Extract text, tables, images, metadata, and tracked changes from Word documents.
npx skills add https://github.com/Stratio/genai-agents --skill docx-reader
Please help me install this Agent Skill. Skill: docx-reader Source: https://github.com/Stratio/genai-agents/tree/main/shared-skills/docx-reader Command: npx skills add https://github.com/Stratio/genai-agents --skill docx-reader
Ingest and extract content from Word documents to obtain prose, tables, images, metadata, comments, and tracked changes, enabling faster content analysis, indexing, and governance of documentation.
Run the quick_extract.py script on a DOCX document to obtain a Markdown-formatted summary.
High-intent search queries and answers about installing and using this skill.
To extract text and tables from Word documents, run a Python script that parses .docx files to return structured Markdown output. This process quickly ingests prose and tabular data into formats suitable for feeding LLMs and downstream pipelines.
Yes, you can extract tracked changes and comments from a docx file using deep analysis mode. This mode surfaces revisions and annotations present in the document, making it ideal for parsing heavily edited contracts and governance workflows.
The best way to parse legacy .doc files in Python is to convert them to modern .docx format first. This conversion ensures reliable parsing and content extraction using libraries like python-docx and lxml for stable structural analysis.
python-docx supports extracting core metadata and images from Word files when combined with lxml. This combination allows you to retrieve document properties and media alongside text for comprehensive content indexing and analysis.
Quick text extraction from Word may fail on complex contracts due to heavy tracked changes or nested tables. A deterministic fallback to deep analysis mode thoroughly parses these complex structures to ensure accurate content retrieval.
You can convert Word document content to Markdown for LLM ingestion by running a quick extraction script on the .docx file. This produces Markdown-ready text and tables, bypassing binary formatting issues for clean downstream processing.
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