by kyle-pena-nlp · Agent Tool · ★ 98
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 | 98 |
| Forks | 6 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 54/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-20 |
| Platforms | python |
| Est. Tokens | ~15k |
jevchat We know Jev. turns that into a chat model. At every step it asks Jev one question: Given the user's question and the reply written so far, which symbol comes next? The options are an alphabet plus an option to stop emitting. Jev returns a probability for each one, and the sampler draws the next symbol from that normalised distribution. Append, repeat, and stop when STOP is drawn.
These tools work well together with jevchat 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: