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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 | 51 |
| Forks | 2 |
| Language | Rust |
| Category | AI Tool |
| License | AGPL-3.0 |
| Quality Score | 68/100 |
| Open Issues | 2 |
| Last Updated | 2025-10-14 |
| Created | 2025-10-11 |
| Platforms | docker, rust |
| Est. Tokens | ~23k |
auditlm A dead-simple, self-hostable code review bot with Forgejo integration. Sometimes you don't have a human to review your changes, and sometimes you don't want to give Microsoft more training data for Copilot. You can think of auditlm as an open-source and self-hosted CodeRabbit. I've using it to assist with its own development, so you can see it in action on some of the recent PRs.
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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: