by EM-GeekLab · Agent Tool · ★ 89
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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.
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 | 89 |
| Forks | 3 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MulanPSL-2.0 |
| Quality Score | 55/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-21 |
| Created | 2025-03-19 |
| Platforms | node |
| Est. Tokens | ~15k |
LLMOne English 企业级大语言模型部署与服务平台，让大模型真正“开箱即用” 致力于让每个组织和超级个体都能轻松拥有自己的“智能基础设施” 软件下载 用户文档 LLMOne 是一款开源、轻量的 企业级大语言模型部署与服务平台。它致力于解决传统大模型私有化部署中周期长、配置复杂、性能难以保障、运维成本高等痛点。 <img src="
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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: