     assert len(response.embeddings) == 2
@@ -288,7 +282,7 @@ def test_health_response():
         version="1.0.0",
         models=["model1", "model2"],
         cache_enabled=True,
-        cache_size=100
+        cache_size=100,
     )
 
     assert response.status == "ok"
@@ -303,7 +297,7 @@ def test_model_info():
         provider="openai",
         dimensions=1536,
         max_tokens=8191,
-        cost_per_million=0.02
+        cost_per_million=0.02,
     )
 
     assert info.name == "test-model"
@@ -315,6 +309,7 @@ def test_model_info():
 # Integration Tests
 # ========================================
 
+
 def test_cache_batch_operations():
     """Test cache batch operations."""
     cache = EmbeddingCache(":memory:")
diff --git a/tests/test_embedding_pipeline.py b/tests/test_embedding_pipeline.py
index f7d316f..48a34fb 100644
--- a/tests/test_embedding_pipeline.py
+++ b/tests/test_embedding_pipeline.py
@@ -23,7 +23,7 @@ from skill_seekers.cli.embedding_pipeline import (
     EmbeddingPipeline,
     LocalEmbeddingProvider,
     EmbeddingCache,
-    CostTracker
+    CostTracker,
 )
 
 
@@ -112,21 +112,16 @@ def test_cost_tracker():
 
     stats = tracker.get_stats()
 
-    assert stats['total_requests'] == 2
-    assert stats['total_tokens'] == 1500
-    assert stats['cache_hits'] == 1
-    assert stats['cache_misses'] == 1
-    assert '50.0%' in stats['cache_rate']
+    assert stats["total_requests"] == 2
+    assert stats["total_tokens"] == 1500
+    assert stats["cache_hits"] == 1
+    assert stats["cache_misses"] == 1
+    assert "50.0%" in stats["cache_rate"]
 
 
 def test_pipeline_initialization():
     """Test pipeline initialization."""
-    config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
-        dimension=128,
-        batch_size=10
-    )
+    config = EmbeddingConfig(provider="local", model="test-model", dimension=128, batch_size=10)
 
     pipeline = EmbeddingPipeline(config)
 
@@ -137,12 +132,7 @@ def test_pipeline_initialization():
 
 def test_pipeline_generate_batch():
     """Test batch embedding generation."""
-    config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
-        dimension=64,
-        batch_size=2
-    )
+    config = EmbeddingConfig(provider="local", model="test-model", dimension=64, batch_size=2)
 
     pipeline = EmbeddingPipeline(config)
 
@@ -159,11 +149,11 @@ def test_pipeline_caching():
     """Test pipeline uses caching."""
     with tempfile.TemporaryDirectory() as tmpdir:
         config = EmbeddingConfig(
-            provider='local',
-            model='test-model',
+            provider="local",
+            model="test-model",
             dimension=32,
             batch_size=10,
-            cache_dir=Path(tmpdir)
+            cache_dir=Path(tmpdir),
         )
 
         pipeline = EmbeddingPipeline(config)
@@ -184,10 +174,10 @@ def test_pipeline_caching():
 def test_pipeline_batch_processing():
     """Test large batch is processed in chunks."""
     config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
+        provider="local",
+        model="test-model",
         dimension=16,
-        batch_size=3  # Small batch size
+        batch_size=3,  # Small batch size
     )
 
     pipeline = EmbeddingPipeline(config)
@@ -201,11 +191,7 @@ def test_pipeline_batch_processing():
 
 def test_validate_dimensions_valid():
     """Test dimension validation with valid embeddings."""
-    config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
-        dimension=128
-    )
+    config = EmbeddingConfig(provider="local", model="test-model", dimension=128)
 
     pipeline = EmbeddingPipeline(config)
 
@@ -217,11 +203,7 @@ def test_validate_dimensions_valid():
 
 def test_validate_dimensions_invalid():
     """Test dimension validation with invalid embeddings."""
-    config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
-        dimension=128
-    )
+    config = EmbeddingConfig(provider="local", model="test-model", dimension=128)
 
     pipeline = EmbeddingPipeline(config)
 
@@ -234,30 +216,22 @@ def test_validate_dimensions_invalid():
 
 def test_embedding_result_metadata():
     """Test embedding result includes metadata."""
-    config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
-        dimension=256
-    )
+    config = EmbeddingConfig(provider="local", model="test-model", dimension=256)
 
     pipeline = EmbeddingPipeline(config)
 
     texts = ["test"]
     result = pipeline.generate_batch(texts, show_progress=False)
 
-    assert 'provider' in result.metadata
-    assert 'model' in result.metadata
-    assert 'dimension' in result.metadata
-    assert result.metadata['dimension'] == 256
+    assert "provider" in result.metadata
+    assert "model" in result.metadata
+    assert "dimension" in result.metadata
+    assert result.metadata["dimension"] == 256
 
 
 def test_cost_stats():
     """Test cost statistics tracking."""
-    config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
-        dimension=64
-    )
+    config = EmbeddingConfig(provider="local", model="test-model", dimension=64)
 
     pipeline = EmbeddingPipeline(config)
 
@@ -266,18 +240,14 @@ def test_cost_stats():
 
     stats = pipeline.get_cost_stats()
 
-    assert 'total_requests' in stats
-    assert 'cache_hits' in stats
-    assert 'estimated_cost' in stats
+    assert "total_requests" in stats
+    assert "cache_hits" in stats
+    assert "estimated_cost" in stats
 
 
 def test_empty_batch():
     """Test handling empty batch."""
-    config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
-        dimension=32
-    )
+    config = EmbeddingConfig(provider="local", model="test-model", dimension=32)
 
     pipeline = EmbeddingPipeline(config)
 
@@ -289,11 +259,7 @@ def test_empty_batch():
 
 def test_single_document():
     """Test single document generation."""
-    config = EmbeddingConfig(
-        provider='local',
-        model='test-model',
-        dimension=128
-    )
+    config = EmbeddingConfig(provider="local", model="test-model", dimension=128)
 
     pipeline = EmbeddingPipeline(config)
 
@@ -306,11 +272,7 @@ def test_single_document():
 def test_different_dimensions():
     """Test different embedding dimensions."""
     for dim in [64, 128, 256, 512]:
-        config = EmbeddingConfig(
-            provider='local',
-            model='test-model',
-            dimension=dim
-        )
+        config = EmbeddingConfig(provider="local", model="test-model", dimension=dim)
 
         pipeline = EmbeddingPipeline(config)
         result = pipeline.generate_batch(["test"], show_progress=False)
diff --git a/tests/test_enhance_skill_local.py b/tests/test_enhance_skill_local.py
index 1d6c712..522c2d8 100644
--- a/tests/test_enhance_skill_local.py
+++ b/tests/test_enhance_skill_local.py
@@ -152,9 +152,7 @@ class TestMultiAgentSupport:
 
     def test_rejects_missing_executable(self, tmp_path, monkeypatch):
         """Test rejection when executable is not found on PATH."""
-        monkeypatch.setattr(
-            "skill_seekers.cli.enhance_skill_local.shutil.which", lambda _exe: None
-        )
+        monkeypatch.setattr("skill_seekers.cli.enhance_skill_local.shutil.which", lambda _exe: None)
         skill_dir = _make_skill_dir(tmp_path)
 
         with pytest.raises(ValueError, match="not found in PATH"):
diff --git a/tests/test_framework_detection.py b/tests/test_framework_detection.py
index b08fbfc..49c41e3 100644
--- a/tests/test_framework_detection.py
+++ b/tests/test_framework_detection.py
@@ -80,8 +80,9 @@ class TestFrameworkDetection(unittest.TestCase):
             arch_data = json.load(f)
 
         self.assertIn("frameworks_detected", arch_data)
-        self.assertIn("Flask", arch_data["frameworks_detected"],
-                      "Flask should be detected from imports")
+        self.assertIn(
+            "Flask", arch_data["frameworks_detected"], "Flask should be detected from imports"
+        )
 
     def test_files_with_imports_are_included(self):
         """Test that files with only imports are included in analysis (Issue #239)."""
@@ -119,24 +120,19 @@ class TestFrameworkDetection(unittest.TestCase):
             analysis_data = json.load(f)
 
         # File should be included
-        self.assertGreater(len(analysis_data["files"]), 0,
-                           "Files with imports should be included")
+        self.assertGreater(len(analysis_data["files"]), 0, "Files with imports should be included")
 
         # Find our import-only file
         import_file = next(
-            (f for f in analysis_data["files"] if "imports_only.py" in f["file"]),
-            None
+            (f for f in analysis_data["files"] if "imports_only.py" in f["file"]), None
         )
         self.assertIsNotNone(import_file, "Import-only file should be in analysis")
 
         # Verify imports were extracted
         self.assertIn("imports", import_file, "Imports should be extracted")
-        self.assertGreater(len(import_file["imports"]), 0,
-                           "Should have captured imports")
-        self.assertIn("django", import_file["imports"],
-                      "Django import should be captured")
-        self.assertIn("flask", import_file["imports"],
-                      "Flask import should be captured")
+        self.assertGreater(len(import_file["imports"]), 0, "Should have captured imports")
+        self.assertIn("django", import_file["imports"], "Django import should be captured")
+        self.assertIn("flask", import_file["imports"], "Flask import should be captured")
 
     def test_no_false_positive_frameworks(self):
         """Test that framework detection doesn't produce false positives (Issue #239)."""
@@ -145,10 +141,7 @@ class TestFrameworkDetection(unittest.TestCase):
         app_dir.mkdir()
 
         # File with no framework imports
-        (app_dir / "utils.py").write_text(
-            "def my_function():\n"
-            "    return 'hello'\n"
-        )
+        (app_dir / "utils.py").write_text("def my_function():\n    return 'hello'\n")
 
         # Run codebase analyzer
         from skill_seekers.cli.codebase_scraper import main as scraper_main
@@ -180,12 +173,10 @@ class TestFrameworkDetection(unittest.TestCase):
 
             frameworks = arch_data.get("frameworks_detected", [])
             # Should not detect Flask just from "app" directory name
-            self.assertNotIn("Flask", frameworks,
-                             "Should not detect Flask without imports")
+            self.assertNotIn("Flask", frameworks, "Should not detect Flask without imports")
             # Should not detect other frameworks with "app" in markers
             for fw in ["ASP.NET", "Rails", "Laravel"]:
-                self.assertNotIn(fw, frameworks,
-                                 f"Should not detect {fw} without real evidence")
+                self.assertNotIn(fw, frameworks, f"Should not detect {fw} without real evidence")
 
 
 if __name__ == "__main__":
diff --git a/tests/test_incremental_updates.py b/tests/test_incremental_updates.py
index dc945e8..818c4a8 100644
--- a/tests/test_incremental_updates.py
+++ b/tests/test_incremental_updates.py
@@ -20,9 +20,7 @@ import time
 # Add src to path
 sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
 
-from skill_seekers.cli.incremental_updater import (
-    IncrementalUpdater
-)
+from skill_seekers.cli.incremental_updater import IncrementalUpdater
 
 
 @pytest.fixture
@@ -281,15 +279,15 @@ def test_apply_update_package(temp_skill_dir):
                 "timestamp": "2026-02-05T12:00:00",
                 "skill_name": "test_skill",
                 "change_summary": {"modified": 1},
-                "total_changes": 1
+                "total_changes": 1,
             },
             "changes": {
                 "SKILL.md": {
                     "action": "modify",
                     "version": 2,
-                    "content": "# Updated Content\n\nApplied from package"
+                    "content": "# Updated Content\n\nApplied from package",
                 }
-            }
+            },
         }
 
         package_path.write_text(json.dumps(update_data))
@@ -298,7 +296,9 @@ def test_apply_update_package(temp_skill_dir):
         success = updater.apply_update_package(package_path)
 
         assert success
-        assert (temp_skill_dir / "SKILL.md").read_text() == "# Updated Content\n\nApplied from package"
+        assert (
+            temp_skill_dir / "SKILL.md"
+        ).read_text() == "# Updated Content\n\nApplied from package"
 
 
 def test_content_hash_consistency(temp_skill_dir):
diff --git a/tests/test_integration.py b/tests/test_integration.py
index 906b421..cca16bb 100644
--- a/tests/test_integration.py
+++ b/tests/test_integration.py
@@ -92,7 +92,11 @@ class TestConfigLoading(unittest.TestCase):
                 {
                     "type": "documentation",
                     "base_url": "https://example.com/",
-                    "selectors": {"main_content": "article", "title": "h1", "code_blocks": "pre code"},
+                    "selectors": {
+                        "main_content": "article",
+                        "title": "h1",
+                        "code_blocks": "pre code",
+                    },
                     "rate_limit": 0.5,
                     "max_pages": 100,
                 }
diff --git a/tests/test_integration_adaptors.py b/tests/test_integration_adaptors.py
index 59b73a1..fb335c7 100644
--- a/tests/test_integration_adaptors.py
+++ b/tests/test_integration_adaptors.py
@@ -113,6 +113,7 @@ def check_service_available(url: str, timeout: int = 5) -> bool:
     """Check if a service is available."""
     try:
         import requests
+
         response = requests.get(url, timeout=timeout)
         return response.status_code == 200
     except Exception:
@@ -133,7 +134,9 @@ class TestWeaviateIntegration:
 
         # Check if Weaviate is running
         if not check_service_available("http://localhost:8080/v1/.well-known/ready"):
-            pytest.skip("Weaviate not running (start with: docker-compose -f tests/docker-compose.test.yml up -d)")
+            pytest.skip(
+                "Weaviate not running (start with: docker-compose -f tests/docker-compose.test.yml up -d)"
+            )
 
         # Connect to Weaviate
         try:
@@ -144,10 +147,7 @@ class TestWeaviateIntegration:
 
         # Package skill
         adaptor = get_adaptor("weaviate")
-        SkillMetadata(
-            name="integration_test",
-            description="Integration test skill for Weaviate"
-        )
+        SkillMetadata(name="integration_test", description="Integration test skill for Weaviate")
         package_path = adaptor.package(sample_skill_dir, tmp_path)
 
         assert package_path.exists(), "Package not created"
@@ -173,19 +173,16 @@ class TestWeaviateIntegration:
             with client.batch as batch:
                 for obj in data["objects"]:
                     batch.add_data_object(
-                        data_object=obj["properties"],
-                        class_name=class_name,
-                        uuid=obj["id"]
+                        data_object=obj["properties"], class_name=class_name, uuid=obj["id"]
                     )
 
             # Wait for indexing
             time.sleep(1)
 
             # Query - Get all objects
-            result = client.query.get(
-                class_name,
-                ["content", "source", "category"]
-            ).with_limit(10).do()
+            result = (
+                client.query.get(class_name, ["content", "source", "category"]).with_limit(10).do()
+            )
 
             # Verify results
             assert "data" in result, "Query returned no data"
@@ -203,8 +200,9 @@ class TestWeaviateIntegration:
 
             # Verify content
             contents = [obj["content"] for obj in objects]
-            assert any("vector" in content.lower() for content in contents), \
+            assert any("vector" in content.lower() for content in contents), (
                 "Expected content not found"
+            )
 
         finally:
             # Cleanup - Delete collection
@@ -234,7 +232,7 @@ class TestWeaviateIntegration:
             description="Test metadata preservation",
             version="2.0.0",
             author="Integration Test Suite",
-            tags=["test", "integration", "weaviate"]
+            tags=["test", "integration", "weaviate"],
         )
         package_path = adaptor.package(sample_skill_dir, tmp_path)
 
@@ -249,18 +247,17 @@ class TestWeaviateIntegration:
             with client.batch as batch:
                 for obj in data["objects"]:
                     batch.add_data_object(
-                        data_object=obj["properties"],
-                        class_name=class_name,
-                        uuid=obj["id"]
+                        data_object=obj["properties"], class_name=class_name, uuid=obj["id"]
                     )
 
             time.sleep(1)
 
             # Query and verify metadata
-            result = client.query.get(
-                class_name,
-                ["source", "version", "author", "tags"]
-            ).with_limit(1).do()
+            result = (
+                client.query.get(class_name, ["source", "version", "author", "tags"])
+                .with_limit(1)
+                .do()
+            )
 
             obj = result["data"]["Get"][class_name][0]
             assert obj["source"] == "metadata_test", "Source not preserved"
@@ -287,7 +284,9 @@ class TestChromaIntegration:
 
         # Check if Chroma is running
         if not check_service_available("http://localhost:8000/api/v1/heartbeat"):
-            pytest.skip("ChromaDB not running (start with: docker-compose -f tests/docker-compose.test.yml up -d)")
+            pytest.skip(
+                "ChromaDB not running (start with: docker-compose -f tests/docker-compose.test.yml up -d)"
+            )
 
         # Connect to ChromaDB
         try:
@@ -299,8 +298,7 @@ class TestChromaIntegration:
         # Package skill
         adaptor = get_adaptor("chroma")
         SkillMetadata(
-            name="chroma_integration_test",
-            description="Integration test skill for ChromaDB"
+            name="chroma_integration_test", description="Integration test skill for ChromaDB"
         )
         package_path = adaptor.package(sample_skill_dir, tmp_path)
 
@@ -326,9 +324,7 @@ class TestChromaIntegration:
 
             # Add documents
             collection.add(
-                documents=data["documents"],
-                metadatas=data["metadatas"],
-                ids=data["ids"]
+                documents=data["documents"], metadatas=data["metadatas"], ids=data["ids"]
             )
 
             # Wait for indexing
@@ -340,8 +336,7 @@ class TestChromaIntegration:
             # Verify results
             assert "documents" in results, "Query returned no documents"
             assert len(results["documents"]) > 0, "No documents returned"
-            assert len(results["documents"]) == len(data["documents"]), \
-                "Document count mismatch"
+            assert len(results["documents"]) == len(data["documents"]), "Document count mismatch"
 
             # Verify metadata
             assert "metadatas" in results, "Query returned no metadatas"
@@ -350,8 +345,9 @@ class TestChromaIntegration:
             assert "category" in first_metadata, "Missing category in metadata"
 
