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 eigenweltlabs/legalwork --skill docx-editgit clone --depth 1 https://github.com/eigenweltlabs/legalworkWrote 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/eigenweltlabs/legalwork/docx-edit)<a href="https://agentmods.dev/skills/eigenweltlabs/legalwork/docx-edit"><img src="https://agentmods.dev/badge/skills/eigenweltlabs/legalwork/docx-edit/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/eigenweltlabs/legalwork/docx-edit"><img src="https://agentmods.dev/badge/skills/eigenweltlabs/legalwork/docx-edit.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 | $0.00128 | $0.03953 |
| Opus 5.5 | $0.00051 | $0.01581 |
| Sonnet 5.5 | $0.00026 | $0.00791 |
| Haiku 4.5 | $0.00013 | $0.00395 |
Grade A, and why
docx-edit 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Word (.docx) reading + editing
For a due diligence report using the bundled Due Diligence template, call
legalwork_review_report_prepare with the saved review IDs and original evidence
indexes. Read its bounded class files, write legal prose directly into the draft
JSON, and run assets/report-from-reviews.py --project <project> --packet <packet.json> --data <draft.json> --template <template.docx> --out <reports/report.docx>. The helper expands source references, verifies quotations,
builds the report tables and preserves the template in one operation. Findings use
citations: [{sourceRef, page, quote}]; a shared quotation can use
citationGroups: [{reviewId, column, values, page, quote}] to expand a saved answer
group internally. Each expanded citation is still checked. Open decisions remain
open. Follow the workflow's report instructions and field map. For a different
firm template, use the generic template population helper below. Do not generate
assembly scripts, read all source documents again, inspect
the template repeatedly, or turn routine report drafting into a second full review.
For creating a new report from a supplied template, use the deterministic
assets/populate-template.py helper instead of generating a Word-building script.
python3 <skill>/assets/populate-template.py inspect --template <template.docx>
returns the field keys and repeated-row tokens. Save the report content as JSON
{"fields": {"token": "text"}, "tables": [{"tokens": ["id", "issue"], "rows": [{"id": "F-01", "issue": "Finding"}]}]},
then run populate --template <template.docx> --data <content.json> --out <report.docx>.
This preserves formatted runs, artwork, headers and repeated-row designs, supports
tokens split across runs, and fails on missing fields. Source/legal validation and
the editor's actual page count still require separate checks. It is for new file
creation; use the live editing backend for an already-open document.
For source-cited reports from a native Jev evidence export, use
assets/verify-evidence.py --project <project> --index <evidence/index.json> --register <register.json> --out <checks.json> before treating quotations or
uncertainty dispositions as verified. It checks current source hashes and literal
page support, requires one citation per affected source, writes diagnostics to disk
and prints only counts. It does not verify legal conclusions or exhaustive scope.
This skill is how this firm reads and edits Word documents with AI. It has three backends, and your FIRST step is always to pick the right one:
What ships with it
5 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.
- 10d ago Changed · +38 lines 9037c4667a4c
- 13d ago Changed · +13 lines cfdbc0eb5489
- 23d ago Changed · +33 lines 12f9fe78310e
- 1mo ago Changed · +13 lines · -17 tokens per session b6330a317451
- 1mo ago First seen · 140 lines · 145 tokens per session scan A 9600f3985c51
docx-edit is a skill published in the GitHub repository eigenweltlabs/legalwork (129 stars, last pushed today), licensed MIT. It adds 128 tokens to every session and 3,953 once invoked, about $0.0005 per session on Opus 5.5. 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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