Agent skill · Code Review & Quality

red-team-verifier-patrick-munro

Adversarial verification of AI-generated legal content with systematic fact-checking, source validation, and quality control. Use when a user asks to verify, fact-check, red-team, validate sources, or quality-control a legal document, briefing, compliance summary, or regulatory analysis before it is distributed to clients, stakeholders, or published. Trigger phrases include "verify", "fact-check", "red team", "red-flag", "check accuracy", "validate sources", "quality control", "is this correct", and "review for errors". Produces a structured verification report with severity-categorized errors

lawve-aigithub.com/lawve-aiGitHub ↗
claude-codeNOASSERTION
Install
npx skills add lawve-ai/awesome-legal-skills --skill red-team-verifier-patrick-munro --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 18 KB
Bundled scripts: none
Version: 2026-04-25
Declared author: Patrick Munro
Path: skills/red-team-verifier-patrick-munro/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 618
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Red Team Verifier ## Purpose This skill provides systematic adversarial verification of AI-generated legal content to establish factual accuracy, proper legal citation, and appropriate disclaimers before the content is distributed to clients or stakeholders. It addresses the core concern about AI in legal practice: *how do I know this is accurate?* The output is a structured verification report, not a reassurance. Where a claim cannot be confirmed against an official source, the skill reports it as unsupported rather than as true. ## When to use - Verification of AI-generated legal content before client or stakeholder distribution - Fact-checking of legal snapshots, briefings, or analyses - Quality control on compliance documents, regulatory summaries, or legal reports - Red-team review of legal outputs before publication - Adversarial testing of legal claims or arguments in draft materials **Trigger phrases**: verify, fact-check, red team, red-flag, check accuracy, validate sources, quality control, is this correct, review for errors. ## Verification stance Every factual claim, citation, date, and number in the input document is treated as unverified until an official source con

What's inside
Steps it walks through
  1. Purpose
  2. When to use
  3. Verification stance
  4. Core verification categories
  5. 1. Factual accuracy
  6. 2. Legal authority citations
  7. 3. Arithmetic validation
  8. 4. Source verification
  9. 5. Speculation detection
  10. 6. Disclaimer adequacy
  11. Verification methodology
  12. Step 1: Initial content review
  13. Step 2: Source verification
  14. Step 3: Arithmetic verification
Ships with 2 files
  • LICENSE.txt
  • README.md
More from awesome-legal-skills
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About this skill
What does the red-team-verifier-patrick-munro skill do?

Adversarial verification of AI-generated legal content with systematic fact-checking, source validation, and quality control. Use when a user asks to verify, fact-check, red-team, validate sources, or quality-control a legal document, briefing, compliance summary, or regulatory analysis before it is distributed to clients, stakeholders, or published. Trigger phrases include "verify", "fact-check", "red team", "red-flag", "check accuracy", "validate sources", "quality control", "is this correct", and "review for errors". Produces a structured verification report with severity-categorized errors

How do I install it?

Run `npx skills add lawve-ai/awesome-legal-skills --skill red-team-verifier-patrick-munro --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From lawve-ai/awesome-legal-skills, a repository with 618 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going