             # Verify content
-            assert any("vector" in doc.lower() for doc in results["documents"]), \
+            assert any("vector" in doc.lower() for doc in results["documents"]), (
                 "Expected content not found"
+            )
 
         finally:
             # Cleanup - Delete collection
@@ -377,8 +373,7 @@ class TestChromaIntegration:
         # Package and upload
         adaptor = get_adaptor("chroma")
         metadata = SkillMetadata(
-            name="chroma_filter_test",
-            description="Test filtering capabilities"
+            name="chroma_filter_test", description="Test filtering capabilities"
         )
         package_path = adaptor.package(sample_skill_dir, tmp_path)
 
@@ -390,23 +385,18 @@ class TestChromaIntegration:
         try:
             collection = client.get_or_create_collection(name=collection_name)
             collection.add(
-                documents=data["documents"],
-                metadatas=data["metadatas"],
-                ids=data["ids"]
+                documents=data["documents"], metadatas=data["metadatas"], ids=data["ids"]
             )
 
             time.sleep(1)
 
             # Query with category filter
-            results = collection.get(
-                where={"category": "getting started"}
-            )
+            results = collection.get(where={"category": "getting started"})
 
             # Verify filtering worked
             assert len(results["documents"]) > 0, "No documents matched filter"
             for metadata in results["metadatas"]:
-                assert metadata["category"] == "getting started", \
-                    "Filter returned wrong category"
+                assert metadata["category"] == "getting started", "Filter returned wrong category"
 
         finally:
             with contextlib.suppress(Exception):
@@ -428,7 +418,9 @@ class TestQdrantIntegration:
 
         # Check if Qdrant is running
         if not check_service_available("http://localhost:6333/"):
-            pytest.skip("Qdrant not running (start with: docker-compose -f tests/docker-compose.test.yml up -d)")
+            pytest.skip(
+                "Qdrant not running (start with: docker-compose -f tests/docker-compose.test.yml up -d)"
+            )
 
         # Connect to Qdrant
         try:
@@ -440,8 +432,7 @@ class TestQdrantIntegration:
         # Package skill
         adaptor = get_adaptor("qdrant")
         SkillMetadata(
-            name="qdrant_integration_test",
-            description="Integration test skill for Qdrant"
+            name="qdrant_integration_test", description="Integration test skill for Qdrant"
         )
         package_path = adaptor.package(sample_skill_dir, tmp_path)
 
@@ -465,25 +456,21 @@ class TestQdrantIntegration:
             # Create collection
             client.create_collection(
                 collection_name=collection_name,
-                vectors_config=VectorParams(
-                    size=vector_size,
-                    distance=Distance.COSINE
-                )
+                vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
             )
 
             # Upload points (with placeholder vectors for testing)
             points = []
             for point in data["points"]:
-                points.append(PointStruct(
-                    id=point["id"],
-                    vector=[0.0] * vector_size,  # Placeholder vectors
-                    payload=point["payload"]
-                ))
+                points.append(
+                    PointStruct(
+                        id=point["id"],
+                        vector=[0.0] * vector_size,  # Placeholder vectors
+                        payload=point["payload"],
+                    )
+                )
 
-            client.upsert(
-                collection_name=collection_name,
-                points=points
-            )
+            client.upsert(collection_name=collection_name, points=points)
 
             # Wait for indexing
             time.sleep(1)
@@ -493,14 +480,10 @@ class TestQdrantIntegration:
 
             # Verify collection
             assert collection_info.points_count > 0, "No points in collection"
-            assert collection_info.points_count == len(data["points"]), \
-                "Point count mismatch"
+            assert collection_info.points_count == len(data["points"]), "Point count mismatch"
 
             # Query - Scroll through points
-            scroll_result = client.scroll(
-                collection_name=collection_name,
-                limit=10
-            )
+            scroll_result = client.scroll(collection_name=collection_name, limit=10)
 
             points_list = scroll_result[0]
             assert len(points_list) > 0, "No points returned"
@@ -514,8 +497,9 @@ class TestQdrantIntegration:
 
             # Verify content
             contents = [p.payload["content"] for p in points_list]
-            assert any("vector" in content.lower() for content in contents), \
+            assert any("vector" in content.lower() for content in contents), (
                 "Expected content not found"
+            )
 
         finally:
             # Cleanup - Delete collection
@@ -527,8 +511,12 @@ class TestQdrantIntegration:
         try:
             from qdrant_client import QdrantClient
             from qdrant_client.models import (
-                Distance, VectorParams, PointStruct,
-                Filter, FieldCondition, MatchValue
+                Distance,
+                VectorParams,
+                PointStruct,
+                Filter,
+                FieldCondition,
+                MatchValue,
             )
         except ImportError:
             pytest.skip("qdrant-client not installed")
@@ -544,10 +532,7 @@ class TestQdrantIntegration:
 
         # Package and upload
         adaptor = get_adaptor("qdrant")
-        SkillMetadata(
-            name="qdrant_filter_test",
-            description="Test filtering capabilities"
-        )
+        SkillMetadata(name="qdrant_filter_test", description="Test filtering capabilities")
         package_path = adaptor.package(sample_skill_dir, tmp_path)
 
         with open(package_path) as f:
@@ -560,19 +545,16 @@ class TestQdrantIntegration:
             # Create and upload
             client.create_collection(
                 collection_name=collection_name,
-                vectors_config=VectorParams(
-                    size=vector_size,
-                    distance=Distance.COSINE
-                )
+                vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
             )
 
             points = []
             for point in data["points"]:
-                points.append(PointStruct(
-                    id=point["id"],
-                    vector=[0.0] * vector_size,
-                    payload=point["payload"]
-                ))
+                points.append(
+                    PointStruct(
+                        id=point["id"], vector=[0.0] * vector_size, payload=point["payload"]
+                    )
+                )
 
             client.upsert(collection_name=collection_name, points=points)
             time.sleep(1)
@@ -581,14 +563,9 @@ class TestQdrantIntegration:
             scroll_result = client.scroll(
                 collection_name=collection_name,
                 scroll_filter=Filter(
-                    must=[
-                        FieldCondition(
-                            key="type",
-                            match=MatchValue(value="reference")
-                        )
-                    ]
+                    must=[FieldCondition(key="type", match=MatchValue(value="reference"))]
                 ),
-                limit=10
+                limit=10,
             )
 
             points_list = scroll_result[0]
@@ -596,8 +573,7 @@ class TestQdrantIntegration:
             # Verify filtering worked
             assert len(points_list) > 0, "No points matched filter"
             for point in points_list:
-                assert point.payload["type"] == "reference", \
-                    "Filter returned wrong type"
+                assert point.payload["type"] == "reference", "Filter returned wrong type"
 
         finally:
             with contextlib.suppress(Exception):
@@ -607,4 +583,5 @@ class TestQdrantIntegration:
 if __name__ == "__main__":
     # Run integration tests
     import sys
+
     sys.exit(pytest.main([__file__, "-v", "-m", "integration"]))
diff --git a/tests/test_issue_277_real_world.py b/tests/test_issue_277_real_world.py
index 263764a..1c15f30 100644
--- a/tests/test_issue_277_real_world.py
+++ b/tests/test_issue_277_real_world.py
@@ -192,9 +192,7 @@ https://mikro-orm.io/docs/defining-entities#formulas
 
         # Verify converted URLs are valid
         # In real scenario, these would be added to pending_urls and scraped
-        self.assertTrue(
-            len(converted_urls) > 0, "Should generate at least one URL to scrape"
-        )
+        self.assertTrue(len(converted_urls) > 0, "Should generate at least one URL to scrape")
 
         # Verify no URLs would cause 404 (no anchors in middle of path)
         for url in converted_urls:
diff --git a/tests/test_mcp_server.py b/tests/test_mcp_server.py
index f81fc21..b58df83 100644
--- a/tests/test_mcp_server.py
+++ b/tests/test_mcp_server.py
@@ -464,13 +464,15 @@ class TestValidateConfigTool(unittest.IsolatedAsyncioTestCase):
         valid_config = {
             "name": "valid-test",
             "description": "Test configuration",
-            "sources": [{
-                "type": "documentation",
-                "base_url": "https://example.com/",
-                "selectors": {"main_content": "article", "title": "h1", "code_blocks": "pre"},
-                "rate_limit": 0.5,
-                "max_pages": 100,
-            }],
+            "sources": [
+                {
+                    "type": "documentation",
+                    "base_url": "https://example.com/",
+                    "selectors": {"main_content": "article", "title": "h1", "code_blocks": "pre"},
+                    "rate_limit": 0.5,
+                    "max_pages": 100,
+                }
+            ],
         }
         with open(config_path, "w") as f:
             json.dump(valid_config, f)
diff --git a/tests/test_multilang_support.py b/tests/test_multilang_support.py
index 7c22271..6127692 100644
--- a/tests/test_multilang_support.py
+++ b/tests/test_multilang_support.py
@@ -19,10 +19,7 @@ import json
 # Add src to path
 sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
 
-from skill_seekers.cli.multilang_support import (
-    LanguageDetector,
-    MultiLanguageManager
-)
+from skill_seekers.cli.multilang_support import LanguageDetector, MultiLanguageManager
 
 
 def test_detect_english():
@@ -32,8 +29,8 @@ def test_detect_english():
     text = "This is an English document. It contains common English words."
     lang_info = detector.detect(text)
 
-    assert lang_info.code == 'en'
-    assert lang_info.name == 'English'
+    assert lang_info.code == "en"
+    assert lang_info.name == "English"
     assert lang_info.confidence > 0.0
 
 
@@ -44,8 +41,8 @@ def test_detect_spanish():
     text = "Este es un documento en español. Contiene palabras comunes en español."
     lang_info = detector.detect(text)
 
-    assert lang_info.code == 'es'
-    assert lang_info.name == 'Spanish'
+    assert lang_info.code == "es"
+    assert lang_info.name == "Spanish"
 
 
 def test_detect_french():
@@ -55,8 +52,8 @@ def test_detect_french():
     text = "Ceci est un document en français. Il contient des mots français communs."
     lang_info = detector.detect(text)
 
-    assert lang_info.code == 'fr'
-    assert lang_info.name == 'French'
+    assert lang_info.code == "fr"
+    assert lang_info.name == "French"
 
 
 def test_detect_german():
@@ -66,8 +63,8 @@ def test_detect_german():
     text = "Dies ist ein deutsches Dokument. Es enthält übliche deutsche Wörter."
     lang_info = detector.detect(text)
 
-    assert lang_info.code == 'de'
-    assert lang_info.name == 'German'
+    assert lang_info.code == "de"
+    assert lang_info.name == "German"
 
 
 def test_detect_chinese():
@@ -77,33 +74,33 @@ def test_detect_chinese():
     text = "这是一个中文文档。它包含常见的中文字符。"
     lang_info = detector.detect(text)
 
-    assert lang_info.code == 'zh'
-    assert lang_info.name == 'Chinese'
+    assert lang_info.code == "zh"
+    assert lang_info.name == "Chinese"
 
 
 def test_detect_from_filename_dot_pattern():
     """Test language detection from filename (file.en.md pattern)."""
     detector = LanguageDetector()
 
-    assert detector.detect_from_filename("README.en.md") == 'en'
-    assert detector.detect_from_filename("guide.es.md") == 'es'
-    assert detector.detect_from_filename("doc.fr.md") == 'fr'
+    assert detector.detect_from_filename("README.en.md") == "en"
+    assert detector.detect_from_filename("guide.es.md") == "es"
+    assert detector.detect_from_filename("doc.fr.md") == "fr"
 
 
 def test_detect_from_filename_underscore_pattern():
     """Test language detection from filename (file_en.md pattern)."""
     detector = LanguageDetector()
 
-    assert detector.detect_from_filename("README_en.md") == 'en'
-    assert detector.detect_from_filename("guide_es.md") == 'es'
+    assert detector.detect_from_filename("README_en.md") == "en"
+    assert detector.detect_from_filename("guide_es.md") == "es"
 
 
 def test_detect_from_filename_dash_pattern():
     """Test language detection from filename (file-en.md pattern)."""
     detector = LanguageDetector()
 
-    assert detector.detect_from_filename("README-en.md") == 'en'
-    assert detector.detect_from_filename("guide-es.md") == 'es'
+    assert detector.detect_from_filename("README-en.md") == "en"
+    assert detector.detect_from_filename("guide-es.md") == "es"
 
 
 def test_detect_from_filename_no_match():
@@ -118,15 +115,11 @@ def test_add_document_single_language():
     """Test adding documents in single language."""
     manager = MultiLanguageManager()
 
-    manager.add_document(
-        "README.md",
-        "This is an English document.",
-        {"category": "overview"}
-    )
+    manager.add_document("README.md", "This is an English document.", {"category": "overview"})
 
     assert len(manager.get_languages()) == 1
-    assert 'en' in manager.get_languages()
-    assert manager.get_document_count('en') == 1
+    assert "en" in manager.get_languages()
+    assert manager.get_document_count("en") == 1
 
 
 def test_add_document_multiple_languages():
@@ -138,9 +131,9 @@ def test_add_document_multiple_languages():
     manager.add_document("README.fr.md", "Ceci est français.", {})
 
     assert len(manager.get_languages()) == 3
-    assert 'en' in manager.get_languages()
-    assert 'es' in manager.get_languages()
-    assert 'fr' in manager.get_languages()
+    assert "en" in manager.get_languages()
+    assert "es" in manager.get_languages()
+    assert "fr" in manager.get_languages()
 
 
 def test_force_language():
@@ -148,15 +141,10 @@ def test_force_language():
     manager = MultiLanguageManager()
 
     # Force Spanish despite English content
-    manager.add_document(
-        "file.md",
-        "This is actually English content.",
-        {},
-        force_language='es'
-    )
+    manager.add_document("file.md", "This is actually English content.", {}, force_language="es")
 
-    assert 'es' in manager.get_languages()
-    assert manager.get_document_count('es') == 1
+    assert "es" in manager.get_languages()
+    assert manager.get_document_count("es") == 1
 
 
 def test_filename_language_priority():
@@ -164,14 +152,10 @@ def test_filename_language_priority():
     manager = MultiLanguageManager()
 
     # Filename says Spanish, but content is English
-    manager.add_document(
-        "guide.es.md",
-        "This is English content.",
-        {}
-    )
+    manager.add_document("guide.es.md", "This is English content.", {})
 
     # Should use filename language
-    assert 'es' in manager.get_languages()
+    assert "es" in manager.get_languages()
 
 
 def test_document_count_all():
@@ -183,8 +167,8 @@ def test_document_count_all():
     manager.add_document("file3.es.md", "Spanish doc", {})
 
     assert manager.get_document_count() == 3
-    assert manager.get_document_count('en') == 2
-    assert manager.get_document_count('es') == 1
+    assert manager.get_document_count("en") == 2
+    assert manager.get_document_count("es") == 1
 
 
 def test_primary_language():
@@ -195,7 +179,7 @@ def test_primary_language():
     manager.add_document("file2.es.md", "Spanish doc", {})
 
     # Primary should be first added
-    assert manager.primary_language == 'en'
+    assert manager.primary_language == "en"
 
 
 def test_translation_status():
@@ -208,9 +192,9 @@ def test_translation_status():
 
     status = manager.get_translation_status()
 
-    assert status.source_language == 'en'
-    assert 'es' in status.translated_languages
-    assert 'fr' in status.translated_languages
+    assert status.source_language == "en"
+    assert "es" in status.translated_languages
+    assert "fr" in status.translated_languages
     assert len(status.translated_languages) == 2
 
 
@@ -225,17 +209,17 @@ def test_export_by_language():
         exports = manager.export_by_language(Path(tmpdir))
 
         assert len(exports) == 2
-        assert 'en' in exports
-        assert 'es' in exports
+        assert "en" in exports
+        assert "es" in exports
 
         # Check files exist
-        assert exports['en'].exists()
-        assert exports['es'].exists()
+        assert exports["en"].exists()
+        assert exports["es"].exists()
 
         # Check content
-        en_data = json.loads(exports['en'].read_text())
-        assert en_data['language'] == 'en'
-        assert en_data['document_count'] == 1
+        en_data = json.loads(exports["en"].read_text())
+        assert en_data["language"] == "en"
+        assert en_data["document_count"] == 1
 
 
 def test_translation_report_generation():
@@ -268,11 +252,11 @@ def test_script_detection():
 
     # English uses Latin script
     en_info = detector.detect("This is English")
-    assert en_info.script == 'Latin'
+    assert en_info.script == "Latin"
 
     # Chinese uses Han script
     zh_info = detector.detect("这是中文")
-    assert zh_info.script == 'Han'
+    assert zh_info.script == "Han"
 
 
 def test_confidence_scoring():
@@ -283,7 +267,7 @@ def test_confidence_scoring():
     strong_en = "The quick brown fox jumps over the lazy dog. This is clearly English."
     lang_info = detector.detect(strong_en)
 
-    assert lang_info.code == 'en'
+    assert lang_info.code == "en"
     assert lang_info.confidence > 0.3  # Should have decent confidence
 
 
@@ -294,9 +278,9 @@ def test_metadata_preservation():
     metadata = {"category": "guide", "version": "1.0"}
     manager.add_document("file.md", "English content", metadata)
 
