by gatelynch · AI Tool · ★ 336
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 | 336 |
| Forks | 90 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 56/100 |
| Last Updated | 2026-04-06 |
| Created | 2026-04-05 |
| Platforms | python |
| Est. Tokens | ~6k |
LLM 知識庫 以 LLM 編譯為核心的個人知識管理系統。靈感來自 Andrej Karpathy 的 LLM 知識庫工作流程及Andrej Karpathy 的 想法文件，把原始素材與編譯知識分開。 跟 Andrej Kapathy llm-wiki 概念文件的差別 Andrej Kapathy的llm-wiki是三層式的架構(raw-wiki-schema)他的架構是由低到高，由上層架構管理下層資料。我的四層架構是(raw-wiki-brainstorming-artifacts)，偏向工作流式的架構，資訊在不同階段間流動。我同時是知識的生產者跟消費者，所以需要雙向編譯跟與AI討論的階段，但最終都會透過LLM編譯彙整到wiki。 問題所在 大多數個人知識系統最終都成了墓地。你收藏文章、做筆記、畫重點——然後再也不去看。收集 、理解與輸出之間的落差，就是埋葬知識的地方。 這個系統透過讓...
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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: