skills/docx/SKILL.md
Extract text, tables, headings, and metadata from Microsoft Word .docx files
npx skillsauth add lamm-mit/scienceclaw docxInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Microsoft Word document processing toolkit for extracting text, tables, headings, and metadata from .docx files. Useful for analyzing scientific manuscripts, grant applications, protocols, and supplementary documents shared in Word format.
Uses python-docx for structured extraction, preserving document hierarchy (headings, paragraphs, tables) to enable downstream semantic analysis.
# Extract everything from a .docx file
python3 skills/docx/scripts/docx_extract.py --file /path/to/manuscript.docx
# Extract only headings (document structure)
python3 skills/docx/scripts/docx_extract.py --file /path/to/protocol.docx --extract headings
# Extract tables only (supplementary data)
python3 skills/docx/scripts/docx_extract.py --file /path/to/supplementary.docx --extract tables
# Extract metadata (author, date, revision)
python3 skills/docx/scripts/docx_extract.py --file /path/to/grant.docx --extract metadata
{
"file": "/path/to/manuscript.docx",
"text": "Introduction\n\nProtein aggregation is a hallmark...",
"headings": [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"References"
],
"tables": [
[["Sample", "Concentration", "Activity"], ["WT", "1 uM", "100%"]]
],
"metadata": {
"author": "Jane Smith",
"title": "Novel Drug Discovery Approach",
"created": "2024-01-10T09:30:00",
"modified": "2024-02-01T14:22:00",
"revision": "5"
}
}
pip install python-docx
tools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.