fix: resolve 18 bugs and code quality issues across adaptors, CLI, and chunking pipeline

Bug fixes:
- Fix --var flag silently dropped in create routing (args.workflow_var → args.var)
- Fix double _score_code_quality() call in word scraper
- Add .docx file extension validation in WordToSkillConverter
- Fix weaviate ImportError masked by generic Exception handler
- Fix RAG chunking crash using non-existent converter.output_dir

Chunking pipeline improvements:
- Wire --chunk-overlap-tokens through entire package pipeline
  (package_skill → adaptor.package → format_skill_md → _maybe_chunk_content → RAGChunker)
- Add auto-scaling overlap: max(50, chunk_tokens//10) when chunk size is non-default
- Rename --no-preserve-code to --no-preserve-code-blocks (backward-compat alias kept)
- Replace hardcoded 512/50 chunk defaults with DEFAULT_CHUNK_TOKENS/DEFAULT_CHUNK_OVERLAP_TOKENS
  constants across all 12 concrete adaptors, rag_chunker, base, and package_skill

Code quality:
- Extract shared _generate_openai_embeddings() and _generate_st_embeddings() to SkillAdaptor
  base class, removing ~150 lines of duplication from chroma/weaviate/pinecone
- Add Pinecone adaptor with full upload support (pinecone_adaptor.py)

Tests (14 new):
- chunk_overlap_tokens parameter wiring, auto-scaling overlap, preserve_code_blocks flag
- .docx/.doc/no-extension file validation, --var flag routing E2E
- Embedding method inheritance verification, backward-compatible flag aliases

Docs:
- Update CHANGELOG, CLI_REFERENCE, API_REFERENCE, packaging guide (EN+ZH)
- Update README test count badge (1880+ → 2283+)

All 2283 tests passing, 8 skipped, 0 failures.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/skill_seekers/cli/word_scraper.py

@@ -109,6 +109,11 @@ class WordToSkillConverter:
         if not os.path.exists(self.docx_path):
             raise FileNotFoundError(f"Word document not found: {self.docx_path}")
 
+        if not self.docx_path.lower().endswith(".docx"):
+            raise ValueError(
+                f"Not a Word document (expected .docx): {self.docx_path}"
+            )
+
         # --- Extract metadata via python-docx ---
         doc = python_docx.Document(self.docx_path)
         core_props = doc.core_properties
@@ -825,8 +830,8 @@ def _build_section(
             raw_text = elem.get_text(separator="\n").strip()
             # Exclude bullet-point / prose lists (•, *, -)
             if raw_text and not re.search(r"^[•\-\*]\s", raw_text, re.MULTILINE):
-                if _score_code_quality(raw_text) >= 5.5:
-                    quality_score = _score_code_quality(raw_text)
+                quality_score = _score_code_quality(raw_text)
+                if quality_score >= 5.5:
                     code_samples.append(
                         {"code": raw_text, "language": "", "quality_score": quality_score}
                     )