Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NEU-ZHA/legal-ai-skills --skill docx-toolkitgit clone --depth 1 https://github.com/NEU-ZHA/legal-ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/neu-zha/legal-ai-skills/docx-toolkit)<a href="https://agentmods.dev/skills/neu-zha/legal-ai-skills/docx-toolkit"><img src="https://agentmods.dev/badge/skills/neu-zha/legal-ai-skills/docx-toolkit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/neu-zha/legal-ai-skills/docx-toolkit"><img src="https://agentmods.dev/badge/skills/neu-zha/legal-ai-skills/docx-toolkit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.00580 |
| Opus 5 | $0.00023 | $0.00290 |
| Sonnet 5 | $0.00009 | $0.00116 |
| Haiku 4.5 | $0.00005 | $0.00058 |
Grade A, and why
docx-toolkit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
DOCX Toolkit
A complete toolkit for processing Microsoft Word documents (.docx and legacy .doc formats).
Capabilities
1. Text + Table Extraction (.docx)
python3 {baseDir}/scripts/extract_text.py input.docx output.txt
Extracts all paragraphs and tables with structure preserved. Tables are formatted as pipe-delimited rows for easy parsing.
2. Text Extraction (Legacy .doc)
python3 {baseDir}/scripts/extract_doc_text.py input.doc output.txt
Handles legacy OLE2 .doc format using olefile. Extracts Unicode text from the WordDocument stream.
3. Image Extraction (.docx)
python3 {baseDir}/scripts/extract_images.py input.docx output_dir/
Extracts all embedded images with:
- Automatic deduplication (MD5 hash comparison)
- Size filtering (skips tiny icons <5KB by default)
- Sequential renaming (img_001.png, img_002.jpg, etc.)
4. Image Compression
python3 {baseDir}/scripts/resize_images.py input_dir/ output_dir/ [--max-width 1024]
Batch resize/compress images for API processing (saves 50-70% on vision API costs).
Dependencies
- Python 3.6+
python-docx— for .docx processingolefile— for legacy .doc processingPillow— for image resizing (optional, only needed for resize script)
Install:
pip3 install python-docx olefile Pillow
Use Cases
- Document analysis: Extract text for AI review/summarization
- Migration: Pull content from Word docs into other formats
- Image audit: Extract and review all embedded images
- Cost optimization: Compress images before sending to vision APIs
- Batch processing: Process multiple documents in a pipeline
Notes
- Large .doc files (>200MB) may require significant RAM for olefile processing
- Image extraction preserves original format (png/jpg/gif/etc.)
- Deduplication catches exact duplicates; near-duplicates still pass through
- CJK (Chinese/Japanese/Korean) text is fully supported in both extractors
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 68 lines · 45 tokens per session scan A 22f3664606dc
docx-toolkit is a skill published in the GitHub repository NEU-ZHA/legal-ai-skills (64 stars, last pushed 23d ago), licensed MIT. It adds 45 tokens to every session and 580 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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