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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 | 642 |
| Forks | 110 |
| Category | AI Tool |
| Quality Score | 48/100 |
| Last Updated | 2026-04-13 |
| Created | 2026-04-10 |
| Est. Tokens | ~16k |
LLM Wiki - 基于 Obsidian 的知识管理系统 一个基于 Andrej Karpathy 的 LLM Wiki 模式 实现的 Obsidian 知识库，利用 LLM 维护可复利的个人知识层。 ✨ 核心理念 传统 Wiki 失败的原因是维护成本太高——人类不擅长处理繁琐的交叉引用、一致性检查和内容更新。而 LLM 恰好擅长这些"记账"工作。 本项目将知识管理分为三层： 原始资料层（）：不可变的事实来源 知识层（）：LLM 维护的结构化知识网络 配置层（）：定义系统规则和工作流 🎯 核心特性 持久化知识积累：Wiki 是可复利的资产，而非每次重新推导 自动化维护：LLM 处理交叉引用、一致性检查、内容更新 结构化组织：按来源、实体、概念、比较、总览分类管理 可追溯性：完整的操作日志和来源引用 Obsidian 原生：充分利用双向链接、标签、图谱等功能 📁 目录结构 🎨...
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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: