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/embedding/cache.py

@@ -5,7 +5,6 @@ Caching layer for embeddings.
 import json
 import sqlite3
 from pathlib import Path
-from typing import List, Optional, Tuple
 from datetime import datetime, timedelta
 
 
@@ -78,7 +77,7 @@ class EmbeddingCache:
     def set(
         self,
         hash_key: str,
-        embedding: List[float],
+        embedding: list[float],
         model: str
     ) -> None:
         """
@@ -103,7 +102,7 @@ class EmbeddingCache:
 
         self.conn.commit()
 
-    def get(self, hash_key: str) -> Optional[List[float]]:
+    def get(self, hash_key: str) -> list[float] | None:
         """
         Retrieve embedding from cache.
 
@@ -146,7 +145,7 @@ class EmbeddingCache:
 
         return json.loads(embedding_json)
 
-    def get_batch(self, hash_keys: List[str]) -> Tuple[List[Optional[List[float]]], List[bool]]:
+    def get_batch(self, hash_keys: list[str]) -> tuple[list[list[float] | None], list[bool]]:
         """
         Retrieve multiple embeddings from cache.
 
@@ -214,7 +213,7 @@ class EmbeddingCache:
 
         self.conn.commit()
 
-    def clear(self, model: Optional[str] = None) -> int:
+    def clear(self, model: str | None = None) -> int:
         """
         Clear cache entries.
 

src/skill_seekers/embedding/generator.py

@@ -4,7 +4,6 @@ Embedding generation with multiple model support.
 
 import os
 import hashlib
-from typing import List, Optional, Tuple
 import numpy as np
 
 # OpenAI support
@@ -128,9 +127,9 @@ class EmbeddingGenerator:
 
     def __init__(
         self,
-        api_key: Optional[str] = None,
-        voyage_api_key: Optional[str] = None,
-        cache_dir: Optional[str] = None
+        api_key: str | None = None,
+        voyage_api_key: str | None = None,
+        cache_dir: str | None = None
     ):
         """
         Initialize embedding generator.
@@ -168,7 +167,7 @@ class EmbeddingGenerator:
             )
         return self.MODELS[model]
 
-    def list_models(self) -> List[dict]:
+    def list_models(self) -> list[dict]:
         """List all available models."""
         models = []
         for name, info in self.MODELS.items():
@@ -186,7 +185,7 @@ class EmbeddingGenerator:
         text: str,
         model: str = "text-embedding-3-small",
         normalize: bool = True
-    ) -> List[float]:
+    ) -> list[float]:
         """
         Generate embedding for a single text.
 
@@ -216,11 +215,11 @@ class EmbeddingGenerator:
 
     def generate_batch(
         self,
-        texts: List[str],
+        texts: list[str],
         model: str = "text-embedding-3-small",
         normalize: bool = True,
         batch_size: int = 32
-    ) -> Tuple[List[List[float]], int]:
+    ) -> tuple[list[list[float]], int]:
         """
         Generate embeddings for multiple texts.
 
@@ -251,7 +250,7 @@ class EmbeddingGenerator:
 
     def _generate_openai(
         self, text: str, model: str, normalize: bool
-    ) -> List[float]:
+    ) -> list[float]:
         """Generate embedding using OpenAI API."""
         if not OPENAI_AVAILABLE:
             raise ImportError(
@@ -277,8 +276,8 @@ class EmbeddingGenerator:
             raise Exception(f"OpenAI embedding generation failed: {e}")
 
     def _generate_openai_batch(
-        self, texts: List[str], model: str, normalize: bool, batch_size: int
-    ) -> Tuple[List[List[float]], int]:
+        self, texts: list[str], model: str, normalize: bool, batch_size: int
+    ) -> tuple[list[list[float]], int]:
         """Generate embeddings using OpenAI API in batches."""
         if not OPENAI_AVAILABLE:
             raise ImportError(
@@ -316,7 +315,7 @@ class EmbeddingGenerator:
 
     def _generate_voyage(
         self, text: str, model: str, normalize: bool
-    ) -> List[float]:
+    ) -> list[float]:
         """Generate embedding using Voyage AI API."""
         if not VOYAGE_AVAILABLE:
             raise ImportError(
@@ -342,8 +341,8 @@ class EmbeddingGenerator:
             raise Exception(f"Voyage AI embedding generation failed: {e}")
 
     def _generate_voyage_batch(
-        self, texts: List[str], model: str, normalize: bool, batch_size: int
-    ) -> Tuple[List[List[float]], int]:
+        self, texts: list[str], model: str, normalize: bool, batch_size: int
+    ) -> tuple[list[list[float]], int]:
         """Generate embeddings using Voyage AI API in batches."""
         if not VOYAGE_AVAILABLE:
             raise ImportError(
@@ -381,7 +380,7 @@ class EmbeddingGenerator:
 
     def _generate_sentence_transformer(
         self, text: str, model: str, normalize: bool
-    ) -> List[float]:
+    ) -> list[float]:
         """Generate embedding using sentence-transformers."""
         if not SENTENCE_TRANSFORMERS_AVAILABLE:
             raise ImportError(
@@ -401,8 +400,8 @@ class EmbeddingGenerator:
         return embedding.tolist()
 
     def _generate_sentence_transformer_batch(
-        self, texts: List[str], model: str, normalize: bool, batch_size: int
-    ) -> Tuple[List[List[float]], int]:
+        self, texts: list[str], model: str, normalize: bool, batch_size: int
+    ) -> tuple[list[list[float]], int]:
         """Generate embeddings using sentence-transformers in batches."""
         if not SENTENCE_TRANSFORMERS_AVAILABLE:
             raise ImportError(
@@ -428,7 +427,7 @@ class EmbeddingGenerator:
         return embeddings.tolist(), dimensions
 
     @staticmethod
-    def _normalize(embedding: List[float]) -> List[float]:
+    def _normalize(embedding: list[float]) -> list[float]:
         """Normalize embedding to unit length."""
         vec = np.array(embedding)
         norm = np.linalg.norm(vec)

src/skill_seekers/embedding/models.py

@@ -2,7 +2,7 @@
 Pydantic models for embedding API.
 """
 
-from typing import List, Optional, Dict, Any
+from typing import Any
 from pydantic import BaseModel, Field
 
 
@@ -32,7 +32,7 @@ class EmbeddingRequest(BaseModel):
 class BatchEmbeddingRequest(BaseModel):
     """Request model for batch embedding generation."""
 
-    texts: List[str] = Field(..., description="List of texts to embed")
+    texts: list[str] = Field(..., description="List of texts to embed")
     model: str = Field(
         default="text-embedding-3-small",
         description="Embedding model to use"
@@ -41,7 +41,7 @@ class BatchEmbeddingRequest(BaseModel):
         default=True,
         description="Normalize embeddings to unit length"
     )
-    batch_size: Optional[int] = Field(
+    batch_size: int | None = Field(
         default=32,
         description="Batch size for processing (default: 32)"
     )
@@ -64,7 +64,7 @@ class BatchEmbeddingRequest(BaseModel):
 class EmbeddingResponse(BaseModel):
     """Response model for embedding generation."""
 
-    embedding: List[float] = Field(..., description="Generated embedding vector")
+    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(
@@ -76,7 +76,7 @@ class EmbeddingResponse(BaseModel):
 class BatchEmbeddingResponse(BaseModel):
     """Response model for batch embedding generation."""
 
-    embeddings: List[List[float]] = Field(..., description="List of embedding vectors")
+    embeddings: list[list[float]] = Field(..., description="List of embedding vectors")
     model: str = Field(..., description="Model used for generation")
     dimensions: int = Field(..., description="Embedding dimensions")
     count: int = Field(..., description="Number of embeddings generated")
@@ -121,7 +121,7 @@ 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(
+    metadata: dict[str, Any] = Field(
         default_factory=dict,
         description="Skill metadata"
     )
@@ -132,9 +132,9 @@ class HealthResponse(BaseModel):
 
     status: str = Field(..., description="Service status")
     version: str = Field(..., description="API version")
-    models: List[str] = Field(..., description="Available embedding models")
+    models: list[str] = Field(..., description="Available embedding models")
     cache_enabled: bool = Field(..., description="Whether cache is enabled")
-    cache_size: Optional[int] = Field(None, description="Number of cached embeddings")
+    cache_size: int | None = Field(None, description="Number of cached embeddings")
 
 
 class ModelInfo(BaseModel):
@@ -144,7 +144,7 @@ class ModelInfo(BaseModel):
     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: Optional[float] = Field(
+    cost_per_million: float | None = Field(
         None,
         description="Cost per million tokens (if applicable)"
     )
@@ -153,5 +153,5 @@ class ModelInfo(BaseModel):
 class ModelsResponse(BaseModel):
     """Response model for listing available models."""
 
-    models: List[ModelInfo] = Field(..., description="List of available models")
+    models: list[ModelInfo] = Field(..., description="List of available models")
     count: int = Field(..., description="Number of available models")

src/skill_seekers/embedding/server.py

@@ -20,7 +20,6 @@ Usage:
 import os
 import sys
 from pathlib import Path
-from typing import List, Optional
 
 try:
     from fastapi import FastAPI, HTTPException, Query
@@ -208,7 +207,7 @@ if FASTAPI_AVAILABLE:
                 )
 
                 # Fill in placeholders and cache
-                for idx, text, embedding in zip(text_indices, texts_to_generate, generated_embeddings):
+                for idx, text, embedding in zip(text_indices, texts_to_generate, generated_embeddings, strict=False):
                     embeddings[idx] = embedding
 
                     if cache:
@@ -300,7 +299,7 @@ if FASTAPI_AVAILABLE:
 
     @app.post("/cache/clear", response_model=dict)
     async def clear_cache(
-        model: Optional[str] = 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: