by zhouying20 · AI Tool · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 60 |
| Forks | 5 |
| Language | Python |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 51/100 |
| Open Issues | 1 |
| Last Updated | 2024-04-10 |
| Created | 2024-03-24 |
| Platforms | python |
| Est. Tokens | ~3k |
Humanizing Machine-Generated Content This repository contains resources of our paper: Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack How to reporduce our result Download and unzip dataset from Google Drive Run Do attacks on your own data Distill sample labels from your target victim detector, train a surrogate model with Follow to start multi-process...
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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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