by devagrawal09 · AI Tool · ★ 696
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.
One of 11 tools we ran end to end on the same task for our TypeSafe Jev page (2026-10-11).
Result: its quick start ran on a Jev key alone · 3 steps beyond the README · 56 s to install and run
| Stars | 696 |
| Forks | 47 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 71/100 |
| Open Issues | 9 |
| Last Updated | 2026-09-17 |
| Created | 2026-09-16 |
| Platforms | node |
| Est. Tokens | ~12k |
Jev Review A small code-review workflow built with TypeSafe Jev. It can review a Git diff or scan a complete codebase, follows the strongest structured signals through focused model calls, and presents the result in a quiet local dashboard. How It Works The reviewer keeps orchestration in code and uses Jev for bounded judgments: Exposes separate change-review and complete-codebase entry points.
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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: