by tinnel123666888 · AI Tool · ★ 168
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git clone https://github.com/tinnel123666888/OmniJev && cd OmniJevTaken 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 | 168 |
| Forks | 9 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 46/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-28 |
| Created | 2026-09-23 |
| Platforms | python |
| Est. Tokens | ~17k |
OmniJev An omni-modal Jev · 全模态 Jev One forward pass, zero generated tokens: typed questions about images, videos, screens and robot scenes, answered with calibrated probabilities. Beijing Zhongguancun Academy · Institute of Automation, Chinese Academy of Sciences · Zevo Website · Try it online · Weights · 中文文档 What it can do OmniJev is a model we trained end to end — not the Jev API with...
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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: