by kevinbadi · AI Tool · ★ 124
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 | 124 |
| Forks | 23 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 64/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-23 |
| Created | 2026-09-18 |
| Platforms | python |
| Est. Tokens | ~14k |
Jev Voice Talk to your Mac. You speak, it opens apps, types, searches, scrolls, presses keys. Everything runs locally except one 250 ms call to Jev (TypeSafe's System One model), which turns the transcript into a typed action plus typed arguments in a single fan-out request. Jev never generates text; code produces candidate values and Jev selects. Code owns execution.
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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: