by Zefan-Cai · AI Tool · ★ 425
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git clone https://github.com/Zefan-Cai/Open-Jev.gitTaken from the project's README on GitHub. Check the security audit before running it.
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 | 425 |
| Forks | 61 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 53/100 |
| Open Issues | 2 |
| Last Updated | 2026-10-04 |
| Created | 2026-09-20 |
| Platforms | python |
| Est. Tokens | ~17k |
Open-Jev Open probability decisions with Qwen3.5-2B, Qwen3.5-9B and Qwen3.8-27B. Supply a context, questions and candidates; get typed probabilities directly, without autoregressive answer generation or parsing generated JSON. Website and successful demos · Code · Dataset · 2B checkpoint · 9B checkpoint Public releases: the dataset and completed 2B/9B checkpoints are available at the links above.
These tools work well together with Open-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: