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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 | 80 |
| Forks | 26 |
| Category | AI Tool |
| Quality Score | 52/100 |
| Last Updated | 2026-04-06 |
| Created | 2026-04-06 |
| Est. Tokens | ~6k |
LLM Wiki 项目使用手册 本项目是一个基于 LLM 的智能知识库管理系统，结合 Qoder Agent、Obsidian 和 QMD 语义检索工具，实现知识的自动沉淀与高效检索。 项目起源 本项目基于 Karpathy 的 LLM Wiki 理念 构建，核心理念是： LLM Wiki 是一种增量式知识库构建模式，与传统 RAG 不同，它会持续构建和维护一个结构化的、相互关联的 markdown 文件集合，知识是累积的而非每次都重新检索。 项目架构 核心组件 Qoder Agent + LLM Wiki Skill 用于自动分析新文章并沉淀到结构化 wiki 中。 安装 Skill 方式 1 - 使用 create-skill 命令（推荐）： 方式 2 - 手动创建： bash 创建 skill 目录 mkdir -p .q
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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: