by yibie · LLM Plugin · ★ 2.5k
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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 | 2,537 |
| Forks | 373 |
| Language | Python |
| Category | LLM Plugin |
| Quality Score | 58/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-31 |
| Created | 2026-09-17 |
| Platforms | python |
| Est. Tokens | ~18k |
awesome-jev A curated awesome list of public projects and practices built on Jev, TypeSafe AI's System One model for typed decisions. This README is the homepage aggregate of the current category files, so the latest accepted entries are visible here without drilling into subpages. Jev is not a chat model.
These tools work well together with awesome-jev for enhanced workflows:
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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: