skill-security-auditor
Audit an AI agent skill before installing it. Use proactively whenever the user is about to add, install, enable, or evaluate an unfamiliar skill — including phrases like "audit this skill", "is this skill safe", "scan skill before install", "check skill for malicious code", "review this plugin", or any pre-install gate on an untrusted, third-party, or community-distributed skill bundle. Runs ten categories of static checks (code execution, network exfiltration, credential harvesting, persistence, prompt injection, supply-chain hooks, obfuscation, destructive filesystem ops, secrets, Trojan So
npx skills add lawve-ai/awesome-legal-skills --skill skill-security-auditor-lawve --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Skill Security Auditor Static analysis for AI agent skills. Run it on any skill from outside your own trust boundary before letting an agent execute its scripts. The auditor walks the entire skill directory and emits findings across ten threat categories, then collapses them into a single verdict that CI can gate on. This skill is paranoid by design. It assumes a skill author may be trying to compromise the host system, exfiltrate credentials, persist beyond the session, or inject hostile instructions into the model. Most skills will trip a couple of LOW or MEDIUM findings — that's expected. The signal worth acting on is CRITICAL and HIGH. ## When to use Trigger this skill when the user: - Is about to install a third-party skill, plugin, or agent extension. - Asks "is this skill safe?", "audit it", "scan it", "is there anything sketchy in here?", or similar pre-install review questions. - Wants a security gate on a skill marketplace, internal skill registry, or CI pipeline that ingests user-submitted skills. - Just cloned a skill repo and wants a one-shot review before running it. Do **not** use this skill when the user is auditing their own first-party code — there are better st
- When to use
- Quick start
- What gets scanned
- Verdict criteria
- Reading a finding
- Suppression
- CI integration
- References
- Limitations
- Why this exists
Audit a local directory python3 scripts/audit.py /path/to/skill Audit a git repo (cloned to a temp dir, optionally cleaned up after) python3 scripts/audit.py https://github.com/example/some-skill.git --cleanup Strict mode — treat HIGH findings as blocking (recommended for CI) python3 scripts/audit.py ./skill --strict Machine-readable output for CI python3 scripts/audit.py ./skill --json --output report.json Markdown report suitable for pasting into a PR comment python3 scripts/audit.py ./skill --markdown --output review.md
What does the skill-security-auditor skill do?
Audit an AI agent skill before installing it. Use proactively whenever the user is about to add, install, enable, or evaluate an unfamiliar skill — including phrases like "audit this skill", "is this skill safe", "scan skill before install", "check skill for malicious code", "review this plugin", or any pre-install gate on an untrusted, third-party, or community-distributed skill bundle. Runs ten categories of static checks (code execution, network exfiltration, credential harvesting, persistence, prompt injection, supply-chain hooks, obfuscation, destructive filesystem ops, secrets, Trojan So
How do I install it?
Run `npx skills add lawve-ai/awesome-legal-skills --skill skill-security-auditor-lawve --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.
