by yeasy · Codex Skill · ★ 185
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 | 185 |
| Forks | 35 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 62/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-04 |
| Created | 2026-02-20 |
| Platforms | python |
| Est. Tokens | ~14k |
English overview 《OpenClaw 入门到精通》 OpenClaw 是一款开源本地优先的个人 AI 助手系统，由 Peter Steinberger 创建。本书结合最佳实践，提供从入门到应用的全流程指南，并深度解构其底层的运行机制和实现原理。 本书特色 实战导向：从零到一搭建最小闭环，提供可直接复用的配置模板 机制剖析：深入解析 Gateway、Agent Loop、工具系统、会话与记忆等核心机制 生产就绪：聚焦可靠性、安全加固、运行监控与故障排查 目标读者与前置要求 目标读者：对 AI 智能体感兴趣的个人用户、AI 应用开发者、大模型落地工程师、系统架构师等。 前置基础：阅读本书需要了解基本的后端开发常识（如 Node.js 或 Python 基础），并对大语言模型 (LLM) 和 AI 智能体有初步概念。可参考 《零基础学 AI》 和 《智能体 AI 权威指南》...
These tools work well together with openclaw_guide for enhanced workflows:
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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: