by arm · Agent Tool · ★ 877
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/arm/metis.git
docker run --rm -it -v "$PWD:/metis" metisTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 877 |
| Forks | 126 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 70/100 |
| Open Issues | 12 |
| Last Updated | 2026-10-08 |
| Created | 2025-07-07 |
| Platforms | python |
| Est. Tokens | ~18k |
Metis: AI-Powered Security Code Review Metis is an open-source, agentic AI security framework for deep security code review, created by Arm's Product Security Team. It helps engineers detect subtle vulnerabilities, improve secure coding practices, and reduce review fatigue. This is especially valuable in large, complex, or legacy codebases where traditional tooling often falls short.
Looking for a metis alternative? If you're comparing metis with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: