awesome-claude-notes: Community-maintained distribution of reusable AI coding agents, commands, skills, hooks, and cross-harness

by loulanyue · Codex Skill · ★ 272

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install awesome-claude-notes

/plugin marketplace add loulanyue/awesome-claude-notes
/plugin install awesome-claude-notes@awesome-claude-notes

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 62/100

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.

agent-skillsai-agentsai-codingautomationclaude-codecodexcursordeveloper-toolsopencodeprompt-engineering

Quick Facts

Stars272
Forks13
LanguageJavaScript
CategoryCodex Skill
LicenseMIT
Quality Score62/100
Open Issues14
Last Updated2026-09-29
Created2026-03-17
Platformsclaude-code, codex, node
Est. Tokens~14k

About awesome-claude-notes

awesome claude notes 语言 简体中文（当前） 简体中文镜像 Português (Brasil) 繁體中文 日本語 한국어 Türkçe 一套面向 Claude Code 与多种 AI coding harness 的生产级工作流资产库。 把 AI coding agent 从“偶尔好用”，推进到“稳定可复用”。 awesome claude notes 提供已经打磨过的 agents、skills、commands、rules、hooks、 MCP configs 和跨平台脚本，帮助你更快搭起一套可落地的 agent harness，而不是从零 拼装目录、提示词和自动化流程。

Compatible Skills

These tools work well together with awesome-claude-notes for enhanced workflows:

  • .claude — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • opencode-primer — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • ai-max — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • VibeRaven — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (59%)

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How this security grade is produced

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:

View on GitHub → Browse Codex Skill tools