by lookski · LLM Plugin · ★ 7
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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 | 7 |
| Forks | 3 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 68/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-02 |
| Created | 2026-09-22 |
| Platforms | browser, python |
| Est. Tokens | ~16k |
Open decision engine: type-safe answers with raw softmax probabilities from a local LLM or any OpenAI-compatible API. Crush radar for WeChat/QQ/Telegram chats - verdict + next-move advice. | 开放决策引擎: 本地 LLM 或任意 OpenAI 兼容 API, 原始 softmax 概率 + 类型安全回答. 微信/QQ/Telegram 追人雷达: 冲/稳/缓/停 + 下一步建议.
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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: