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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 | 90 |
| Forks | 7 |
| Language | Cuda |
| Category | AI Tool |
| License | MIT |
| Quality Score | 55/100 |
| Last Updated | 2026-09-28 |
| Created | 2026-09-27 |
| Est. Tokens | ~20k |
OpenJev-Fast: 6× Faster Open-Jev-27B Inference with Specialized CUDA Kernels Report: web page · PDF A faster inference backend for Open-Jev. It runs locally as a drop-in replacement for Open-Jev's own local server (, same request format), with the same model (Open-Jev-27B-v1.1 on Qwen3.8-27B), in bf16 with no quantization.
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SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integration
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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: