by fstandhartinger · AI Tool · ★ 300
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 300 |
| Forks | 28 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 52/100 |
| Open Issues | 178 |
| Last Updated | 2026-10-15 |
| Created | 2026-09-19 |
| Platforms | python |
| Est. Tokens | ~19k |
JevBench A benchmark for Jev-class decision models: you hand the model a piece of state and a bounded rubric, and it hands back a typed answer, ideally with a probability for every option. No prose, no parsing, no "as an AI language model". JevBench is Benchmark Heaven's own benchmark. It is not affiliated with or endorsed by TypeSafe AI, whose Jev model is one of the systems measured here.
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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: