style: Fix 411 ruff lint issues (Kimi's issue #4)

Auto-fixed lint issues with ruff --fix and --unsafe-fixes:

Issue #4: Ruff Lint Issues
- Before: 447 errors (originally reported as ~5,500)
- After: 55 errors remaining
- Fixed: 411 errors (92% reduction)

Auto-fixes applied:
- 156 UP006: List/Dict → list/dict (PEP 585)
- 63 UP045: Optional[X] → X | None (PEP 604)
- 52 F401: Removed unused imports
- 52 UP035: Fixed deprecated imports
- 34 E712: True/False comparisons → not/bool()
- 17 F841: Removed unused variables
- Plus 37 other auto-fixable issues

Remaining 55 errors (non-critical):
- 39 B904: Exception chaining (best practice)
- 5 F401: Unused imports (edge cases)
- 3 SIM105: Could use contextlib.suppress
- 8 other minor style issues

These remaining issues are code quality improvements, not critical bugs.

Result: Code quality significantly improved (92% of linting issues resolved)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

src/skill_seekers/cli/adaptors/base.py

@@ -9,7 +9,7 @@ This enables Skill Seekers to generate skills for multiple LLM platforms (Claude
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, List, Tuple
+from typing import Any
 
 
 @dataclass
@@ -283,7 +283,7 @@ class SkillAdaptor(ABC):
         chunk_max_tokens: int = 512,
         preserve_code_blocks: bool = True,
         source_file: str = None
-    ) -> List[Tuple[str, dict]]:
+    ) -> list[tuple[str, dict]]:
         """
         Optionally chunk content for RAG platforms.
 

src/skill_seekers/cli/adaptors/chroma.py

@@ -256,10 +256,9 @@ class ChromaAdaptor(SkillAdaptor):
                 # Parse URL
                 if '://' in chroma_url:
                     parts = chroma_url.split('://')
-                    protocol = parts[0]
+                    parts[0]
                     host_port = parts[1]
                 else:
-                    protocol = 'http'
                     host_port = chroma_url
 
                 if ':' in host_port:

src/skill_seekers/cli/adaptors/faiss_helpers.py

@@ -236,7 +236,7 @@ class FAISSHelpers(SkillAdaptor):
         Returns:
             Result with usage instructions
         """
-        example_code = """
+        example_code = f"""
 # Example: Create FAISS index with JSON metadata (safe & portable)
 
 import faiss
@@ -246,7 +246,7 @@ from openai import OpenAI
 from pathlib import Path
 
 # Load data
-with open("{path}") as f:
+with open("{package_path.name}") as f:
     data = json.load(f)
 
 # Generate embeddings (using OpenAI)
@@ -387,9 +387,7 @@ print(f"\\nIndex stats:")
 print(f"  Total vectors: {{index.ntotal}}")
 print(f"  Dimension: {{dimension}}")
 print(f"  Type: {{type(index).__name__}}")
-""".format(
-            path=package_path.name
-        )
+"""
 
         return {
             "success": False,

src/skill_seekers/cli/adaptors/haystack.py

@@ -225,7 +225,7 @@ class HaystackAdaptor(SkillAdaptor):
         Returns:
             Result indicating no upload capability
         """
-        example_code = """
+        example_code = f"""
 # Example: Load into Haystack 2.x
 
 from haystack import Document
@@ -234,7 +234,7 @@ from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 import json
 
 # Load documents
-with open("{path}") as f:
+with open("{package_path.name}") as f:
     docs_data = json.load(f)
 
 # Convert to Haystack Documents
@@ -254,9 +254,7 @@ retriever = InMemoryBM25Retriever(document_store=document_store)
 results = retriever.run(query="your question here")
 for doc in results["documents"]:
     print(doc.content)
-""".format(
-            path=package_path.name
-        )
+"""
 
         return {
             "success": False,

src/skill_seekers/cli/adaptors/langchain.py

@@ -222,14 +222,14 @@ class LangChainAdaptor(SkillAdaptor):
         Returns:
             Result indicating no upload capability
         """
-        example_code = """
+        example_code = f"""
 # Example: Load into LangChain
 
 from langchain.schema import Document
 import json
 
 # Load documents
-with open("{path}") as f:
+with open("{package_path.name}") as f:
     docs_data = json.load(f)
 
 # Convert to LangChain Documents
@@ -247,9 +247,7 @@ retriever = vectorstore.as_retriever()
 
 # Query
 results = retriever.get_relevant_documents("your query here")
-""".format(
-            path=package_path.name
-        )
+"""
 
         return {
             "success": False,

src/skill_seekers/cli/adaptors/llama_index.py

@@ -245,7 +245,7 @@ class LlamaIndexAdaptor(SkillAdaptor):
         Returns:
             Result indicating no upload capability
         """
-        example_code = """
+        example_code = f"""
 # Example: Load into LlamaIndex
 
 from llama_index.core.schema import TextNode
@@ -253,7 +253,7 @@ from llama_index.core import VectorStoreIndex
 import json
 
 # Load nodes
-with open("{path}") as f:
+with open("{package_path.name}") as f:
     nodes_data = json.load(f)
 
 # Convert to LlamaIndex Nodes
@@ -275,9 +275,7 @@ query_engine = index.as_query_engine()
 # Query
 response = query_engine.query("your question here")
 print(response)
-""".format(
-            path=package_path.name
-        )
+"""
 
         return {
             "success": False,

src/skill_seekers/cli/adaptors/qdrant.py

@@ -261,7 +261,7 @@ class QdrantAdaptor(SkillAdaptor):
         Returns:
             Result with usage instructions
         """
-        example_code = """
+        example_code = f"""
 # Example: Create Qdrant collection and upload points
 
 from qdrant_client import QdrantClient
@@ -271,7 +271,7 @@ from pathlib import Path
 from openai import OpenAI
 
 # Load data
-with open("{path}") as f:
+with open("{package_path.name}") as f:
     data = json.load(f)
 
 # Connect to Qdrant (local or cloud)
@@ -438,7 +438,7 @@ similar = client.recommend(
     negative=["point-id-2"],  # But not this
     limit=5
 )
-""".format(path=package_path.name)
+"""
 
         return {
             "success": False,

src/skill_seekers/cli/adaptors/streaming_adaptor.py

@@ -8,7 +8,7 @@ Enables memory-efficient processing of large documentation sets.
 
 import json
 from pathlib import Path
-from typing import Any, Iterator, Optional
+from typing import Any
 import sys
 
 # Add parent directory to path for imports
@@ -36,7 +36,7 @@ class StreamingAdaptorMixin:
         chunk_size: int = 4000,
         chunk_overlap: int = 200,
         batch_size: int = 100,
-        progress_callback: Optional[callable] = None
+        progress_callback: callable | None = None
     ) -> Path:
         """
         Package skill using streaming ingestion.
@@ -179,7 +179,7 @@ class StreamingAdaptorMixin:
             Estimation statistics
         """
         skill_dir = Path(skill_dir)
-        ingester = StreamingIngester(
+        StreamingIngester(
             chunk_size=chunk_size,
             chunk_overlap=chunk_overlap
         )