-    docs = manager.documents['en']
+    docs = manager.documents["en"]
     assert len(docs) == 1
-    assert docs[0]['metadata'] == metadata
+    assert docs[0]["metadata"] == metadata
 
 
 if __name__ == "__main__":
diff --git a/tests/test_preset_system.py b/tests/test_preset_system.py
index 6626ed3..3f28529 100644
--- a/tests/test_preset_system.py
+++ b/tests/test_preset_system.py
@@ -14,9 +14,9 @@ class TestPresetDefinitions:
 
     def test_all_presets_defined(self):
         """Test that all expected presets are defined."""
-        assert 'quick' in PRESETS
-        assert 'standard' in PRESETS
-        assert 'comprehensive' in PRESETS
+        assert "quick" in PRESETS
+        assert "standard" in PRESETS
+        assert "comprehensive" in PRESETS
         assert len(PRESETS) == 3
 
     def test_preset_structure(self):
@@ -25,7 +25,7 @@ class TestPresetDefinitions:
             assert isinstance(preset, AnalysisPreset)
             assert preset.name
             assert preset.description
-            assert preset.depth in ['surface', 'deep', 'full']
+            assert preset.depth in ["surface", "deep", "full"]
             assert isinstance(preset.features, dict)
             assert 0 <= preset.enhance_level <= 3
             assert preset.estimated_time
@@ -33,45 +33,45 @@ class TestPresetDefinitions:
 
     def test_quick_preset(self):
         """Test quick preset configuration."""
-        quick = PRESETS['quick']
-        assert quick.name == 'Quick'
-        assert quick.depth == 'surface'
+        quick = PRESETS["quick"]
+        assert quick.name == "Quick"
+        assert quick.depth == "surface"
         assert quick.enhance_level == 0
-        assert quick.estimated_time == '1-2 minutes'
-        assert quick.icon == '⚡'
+        assert quick.estimated_time == "1-2 minutes"
+        assert quick.icon == "⚡"
         # Quick should disable slow features
-        assert quick.features['api_reference']  # Essential
-        assert not quick.features['dependency_graph']  # Slow
-        assert not quick.features['patterns']  # Slow
-        assert not quick.features['test_examples']  # Slow
-        assert not quick.features['how_to_guides']  # Requires AI
-        assert quick.features['docs']  # Essential
+        assert quick.features["api_reference"]  # Essential
+        assert not quick.features["dependency_graph"]  # Slow
+        assert not quick.features["patterns"]  # Slow
+        assert not quick.features["test_examples"]  # Slow
+        assert not quick.features["how_to_guides"]  # Requires AI
+        assert quick.features["docs"]  # Essential
 
     def test_standard_preset(self):
         """Test standard preset configuration."""
-        standard = PRESETS['standard']
-        assert standard.name == 'Standard'
-        assert standard.depth == 'deep'
+        standard = PRESETS["standard"]
+        assert standard.name == "Standard"
+        assert standard.depth == "deep"
         assert standard.enhance_level == 1
-        assert standard.estimated_time == '5-10 minutes'
-        assert standard.icon == '🎯'
+        assert standard.estimated_time == "5-10 minutes"
+        assert standard.icon == "🎯"
         # Standard should enable core features
-        assert standard.features['api_reference']
-        assert standard.features['dependency_graph']
-        assert standard.features['patterns']
-        assert standard.features['test_examples']
-        assert not standard.features['how_to_guides']  # Slow
-        assert standard.features['config_patterns']
-        assert standard.features['docs']
+        assert standard.features["api_reference"]
+        assert standard.features["dependency_graph"]
+        assert standard.features["patterns"]
+        assert standard.features["test_examples"]
+        assert not standard.features["how_to_guides"]  # Slow
+        assert standard.features["config_patterns"]
+        assert standard.features["docs"]
 
     def test_comprehensive_preset(self):
         """Test comprehensive preset configuration."""
-        comprehensive = PRESETS['comprehensive']
-        assert comprehensive.name == 'Comprehensive'
-        assert comprehensive.depth == 'full'
+        comprehensive = PRESETS["comprehensive"]
+        assert comprehensive.name == "Comprehensive"
+        assert comprehensive.depth == "full"
         assert comprehensive.enhance_level == 3
-        assert comprehensive.estimated_time == '20-60 minutes'
-        assert comprehensive.icon == '🚀'
+        assert comprehensive.estimated_time == "20-60 minutes"
+        assert comprehensive.icon == "🚀"
         # Comprehensive should enable ALL features
         assert all(comprehensive.features.values())
 
@@ -81,44 +81,44 @@ class TestPresetManager:
 
     def test_get_preset(self):
         """Test PresetManager.get_preset()."""
-        quick = PresetManager.get_preset('quick')
+        quick = PresetManager.get_preset("quick")
         assert quick is not None
-        assert quick.name == 'Quick'
-        assert quick.depth == 'surface'
+        assert quick.name == "Quick"
+        assert quick.depth == "surface"
 
         # Case insensitive
-        standard = PresetManager.get_preset('STANDARD')
+        standard = PresetManager.get_preset("STANDARD")
         assert standard is not None
-        assert standard.name == 'Standard'
+        assert standard.name == "Standard"
 
     def test_get_preset_invalid(self):
         """Test PresetManager.get_preset() with invalid name."""
-        invalid = PresetManager.get_preset('nonexistent')
+        invalid = PresetManager.get_preset("nonexistent")
         assert invalid is None
 
     def test_list_presets(self):
         """Test PresetManager.list_presets()."""
         presets = PresetManager.list_presets()
         assert len(presets) == 3
-        assert 'quick' in presets
-        assert 'standard' in presets
-        assert 'comprehensive' in presets
+        assert "quick" in presets
+        assert "standard" in presets
+        assert "comprehensive" in presets
 
     def test_format_preset_help(self):
         """Test PresetManager.format_preset_help()."""
         help_text = PresetManager.format_preset_help()
-        assert 'Available presets:' in help_text
-        assert '⚡ quick' in help_text
-        assert '🎯 standard' in help_text
-        assert '🚀 comprehensive' in help_text
-        assert '1-2 minutes' in help_text
-        assert '5-10 minutes' in help_text
-        assert '20-60 minutes' in help_text
+        assert "Available presets:" in help_text
+        assert "⚡ quick" in help_text
+        assert "🎯 standard" in help_text
+        assert "🚀 comprehensive" in help_text
+        assert "1-2 minutes" in help_text
+        assert "5-10 minutes" in help_text
+        assert "20-60 minutes" in help_text
 
     def test_get_default_preset(self):
         """Test PresetManager.get_default_preset()."""
         default = PresetManager.get_default_preset()
-        assert default == 'standard'
+        assert default == "standard"
 
 
 class TestPresetApplication:
@@ -126,85 +126,85 @@ class TestPresetApplication:
 
     def test_apply_preset_quick(self):
         """Test applying quick preset."""
-        args = {'directory': '/tmp/test'}
-        updated = PresetManager.apply_preset('quick', args)
+        args = {"directory": "/tmp/test"}
+        updated = PresetManager.apply_preset("quick", args)
 
-        assert updated['depth'] == 'surface'
-        assert updated['enhance_level'] == 0
-        assert updated['skip_patterns']  # Quick disables patterns
-        assert updated['skip_dependency_graph']  # Quick disables dep graph
-        assert updated['skip_test_examples']  # Quick disables tests
-        assert updated['skip_how_to_guides']  # Quick disables guides
-        assert not updated['skip_api_reference']  # Quick enables API ref
-        assert not updated['skip_docs']  # Quick enables docs
+        assert updated["depth"] == "surface"
+        assert updated["enhance_level"] == 0
+        assert updated["skip_patterns"]  # Quick disables patterns
+        assert updated["skip_dependency_graph"]  # Quick disables dep graph
+        assert updated["skip_test_examples"]  # Quick disables tests
+        assert updated["skip_how_to_guides"]  # Quick disables guides
+        assert not updated["skip_api_reference"]  # Quick enables API ref
+        assert not updated["skip_docs"]  # Quick enables docs
 
     def test_apply_preset_standard(self):
         """Test applying standard preset."""
-        args = {'directory': '/tmp/test'}
-        updated = PresetManager.apply_preset('standard', args)
+        args = {"directory": "/tmp/test"}
+        updated = PresetManager.apply_preset("standard", args)
 
-        assert updated['depth'] == 'deep'
-        assert updated['enhance_level'] == 1
-        assert not updated['skip_patterns']  # Standard enables patterns
-        assert not updated['skip_dependency_graph']  # Standard enables dep graph
-        assert not updated['skip_test_examples']  # Standard enables tests
-        assert updated['skip_how_to_guides']  # Standard disables guides (slow)
-        assert not updated['skip_api_reference']  # Standard enables API ref
-        assert not updated['skip_docs']  # Standard enables docs
+        assert updated["depth"] == "deep"
+        assert updated["enhance_level"] == 1
+        assert not updated["skip_patterns"]  # Standard enables patterns
+        assert not updated["skip_dependency_graph"]  # Standard enables dep graph
+        assert not updated["skip_test_examples"]  # Standard enables tests
+        assert updated["skip_how_to_guides"]  # Standard disables guides (slow)
+        assert not updated["skip_api_reference"]  # Standard enables API ref
+        assert not updated["skip_docs"]  # Standard enables docs
 
     def test_apply_preset_comprehensive(self):
         """Test applying comprehensive preset."""
-        args = {'directory': '/tmp/test'}
-        updated = PresetManager.apply_preset('comprehensive', args)
+        args = {"directory": "/tmp/test"}
+        updated = PresetManager.apply_preset("comprehensive", args)
 
-        assert updated['depth'] == 'full'
-        assert updated['enhance_level'] == 3
+        assert updated["depth"] == "full"
+        assert updated["enhance_level"] == 3
         # Comprehensive enables ALL features
-        assert not updated['skip_patterns']
-        assert not updated['skip_dependency_graph']
-        assert not updated['skip_test_examples']
-        assert not updated['skip_how_to_guides']
-        assert not updated['skip_api_reference']
-        assert not updated['skip_config_patterns']
-        assert not updated['skip_docs']
+        assert not updated["skip_patterns"]
+        assert not updated["skip_dependency_graph"]
+        assert not updated["skip_test_examples"]
+        assert not updated["skip_how_to_guides"]
+        assert not updated["skip_api_reference"]
+        assert not updated["skip_config_patterns"]
+        assert not updated["skip_docs"]
 
     def test_cli_overrides_preset(self):
         """Test that CLI args override preset defaults."""
         args = {
-            'directory': '/tmp/test',
-            'enhance_level': 2,  # Override preset default
-            'skip_patterns': False  # Override preset default
+            "directory": "/tmp/test",
+            "enhance_level": 2,  # Override preset default
+            "skip_patterns": False,  # Override preset default
         }
 
