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 vasilyu1983/AI-Agents-public --skill document-docxgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/document-docx)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/document-docx"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/document-docx/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/vasilyu1983/ai-agents-public/document-docx"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/document-docx.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00038 | $0.03226 |
| Opus 5 | $0.00019 | $0.01613 |
| Sonnet 5 | $0.00008 | $0.00645 |
| Haiku 4.5 | $0.00004 | $0.00323 |
Grade A, and why
document-docx 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document DOCX Skill - Quick Reference
This skill covers creation, editing, review, extraction, and release workflows for .docx documents.
Modern best practices (Jul 2026):
- Treat
.docxas the editable source and PDF as a release artifact. - Prefer templates and built-in styles over manual formatting.
- Use comments for review notes; use Word Compare for true redlines.
- For LLM/RAG extraction, optimize for structure, trust level, and sanitization rather than visual fidelity.
- Treat macro-enabled Office files (
.docm,.dotm) as untrusted by default. - Before promising a feature (comments, alt text, tracked changes), check the installed library version — several of these APIs are recent additions and silently absent on older pins. See "Version-Gate Before Promising A Feature" below.
Core Decision Rules (2026)
- If non-developers need to own layout/design, prefer
docxtplwith a Word-authored template. - If the stack is Python and edits are structural, prefer
python-docx. - If the stack is TypeScript/Node and the output is generated server-side, prefer
docx. - If you need semantic HTML from a trusted document, prefer
mammoth, then sanitize before rendering or storing the output. - If you need Markdown/JSON for search, indexing, or RAG, prefer
MarkItDownorDocling. - If the user asks for tracked changes, do not promise high-level library support. Generate a revised
.docxand use Word Compare, or switch to OOXML-specialized tooling. - If the user asks for PDF output, prefer Word automation for highest fidelity and LibreOffice headless for cross-platform batch workflows.
- If the input is
.doc, convert to.docxfirst. If it is.docmor.dotm, do not trust embedded macros.
Quick Reference
| Task | Tool/Library | Language | When to Use |
|---|---|---|---|
| Create/edit DOCX | python-docx |
Python | Structural edits, reports, contracts, section/table/image work |
| Create/edit DOCX | docx |
Node.js | Server-side generation in TypeScript-heavy stacks |
| Template fill | docxtpl |
Python | Word-authored templates, mail merge, batch documents |
| Add/access comments | python-docx + Word review workflow |
Python / Word | Review notes without tracked revisions |
| Convert DOCX to HTML | mammoth |
Node.js | Semantic HTML from trusted documents |
| Convert DOCX to Markdown | MarkItDown |
Python | LLM/RAG ingestion where Markdown is preferred |
| Convert DOCX to Markdown/HTML/JSON | Docling |
Python / CLI | Multi-format ingestion, structured extraction, batch conversion |
| Parse text/tables/metadata | python-docx + OOXML inspection |
Python | Extraction, audits, migration tooling |
| Parse tracked changes/comments | OOXML, Open XML SDK, docx4j, Aspose.Words | Python / .NET / Java | Revision-heavy workflows and interoperability edge cases |
| Convert DOCX to PDF | Word automation / LibreOffice headless | OS tooling | Release artifacts and cross-platform smoke checks |
What ships with it
22 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.
- agents/openai.yaml 378 B
- assets/contract-template.md 13 KB
- assets/doc-template-pack.md 1.9 KB
- assets/docx-template-authoring-checklist.md 1.8 KB
- assets/report-template.md 8.3 KB
- data/sources.json 8.4 KB
- learnings.consolidated.md 589 B
- learnings.md 789 B
- references/accessibility-compliance.md 6.7 KB
- references/cross-platform-compatibility.md 6.4 KB
- references/document-automation-pipelines.md 6.5 KB
- references/docx-patterns.md 13 KB
- references/extraction-stack.md 975 B
- references/llm-extraction-workflows.md 4.8 KB
- references/review-comments-workflows.md 3.7 KB
- references/template-workflows.md 13 KB
- references/tracked-changes.md 2.0 KB
- scripts/docx_extract.py 7.3 KB runs code
- scripts/docx_inspect_ooxml.py 3.1 KB runs code
- scripts/docx_quality_gate.py 6.1 KB runs code
- scripts/docx_render_template.py 1.7 KB runs code
- scripts/docx_to_html.mjs 4.3 KB runs code
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.
- 10d ago First seen · 256 lines · 38 tokens per session scan A 929c7c665254
document-docx is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 8d ago), licensed MIT. It adds 38 tokens to every session and 3,226 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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