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 qagit 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/qa)<a href="https://agentmods.dev/skills/anylegal-ai/anylegal-oss/qa"><img src="https://agentmods.dev/badge/skills/anylegal-ai/anylegal-oss/qa/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/qa"><img src="https://agentmods.dev/badge/skills/anylegal-ai/anylegal-oss/qa.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 | $0.00031 | $0.01002 |
| Opus 5 | $0.00015 | $0.00501 |
| Sonnet 5 | $0.00006 | $0.00200 |
| Haiku 4.5 | $0.00003 | $0.00100 |
Grade A, and why
qa 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Assurance Review
When to Use
Use this skill when:
- The user says /qa
- A matter is transitioning from WORKING to REVIEW
- Counsel requests a fresh QA pass after a return
- The agent has finished editing a document and needs verification
Process
1. Inventory the Workspace
Call list_documents to see all files. Identify:
- The primary deliverable (the main contract/document being worked on)
- The intake summary (
intake_*.md) for client instructions - Any playbook files (
Playbook/*.md,anylegal.md) - Any research notes or memos created during the work
- The original uploaded document (if different from the deliverable)
2. Template Compliance Check
Read the deliverable with read_document. Check:
- Document structure matches the expected template format
- Clause numbering is sequential and correct
- Cross-references (to other clauses, schedules, annexes) are accurate
- Defined terms are used consistently (capitalization, spelling)
- No placeholder text remains (e.g., "[INSERT]", "TBD", "XXX")
- Standard boilerplate clauses are present (severability, governing law, etc.)
3. Factual Accuracy Check
Verify legal facts in the document:
- Jurisdiction references are correct for the matter type
- Legal citations (statutes, regulations, case law) are valid — use
web_searchto spot-check - Dates, deadlines, and time periods are internally consistent
- Party names and entity types are correct throughout
- Currency and monetary amounts are consistent
4. Completeness Check
Read the intake summary and compare against the deliverable:
- All client instructions from intake have been addressed
- All documents mentioned in intake have been reviewed/incorporated
- No sections are left unfinished or contain TODO markers
- If multi-jurisdictional, all specified jurisdictions are covered
5. Track Changes Review
Use run_code (default Python) with lxml to count tracked changes (w:ins, w:del), extract authors, and assess edit scope. Then:
- Every insertion has a clear purpose (improves protection, fixes error, adds required clause)
- Every deletion is justified (removes risk, corrects error, simplifies)
- No unintended formatting-only changes
- Tracked changes are clean (no stacked/overlapping revisions)
- Total edit count is proportionate to the matter scope
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 · 128 lines · 31 tokens per session scan A 25f10dfe8030
qa is a skill published in the GitHub repository anylegal-ai/anylegal-oss (11 stars, last pushed 4mo ago), licensed MIT. It adds 31 tokens to every session and 1,002 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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