-        updated = PresetManager.apply_preset('quick', args)
+        updated = PresetManager.apply_preset("quick", args)
 
         # Preset says enhance_level=0, but CLI said 2
-        assert updated['enhance_level'] == 2  # CLI wins
+        assert updated["enhance_level"] == 2  # CLI wins
 
         # Preset says skip_patterns=True (disabled), but CLI said False (enabled)
-        assert not updated['skip_patterns']  # CLI wins
+        assert not updated["skip_patterns"]  # CLI wins
 
     def test_apply_preset_preserves_args(self):
         """Test that apply_preset preserves existing args."""
         args = {
-            'directory': '/tmp/test',
-            'output': 'custom_output/',
-            'languages': 'Python,JavaScript'
+            "directory": "/tmp/test",
+            "output": "custom_output/",
+            "languages": "Python,JavaScript",
         }
 
-        updated = PresetManager.apply_preset('standard', args)
+        updated = PresetManager.apply_preset("standard", args)
 
         # Existing args should be preserved
-        assert updated['directory'] == '/tmp/test'
-        assert updated['output'] == 'custom_output/'
-        assert updated['languages'] == 'Python,JavaScript'
+        assert updated["directory"] == "/tmp/test"
+        assert updated["output"] == "custom_output/"
+        assert updated["languages"] == "Python,JavaScript"
 
     def test_apply_preset_invalid(self):
         """Test applying invalid preset raises error."""
-        args = {'directory': '/tmp/test'}
+        args = {"directory": "/tmp/test"}
 
         with pytest.raises(ValueError, match="Unknown preset: nonexistent"):
-            PresetManager.apply_preset('nonexistent', args)
+            PresetManager.apply_preset("nonexistent", args)
 
 
 class TestDeprecationWarnings:
@@ -215,12 +215,7 @@ class TestDeprecationWarnings:
         from skill_seekers.cli.codebase_scraper import _check_deprecated_flags
         import argparse
 
-        args = argparse.Namespace(
-            quick=True,
-            comprehensive=False,
-            depth=None,
-            ai_mode='auto'
-        )
+        args = argparse.Namespace(quick=True, comprehensive=False, depth=None, ai_mode="auto")
 
         _check_deprecated_flags(args)
 
@@ -235,12 +230,7 @@ class TestDeprecationWarnings:
         from skill_seekers.cli.codebase_scraper import _check_deprecated_flags
         import argparse
 
-        args = argparse.Namespace(
-            quick=False,
-            comprehensive=True,
-            depth=None,
-            ai_mode='auto'
-        )
+        args = argparse.Namespace(quick=False, comprehensive=True, depth=None, ai_mode="auto")
 
         _check_deprecated_flags(args)
 
@@ -255,12 +245,7 @@ class TestDeprecationWarnings:
         from skill_seekers.cli.codebase_scraper import _check_deprecated_flags
         import argparse
 
-        args = argparse.Namespace(
-            quick=False,
-            comprehensive=False,
-            depth='full',
-            ai_mode='auto'
-        )
+        args = argparse.Namespace(quick=False, comprehensive=False, depth="full", ai_mode="auto")
 
         _check_deprecated_flags(args)
 
@@ -275,12 +260,7 @@ class TestDeprecationWarnings:
         from skill_seekers.cli.codebase_scraper import _check_deprecated_flags
         import argparse
 
-        args = argparse.Namespace(
-            quick=False,
-            comprehensive=False,
-            depth=None,
-            ai_mode='api'
-        )
+        args = argparse.Namespace(quick=False, comprehensive=False, depth=None, ai_mode="api")
 
         _check_deprecated_flags(args)
 
@@ -295,12 +275,7 @@ class TestDeprecationWarnings:
         from skill_seekers.cli.codebase_scraper import _check_deprecated_flags
         import argparse
 
-        args = argparse.Namespace(
-            quick=True,
-            comprehensive=False,
-            depth='surface',
-            ai_mode='local'
-        )
+        args = argparse.Namespace(quick=True, comprehensive=False, depth="surface", ai_mode="local")
 
         _check_deprecated_flags(args)
 
@@ -317,12 +292,7 @@ class TestDeprecationWarnings:
         from skill_seekers.cli.codebase_scraper import _check_deprecated_flags
         import argparse
 
-        args = argparse.Namespace(
-            quick=False,
-            comprehensive=False,
-            depth=None,
-            ai_mode='auto'
-        )
+        args = argparse.Namespace(quick=False, comprehensive=False, depth=None, ai_mode="auto")
 
         _check_deprecated_flags(args)
 
@@ -337,31 +307,31 @@ class TestBackwardCompatibility:
     def test_old_flags_still_work(self):
         """Test that old flags still work (with warnings)."""
         # --quick flag
-        args = {'quick': True}
-        updated = PresetManager.apply_preset('quick', args)
-        assert updated['depth'] == 'surface'
+        args = {"quick": True}
+        updated = PresetManager.apply_preset("quick", args)
+        assert updated["depth"] == "surface"
 
         # --comprehensive flag
-        args = {'comprehensive': True}
-        updated = PresetManager.apply_preset('comprehensive', args)
-        assert updated['depth'] == 'full'
+        args = {"comprehensive": True}
+        updated = PresetManager.apply_preset("comprehensive", args)
+        assert updated["depth"] == "full"
 
     def test_preset_flag_preferred(self):
         """Test that --preset flag is the recommended way."""
         # Using --preset quick
-        args = {'preset': 'quick'}
-        updated = PresetManager.apply_preset('quick', args)
-        assert updated['depth'] == 'surface'
+        args = {"preset": "quick"}
+        updated = PresetManager.apply_preset("quick", args)
+        assert updated["depth"] == "surface"
 
         # Using --preset standard
-        args = {'preset': 'standard'}
-        updated = PresetManager.apply_preset('standard', args)
-        assert updated['depth'] == 'deep'
+        args = {"preset": "standard"}
+        updated = PresetManager.apply_preset("standard", args)
+        assert updated["depth"] == "deep"
 
         # Using --preset comprehensive
-        args = {'preset': 'comprehensive'}
-        updated = PresetManager.apply_preset('comprehensive', args)
-        assert updated['depth'] == 'full'
+        args = {"preset": "comprehensive"}
+        updated = PresetManager.apply_preset("comprehensive", args)
+        assert updated["depth"] == "full"
 
 
 if __name__ == "__main__":
diff --git a/tests/test_quality_metrics.py b/tests/test_quality_metrics.py
index 25e4b7f..714510f 100644
--- a/tests/test_quality_metrics.py
+++ b/tests/test_quality_metrics.py
@@ -19,10 +19,7 @@ import tempfile
 # Add src to path
 sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
 
-from skill_seekers.cli.quality_metrics import (
-    QualityAnalyzer,
-    MetricLevel
-)
+from skill_seekers.cli.quality_metrics import QualityAnalyzer, MetricLevel
 
 
 @pytest.fixture
@@ -176,9 +173,9 @@ def test_calculate_statistics(complete_skill_dir):
     analyzer = QualityAnalyzer(complete_skill_dir)
     stats = analyzer.calculate_statistics()
 
-    assert stats['total_files'] > 0
-    assert stats['markdown_files'] > 0
-    assert stats['total_words'] > 0
+    assert stats["total_files"] > 0
+    assert stats["markdown_files"] > 0
+    assert stats["total_words"] > 0
 
 
 def test_overall_score_calculation():
@@ -197,9 +194,7 @@ def test_overall_score_calculation():
         coverage = 70.0
         health = 85.0
 
-        overall = analyzer.calculate_overall_score(
-            completeness, accuracy, coverage, health
-        )
+        overall = analyzer.calculate_overall_score(completeness, accuracy, coverage, health)
 
         assert overall.completeness == 80.0
         assert overall.accuracy == 90.0
@@ -218,13 +213,13 @@ def test_grade_assignment():
 
         # Test various scores
         score_95 = analyzer.calculate_overall_score(95, 95, 95, 95)
-        assert score_95.grade == 'A+'
+        assert score_95.grade == "A+"
 
         score_85 = analyzer.calculate_overall_score(85, 85, 85, 85)
-        assert score_85.grade in ['A-', 'B+']
+        assert score_85.grade in ["A-", "B+"]
 
         score_70 = analyzer.calculate_overall_score(70, 70, 70, 70)
-        assert score_70.grade in ['B-', 'C+', 'C']
+        assert score_70.grade in ["B-", "C+", "C"]
 
 
 def test_generate_recommendations():
@@ -240,7 +235,7 @@ def test_generate_recommendations():
         recommendations = analyzer.generate_recommendations(score)
 
         assert len(recommendations) > 0
-        assert any('completeness' in r.lower() for r in recommendations)
+        assert any("completeness" in r.lower() for r in recommendations)
 
 
 def test_generate_report(complete_skill_dir):
diff --git a/tests/test_rag_chunker.py b/tests/test_rag_chunker.py
index 7ce7282..ae674ef 100644
--- a/tests/test_rag_chunker.py
+++ b/tests/test_rag_chunker.py
@@ -28,7 +28,7 @@ class TestRAGChunker:
             chunk_overlap=100,
             preserve_code_blocks=False,
             preserve_paragraphs=False,
-            min_chunk_size=50
+            min_chunk_size=50,
         )
 
         assert chunker.chunk_size == 1024
@@ -180,13 +180,17 @@ class TestRAGChunker:
 
         # Create SKILL.md
         skill_md = skill_dir / "SKILL.md"
-        skill_md.write_text("# Main Skill\n\nThis is the main skill content.\n\nWith multiple paragraphs.")
+        skill_md.write_text(
+            "# Main Skill\n\nThis is the main skill content.\n\nWith multiple paragraphs."
+        )
 
         # Create references directory with files
         references_dir = skill_dir / "references"
         references_dir.mkdir()
 
-        (references_dir / "getting_started.md").write_text("# Getting Started\n\nQuick start guide.")
+        (references_dir / "getting_started.md").write_text(
+            "# Getting Started\n\nQuick start guide."
+        )
         (references_dir / "api.md").write_text("# API Reference\n\nAPI documentation.")
 
