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Featured in: TypeSafe Jev
git clone https://github.com/kyegomez/open-jevTaken from the project's README on GitHub.
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.
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 | 142 |
| Forks | 26 |
| Language | Python |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 71/100 |
| Last Updated | 2026-09-21 |
| Created | 2026-09-21 |
| Platforms | claude-code, python |
| Est. Tokens | ~3k |
Open Jev An open-source, from-first-principles reconstruction of the ideas behind TypeSafe AI's Jev, written in PyTorch. [!IMPORTANT] This is an unofficial research implementation with random weights. It is not the production Jev model, does not reproduce TypeSafe AI's training data or weights, and makes no claim of matching their published results.
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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: