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 anylegal-ai/anylegal-oss --skill docx-xmlgit clone --depth 1 https://github.com/anylegal-ai/anylegal-ossWrote 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/anylegal-ai/anylegal-oss/docx-xml)<a href="https://agentmods.dev/skills/anylegal-ai/anylegal-oss/docx-xml"><img src="https://agentmods.dev/badge/skills/anylegal-ai/anylegal-oss/docx-xml/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/anylegal-ai/anylegal-oss/docx-xml"><img src="https://agentmods.dev/badge/skills/anylegal-ai/anylegal-oss/docx-xml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00063 | $0.03484 |
| Opus 5 | $0.00032 | $0.01742 |
| Sonnet 5 | $0.00013 | $0.00697 |
| Haiku 4.5 | $0.00006 | $0.00348 |
Grade A, and why
docx-xml 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.
How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DOCX XML — Advanced Skill
You are looking at this skill because a structural edit needs raw OOXML manipulation. Always check first whether edit_document can do it — see the docx-editing skill. Only drop to run_code for:
- Deleting a clause including its paragraph mark (needs
<w:p><w:pPr><w:rPr><w:del/></w:rPr></w:pPr>pattern) - Inserting multi-paragraph content mid-document
- Editing a part other than
word/document.xml(styles.xml, headers/footers, numbering.xml) - Bulk structural rewrites spanning dozens of paragraphs
Overview
A .docx file is a ZIP archive containing XML files. The main content is in word/document.xml. The OOXML namespace for Word is http://schemas.openxmlformats.org/wordprocessingml/2006/main (prefix w:).
Use language="python" + lxml + zipfile on the XML parts directly.
Why NOT docx-js: docx-js is write-only — there is no Document.load(...) API. It's for creating new DOCX files from scratch (the draft skill uses it). You cannot use it to load Contract.docx, mutate, and save back.
Why NOT python-docx for run-level mutation: python-docx silently corrupts complex legal templates. It loses non-target glyphs (other placeholders, special characters) when run.text is reassigned, has no reliable API for footnotes / bookmarks / internal hyperlinks / TableOfContents, and collapses <w:rPr> run properties when rewriting text — losing bold / font / size on surrounding runs. Never use python-docx for run.text.replace(...) or any run-level mutation. python-docx for reading and additive operations (adding images, adding paragraphs) is acceptable.
The right pattern — read XML, mutate with lxml, repack with zipfile
# run_code(language="python", input_files=["Contract.docx"], code=...)
import zipfile, io
from lxml import etree
NS = "http://schemas.openxmlformats.org/wordprocessingml/2006/main"
W = f"{{{NS}}}"
# 1. Read document.xml from the input DOCX.
with zipfile.ZipFile('/sandbox/input/Contract.docx') as zin:
doc_xml = zin.read('word/document.xml')
parts = {name: zin.read(name) for name in zin.namelist()}
# 2. Mutate with lxml (example — delete a specific paragraph including its mark).
root = etree.fromstring(doc_xml)
for p in root.iter(f"{W}p"):
text = "".join(t.text or "" for t in p.iter(f"{W}t"))
if "text-to-delete" in text:
p.getparent().remove(p)
# 3. Repack, preserving every other part.
parts['word/document.xml'] = etree.tostring(root, xml_declaration=True,
encoding='UTF-8', standalone=True)
buf = io.BytesIO()
with zipfile.ZipFile(buf, 'w', zipfile.ZIP_DEFLATED) as zout:
for name, data in parts.items():
zout.writestr(name, data)
with open('/sandbox/output/Contract_Edited.docx', 'wb') as f:
f.write(buf.getvalue())
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 · 303 lines · 63 tokens per session scan A c41fea302084
docx-xml is a skill published in the GitHub repository anylegal-ai/anylegal-oss (11 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 3,484 once invoked, about $0.0003 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.
Other skills, from other repositories
baoyu-youtube-transcript
A tool for downloading the written captions, subtitles, chapter information, speaker labels, and cover image from a YouTube video using its URL or ID.
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
read
Reads URLs and PDFs by fetching source content, defaulting to concise summaries for plain read requests and clean Markdown when asked to convert, save, quote, cite, or feed downstream work. Use when users ask in any language to read, fetch, check, summarize, quote, cite, convert, or save a URL or PDF. Not for local…
overleaf-sync
A two-way connection between a local paper folder and Overleaf, a web-based LaTeX editor for writing research papers. It lets you move changes between the local files and the shared Overleaf project.