         # Chunk skill
@@ -209,7 +213,7 @@ class TestRAGChunker:
             {
                 "chunk_id": "test_0",
                 "page_content": "Test content",
-                "metadata": {"source": "test", "chunk_index": 0}
+                "metadata": {"source": "test", "chunk_index": 0},
             }
         ]
 
@@ -340,7 +344,7 @@ class TestRAGChunker:
         metadata = {
             "source": "react-docs",
             "category": "hooks",
-            "url": "https://react.dev/reference/react"
+            "url": "https://react.dev/reference/react",
         }
 
         chunks = chunker.chunk_document(text, metadata)
@@ -379,10 +383,7 @@ class TestRAGChunkerIntegration:
 
         # Convert to LangChain Documents
         docs = [
-            Document(
-                page_content=chunk["page_content"],
-                metadata=chunk["metadata"]
-            )
+            Document(page_content=chunk["page_content"], metadata=chunk["metadata"])
             for chunk in chunks
         ]
 
@@ -407,11 +408,7 @@ class TestRAGChunkerIntegration:
 
         # Convert to LlamaIndex TextNodes
         nodes = [
-            TextNode(
-                text=chunk["page_content"],
-                metadata=chunk["metadata"],
-                id_=chunk["chunk_id"]
-            )
+            TextNode(text=chunk["page_content"], metadata=chunk["metadata"], id_=chunk["chunk_id"])
             for chunk in chunks
         ]
 
diff --git a/tests/test_server_fastmcp_http.py b/tests/test_server_fastmcp_http.py
index d093066..91ada46 100644
--- a/tests/test_server_fastmcp_http.py
+++ b/tests/test_server_fastmcp_http.py
@@ -13,6 +13,7 @@ pytest.importorskip("mcp.server")
 # Check if starlette is available
 try:
     from starlette.testclient import TestClient
+
     STARLETTE_AVAILABLE = True
 except ImportError:
     STARLETTE_AVAILABLE = False
@@ -21,8 +22,7 @@ from skill_seekers.mcp.server_fastmcp import mcp
 
 # Skip all tests if starlette is not installed
 pytestmark = pytest.mark.skipif(
-    not STARLETTE_AVAILABLE,
-    reason="starlette not installed (pip install starlette httpx)"
+    not STARLETTE_AVAILABLE, reason="starlette not installed (pip install starlette httpx)"
 )
 
 
diff --git a/tests/test_streaming_ingestion.py b/tests/test_streaming_ingestion.py
index 7360b6b..dc5f3d7 100644
--- a/tests/test_streaming_ingestion.py
+++ b/tests/test_streaming_ingestion.py
@@ -18,10 +18,7 @@ import tempfile
 # Add src to path
 sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
 
-from skill_seekers.cli.streaming_ingest import (
-    StreamingIngester,
-    IngestionProgress
-)
+from skill_seekers.cli.streaming_ingest import StreamingIngester, IngestionProgress
 
 
 @pytest.fixture
@@ -158,11 +155,13 @@ def test_progress_tracking(temp_skill_dir):
     progress_updates = []
 
     def callback(progress: IngestionProgress):
-        progress_updates.append({
-            "processed_docs": progress.processed_documents,
-            "processed_chunks": progress.processed_chunks,
-            "percent": progress.progress_percent
-        })
+        progress_updates.append(
+            {
+                "processed_docs": progress.processed_documents,
+                "processed_chunks": progress.processed_chunks,
+                "percent": progress.progress_percent,
+            }
+        )
 
     list(ingester.stream_skill_directory(temp_skill_dir, callback=callback))
 
@@ -171,7 +170,9 @@ def test_progress_tracking(temp_skill_dir):
 
     # Progress should increase
     for i in range(len(progress_updates) - 1):
-        assert progress_updates[i + 1]["processed_chunks"] >= progress_updates[i]["processed_chunks"]
+        assert (
+            progress_updates[i + 1]["processed_chunks"] >= progress_updates[i]["processed_chunks"]
+        )
 
 
 def test_checkpoint_save_load():
@@ -189,7 +190,7 @@ def test_checkpoint_save_load():
             processed_chunks=50,
             failed_chunks=2,
             bytes_processed=10000,
-            start_time=1234567890.0
+            start_time=1234567890.0,
         )
 
         # Save checkpoint
@@ -215,7 +216,7 @@ def test_format_progress():
         processed_chunks=50,
         failed_chunks=0,
         bytes_processed=10000,
-        start_time=0.0
+        start_time=0.0,
     )
 
     progress_str = ingester.format_progress()
@@ -245,17 +246,19 @@ def test_chunk_size_validation():
 
     # Small chunks
     ingester_small = StreamingIngester(chunk_size=100, chunk_overlap=10)
-    chunks_small = list(ingester_small.chunk_document(
-        content,
-        {"source": "test", "file": "test.md", "category": "test"}
-    ))
+    chunks_small = list(
+        ingester_small.chunk_document(
+            content, {"source": "test", "file": "test.md", "category": "test"}
+        )
+    )
 
     # Large chunks
     ingester_large = StreamingIngester(chunk_size=500, chunk_overlap=50)
-    chunks_large = list(ingester_large.chunk_document(
-        content,
-        {"source": "test", "file": "test.md", "category": "test"}
-    ))
+    chunks_large = list(
+        ingester_large.chunk_document(
+            content, {"source": "test", "file": "test.md", "category": "test"}
+        )
+    )
 
     # Smaller chunk size should create more chunks
     assert len(chunks_small) > len(chunks_large)
diff --git a/tests/test_upload_integration.py b/tests/test_upload_integration.py
index 6c1eaac..5d69ee2 100644
--- a/tests/test_upload_integration.py
+++ b/tests/test_upload_integration.py
@@ -21,9 +21,9 @@ def sample_chroma_package(tmp_path):
         "metadatas": [
             {"source": "test", "category": "overview", "file": "SKILL.md"},
             {"source": "test", "category": "api", "file": "API.md"},
-            {"source": "test", "category": "guide", "file": "GUIDE.md"}
+            {"source": "test", "category": "guide", "file": "GUIDE.md"},
         ],
-        "ids": ["id1", "id2", "id3"]
+        "ids": ["id1", "id2", "id3"],
     }
 
     package_path = tmp_path / "test-chroma.json"
@@ -43,8 +43,8 @@ def sample_weaviate_package(tmp_path):
             "properties": [
                 {"name": "content", "dataType": ["text"]},
                 {"name": "source", "dataType": ["string"]},
-                {"name": "category", "dataType": ["string"]}
-            ]
+                {"name": "category", "dataType": ["string"]},
+            ],
         },
         "objects": [
             {
@@ -52,18 +52,14 @@ def sample_weaviate_package(tmp_path):
                 "properties": {
                     "content": "Test content 1",
                     "source": "test",
-                    "category": "overview"
-                }
+                    "category": "overview",
+                },
             },
             {
                 "id": "00000000-0000-0000-0000-000000000002",
-                "properties": {
-                    "content": "Test content 2",
-                    "source": "test",
-                    "category": "api"
-                }
-            }
-        ]
+                "properties": {"content": "Test content 2", "source": "test", "category": "api"},
+            },
+        ],
     }
 
     package_path = tmp_path / "test-weaviate.json"
@@ -76,40 +72,41 @@ class TestChromaUploadBasics:
 
     def test_chroma_adaptor_exists(self):
         """Test that ChromaDB adaptor can be loaded."""
-        adaptor = get_adaptor('chroma')
+        adaptor = get_adaptor("chroma")
         assert adaptor is not None
-        assert adaptor.PLATFORM == 'chroma'
+        assert adaptor.PLATFORM == "chroma"
 
     def test_chroma_upload_without_chromadb_installed(self, sample_chroma_package):
         """Test upload fails gracefully without chromadb installed."""
-        adaptor = get_adaptor('chroma')
+        adaptor = get_adaptor("chroma")
 
         # Temporarily remove chromadb if it exists
         import sys
-        chromadb_backup = sys.modules.get('chromadb')
-        if 'chromadb' in sys.modules:
-            del sys.modules['chromadb']
+
+        chromadb_backup = sys.modules.get("chromadb")
+        if "chromadb" in sys.modules:
+            del sys.modules["chromadb"]
 
         try:
             result = adaptor.upload(sample_chroma_package)
 
-            assert result['success'] is False
-            assert 'chromadb not installed' in result['message']
-            assert 'pip install chromadb' in result['message']
+            assert result["success"] is False
+            assert "chromadb not installed" in result["message"]
+            assert "pip install chromadb" in result["message"]
         finally:
             if chromadb_backup:
-                sys.modules['chromadb'] = chromadb_backup
+                sys.modules["chromadb"] = chromadb_backup
 
     def test_chroma_upload_api_signature(self, sample_chroma_package):
         """Test ChromaDB upload has correct API signature."""
-        adaptor = get_adaptor('chroma')
+        adaptor = get_adaptor("chroma")
 
         # Verify upload method exists and accepts kwargs
-        assert hasattr(adaptor, 'upload')
+        assert hasattr(adaptor, "upload")
         assert callable(adaptor.upload)
 
         # Verify adaptor methods exist
-        assert hasattr(adaptor, '_generate_openai_embeddings')
+        assert hasattr(adaptor, "_generate_openai_embeddings")
 
 
 class TestWeaviateUploadBasics:
@@ -117,40 +114,41 @@ class TestWeaviateUploadBasics:
 
     def test_weaviate_adaptor_exists(self):
         """Test that Weaviate adaptor can be loaded."""
-        adaptor = get_adaptor('weaviate')
+        adaptor = get_adaptor("weaviate")
         assert adaptor is not None
-        assert adaptor.PLATFORM == 'weaviate'
+        assert adaptor.PLATFORM == "weaviate"
 
     def test_weaviate_upload_without_weaviate_installed(self, sample_weaviate_package):
         """Test upload fails gracefully without weaviate-client installed."""
-        adaptor = get_adaptor('weaviate')
+        adaptor = get_adaptor("weaviate")
 
         # Temporarily remove weaviate if it exists
         import sys
-        weaviate_backup = sys.modules.get('weaviate')
-        if 'weaviate' in sys.modules:
-            del sys.modules['weaviate']
+
+        weaviate_backup = sys.modules.get("weaviate")
+        if "weaviate" in sys.modules:
+            del sys.modules["weaviate"]
 
         try:
             result = adaptor.upload(sample_weaviate_package)
 
-            assert result['success'] is False
-            assert 'weaviate-client not installed' in result['message']
-            assert 'pip install weaviate-client' in result['message']
+            assert result["success"] is False
+            assert "weaviate-client not installed" in result["message"]
+            assert "pip install weaviate-client" in result["message"]
         finally:
             if weaviate_backup:
-                sys.modules['weaviate'] = weaviate_backup
+                sys.modules["weaviate"] = weaviate_backup
 
     def test_weaviate_upload_api_signature(self, sample_weaviate_package):
         """Test Weaviate upload has correct API signature."""
-        adaptor = get_adaptor('weaviate')
+        adaptor = get_adaptor("weaviate")
 
         # Verify upload method exists and accepts kwargs
-        assert hasattr(adaptor, 'upload')
+        assert hasattr(adaptor, "upload")
         assert callable(adaptor.upload)
 
         # Verify adaptor methods exist
-        assert hasattr(adaptor, '_generate_openai_embeddings')
+        assert hasattr(adaptor, "_generate_openai_embeddings")
 
 
 class TestPackageStructure:
@@ -161,30 +159,30 @@ class TestPackageStructure:
         with open(sample_chroma_package) as f:
             data = json.load(f)
 
-        assert 'collection_name' in data
-        assert 'documents' in data
-        assert 'metadatas' in data
-        assert 'ids' in data
-        assert len(data['documents']) == len(data['metadatas']) == len(data['ids'])
+        assert "collection_name" in data
+        assert "documents" in data
+        assert "metadatas" in data
+        assert "ids" in data
+        assert len(data["documents"]) == len(data["metadatas"]) == len(data["ids"])
 
     def test_weaviate_package_structure(self, sample_weaviate_package):
         """Test Weaviate package has required fields."""
         with open(sample_weaviate_package) as f:
             data = json.load(f)
 
-        assert 'class_name' in data
-        assert 'schema' in data
-        assert 'objects' in data
-        assert len(data['objects']) == 2
+        assert "class_name" in data
+        assert "schema" in data
+        assert "objects" in data
+        assert len(data["objects"]) == 2
 
         # Verify schema structure
-        assert 'class' in data['schema']
-        assert 'properties' in data['schema']
+        assert "class" in data["schema"]
+        assert "properties" in data["schema"]
 
         # Verify object structure
-        for obj in data['objects']:
-            assert 'id' in obj
-            assert 'properties' in obj
+        for obj in data["objects"]:
+            assert "id" in obj
+            assert "properties" in obj
 
 
 class TestUploadCommandIntegration:
@@ -199,25 +197,26 @@ class TestUploadCommandIntegration:
 
         # Verify it accepts kwargs for vector DBs
         import inspect
+
         sig = inspect.signature(upload_skill_api)
         params = list(sig.parameters.keys())
-        assert 'package_path' in params
-        assert 'target' in params
-        assert 'api_key' in params
-        assert 'kwargs' in params  # For platform-specific options
+        assert "package_path" in params
+        assert "target" in params
+        assert "api_key" in params
+        assert "kwargs" in params  # For platform-specific options
 
     def test_upload_command_supports_chroma(self):
         """Test upload command recognizes chroma as target."""
 
         # This should not raise ValueError
-        adaptor = get_adaptor('chroma')
+        adaptor = get_adaptor("chroma")
         assert adaptor is not None
 
     def test_upload_command_supports_weaviate(self):
         """Test upload command recognizes weaviate as target."""
 
         # This should not raise ValueError
-        adaptor = get_adaptor('weaviate')
+        adaptor = get_adaptor("weaviate")
         assert adaptor is not None
 
 
@@ -226,7 +225,7 @@ class TestErrorHandling:
 
     def test_chroma_handles_missing_file(self, tmp_path):
         """Test ChromaDB upload handles missing files gracefully."""
-        adaptor = get_adaptor('chroma')
+        adaptor = get_adaptor("chroma")
 
         missing_file = tmp_path / "nonexistent.json"
 
@@ -234,14 +233,14 @@ class TestErrorHandling:
         try:
             result = adaptor.upload(missing_file)
             # If it returns a dict, it should indicate failure
-            assert result['success'] is False
+            assert result["success"] is False
         except FileNotFoundError:
             # This is also acceptable
             pass
 
     def test_weaviate_handles_missing_file(self, tmp_path):
         """Test Weaviate upload handles missing files gracefully."""
-        adaptor = get_adaptor('weaviate')
+        adaptor = get_adaptor("weaviate")
 
         missing_file = tmp_path / "nonexistent.json"
 
@@ -249,14 +248,14 @@ class TestErrorHandling:
         try:
             result = adaptor.upload(missing_file)
             # If it returns a dict, it should indicate failure
-            assert result['success'] is False
+            assert result["success"] is False
         except FileNotFoundError:
             # This is also acceptable
             pass
 
     def test_chroma_handles_invalid_json(self, tmp_path):
         """Test ChromaDB upload handles invalid JSON gracefully."""
-        adaptor = get_adaptor('chroma')
+        adaptor = get_adaptor("chroma")
 
         invalid_file = tmp_path / "invalid.json"
         invalid_file.write_text("not valid json{")
@@ -265,14 +264,14 @@ class TestErrorHandling:
         try:
             result = adaptor.upload(invalid_file)
             # If it returns a dict, it should indicate failure
-            assert result['success'] is False
+            assert result["success"] is False
         except json.JSONDecodeError:
             # This is also acceptable
             pass
 
     def test_weaviate_handles_invalid_json(self, tmp_path):
         """Test Weaviate upload handles invalid JSON gracefully."""
-        adaptor = get_adaptor('weaviate')
+        adaptor = get_adaptor("weaviate")
 
         invalid_file = tmp_path / "invalid.json"
         invalid_file.write_text("not valid json{")
@@ -281,7 +280,7 @@ class TestErrorHandling:
         try:
             result = adaptor.upload(invalid_file)
             # If it returns a dict, it should indicate failure
-            assert result['success'] is False
+            assert result["success"] is False
         except json.JSONDecodeError:
             # This is also acceptable
             pass
diff --git a/tests/test_url_conversion.py b/tests/test_url_conversion.py
index 3eb31bd..e37a540 100644
--- a/tests/test_url_conversion.py
+++ b/tests/test_url_conversion.py
@@ -155,13 +155,9 @@ class TestConvertToMdUrls(unittest.TestCase):
 
         # Should deduplicate to 3 unique base URLs
         self.assertEqual(len(result), 3)
-        self.assertIn(
-            "https://mikro-orm.io/docs/quick-start/index.html.md", result
-        )
+        self.assertIn("https://mikro-orm.io/docs/quick-start/index.html.md", result)
         self.assertIn("https://mikro-orm.io/docs/propagation/index.html.md", result)
-        self.assertIn(
-            "https://mikro-orm.io/docs/defining-entities/index.html.md", result
-        )
+        self.assertIn("https://mikro-orm.io/docs/defining-entities/index.html.md", result)
 
         # Should NOT contain any URLs with anchor fragments
         for url in result: