by alanhuangyoo · LLM Plugin · ★ 66
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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 | 66 |
| Forks | 4 |
| Language | Python |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 62/100 |
| Last Updated | 2026-10-09 |
| Created | 2026-09-22 |
| Platforms | browser, python |
| Est. Tokens | ~18k |
OpenJev The open-source, local alternative to Jev: System-One decision models for agents. Typed questions in, calibrated probabilities out, in one forward pass. No text generation, no API key. Python package () · models on the Hugging Face Hub Quickstart · Results · How it works · Train your own · Citation <img src="https://raw.githubusercontent.com/alanhuangyoo/OpenJev/main/as
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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.
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