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/benchmark_cli.py

@@ -42,17 +42,15 @@ def run_scraping_benchmark(runner, config):
         scrape_config_path = config.get("scrape_config")
 
         # Time scraping
-        with bench.timer("scrape_docs"):
-            with bench.memory("scrape_docs"):
-                pages = scrape_all(scrape_config_path)
+        with bench.timer("scrape_docs"), bench.memory("scrape_docs"):
+            pages = scrape_all(scrape_config_path)
 
         # Track metrics
         bench.metric("pages_scraped", len(pages), "pages")
 
         # Time building
-        with bench.timer("build_skill"):
-            with bench.memory("build_skill"):
-                build_skill(scrape_config_path, pages)
+        with bench.timer("build_skill"), bench.memory("build_skill"):
+            build_skill(scrape_config_path, pages)
 
     name = config.get("name", "scraping-benchmark")
     report = runner.run(name, benchmark_func)
@@ -76,9 +74,8 @@ def run_embedding_benchmark(runner, config):
 
         # Batch embedding
         if len(texts) > 1:
-            with bench.timer("batch_embedding"):
-                with bench.memory("batch_embedding"):
-                    embeddings = generator.generate_batch(texts, model=model)
+            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")