docx-smart-extractor

Word document extraction with token-efficient processing uses semantic chunking and caching to reduce token consumption by 10–50x. This Skill extracts .docx and .docm files locally, preserving headings, tables, and metadata as structured JSON output without sending data to LLMs, enabling fast offline analysis of documents up to 50 MB+.

Yes. Local extraction of Word documents preserves formatting, tables, headings, comments, and tracked changes without requiring internet or LLM calls. The Skill outputs structured JSON artifacts—full_document.json, headings.json, tables.json, metadata.json—enabling immediate querying and downstream processing of files ranging from 0.5 MB to 50 MB+.

Extract Word documents locally using semantic chunking to organize content into queryable chunks, then cache results for instant reuse. This approach preserves contract structure, policy headings, and metadata while eliminating token overhead, making it ideal for reviewing large documents without LLM processing costs.

No. This Skill performs zero-LLM extraction, running entirely offline on .docx and .docm files to pull headings, tables, metadata, and comments into structured JSON. You can immediately query cached results without external API calls, making it suitable for confidential documents and air-gapped environments.

Semantic chunking organizes document content into meaningful segments by structure—headings, tables, paragraphs—rather than arbitrary splits. Combined with caching, it enables reuse and targeted querying, cutting token consumption 10–50x compared to processing full documents repeatedly.

Extraction supports .docx and .docm files from 0.5 MB to 50 MB+ with full preservation of headings, tables, metadata, comments, and tracked changes. Output is structured JSON artifacts enabling efficient offline analysis without streaming or size-imposed LLM constraints.