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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 | 69 |
| Forks | 12 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 52/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-20 |
| Platforms | python |
| Est. Tokens | ~15k |
Jev-as-a-Judge with Comet Opik A small, auditable example of evaluating refund-support traces with Jev and recording the results as an Opik experiment. What this project does Replays ten synthetic, frozen support traces, including correct answers, invented policies, false action claims, ambiguous wording, and an evaluator-injection attempt.
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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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