Awesome Skills

A curated library of role-based AI skills, organised by professional domain.

🌐 Website · 📚 Catalog · 📖 Install Guide · 🤝 Contributing

Language: English | 中文

English

What this is

A library of 956 skill files (SKILL.md + optional references/) covering 3 kinds (persona / tool / workflow) across 60+ domains. Each skill is a prompt pack — an instruction set plus reference material — designed to load into Claude Code, OpenCode, Cursor, or similar agent runtimes to give an LLM a consistent professional persona, tool expertise, or engineering workflow methodology.

What this is NOT

• Not an alternative to executable skill packs. The official Anthropic/OpenAI SKILL.md convention can ship with scripts/ and assets/ that the agent runs. The vast majority of skills here are persona + methodology, not runnable tools. Treat them as opinionated system prompts, not as plugins.
• Not a certification body. Historical quality scores in this repo were produced by a self-authored scoring script grading against a self-authored rubric. The scoring is useful internally; it is not an independent benchmark.
• Not feature-complete for every domain. Coverage is broad but uneven — some categories have deep role hierarchies, others have a single stub.

Layout

skills/                   956 skill files organised by kind
  persona/                827 role-based professional persona skills
    <domain>/<role>/
      SKILL.md            Frontmatter + system prompt (target ≤ 300 lines)
      references/         On-demand deep content (workflow / scenarios / …)
      EVALUATION_REPORT.md  Optional self-scored quality report

  tool/                   116 technology-specific expert skills (kind: tool)
    <technology>/
      <skill-name>/SKILL.md

  workflow/               8 process-driven action skills (kind: workflow)
    engineering/          TDD, debug-diagnose, zoom-out, architecture-review,
                          issue-triage, to-prd
    meta/                 write-skill, caveman

external/                 Registry of curated third-party skill repos
                          (anthropics/skills, mattpocock/skills, VoltAgent, …).
                          Pulled on demand via scripts/sync_external.py — not vendored.
benchmarks/               Evaluation dataset + scoring script for comparison
packages/                 Curated skill bundles by domain
roadmap/                  Career-path documents (independent of skills/)
taxonomy.yml              Single source of truth: 21 categories, 80 aliases
tools/                    Python package for skill analysis (scoring, tokens, anti-patterns)
scripts/                  Maintenance scripts (catalog regen, external sync, taxonomy check, …)
.github/                  CI workflows + scripts, CI/CD docs

Quick start

The simplest way to install a skill is to have your agent read its SKILL.md URL:

Read https://github.com/theneoai/awesome-skills/blob/main/skills/persona/executive/ceo/SKILL.md and install as a skill

Platform-specific instructions (OpenCode native command, Claude Code, Cursor, Cline, Codex, Kimi) are in INSTALL-GUIDE.md.

Skill packages

Pre-bundled collections in packages/:

Featured enterprise skills

Role skills modelled after the methodology of specific companies (15 shown out of 100+ under skills/enterprise/):

Full list in CATALOG.md.

External ecosystem hub

external/ is a registry of top third-party skill / subagent / plugin repositories — not a mirror. Pull any subset on demand:

python3 scripts/sync_external.py --list                 # see what's registered
python3 scripts/sync_external.py --all                  # shallow-clone everything
python3 scripts/sync_external.py --slug anthropics-skills

Registered sources (see external/README.md for the full table):

To propose a new source, edit external/sources.yml and open a PR. External clones are gitignored and never linted by our CI — we are a pointer, not an enforcer.

Tooling

The repo ships a Python package for static analysis of skills:

pip install -e ./tools/
python -m tools.skill_analyzer.cli score        # 8-dimension rubric scores
python -m tools.skill_analyzer.cli tokenizer    # Token budget + API cost
python -m tools.skill_analyzer.cli antipattern  # Common mistakes scanner

CI (.github/workflows/quality.yml) runs these on any PR that touches skills/, tools/, or .github/scripts/, and blocks merges whose changed SKILL.md files fall below score/token/description thresholds.

Known limitations

• Text-only skills. Virtually no skill ships executable scripts/ or assets/ — they are persona prompts.
• Description overlap. The role taxonomy spans 60+ domains and 956 skills and many descriptions overlap, which hurts automatic skill-discovery in agent runtimes. A description-similarity linter runs in CI (informational).
• Self-scored quality. EVALUATION_REPORT.md files reflect a self-graded rubric, not external review.
• Three category systems. packages/ (14), roadmap/ (22), and skills/ (60) use different taxonomies — being consolidated.

Documentation

License

MIT — see LICENSE.

中文

项目定位

按专业领域组织的 AI Skill 库，共 956 个技能，按 3 种类型（persona / tool / workflow）覆盖 60+ 领域。每个技能是一份 SKILL.md（+ 可选 references/），本质是角色化的提示词包——一套系统指令加参考资料，用于在 Claude Code / OpenCode / Cursor 等 agent 运行时里给 LLM 加载稳定的专业身份、工具专长或工程工作流方法论。

不是什么

• 不是可执行 Skill 套件。Anthropic/OpenAI 官方 SKILL.md 可以随包携带 scripts/ 和 assets/ 交给 agent 调用；本仓库绝大多数 skill 仅提供角色 + 方法论的文本，应视作有观点的系统提示词，而非可运行插件。
• 不是第三方认证。仓库内历史质量分由本仓库自己编写的脚本按自家标准打出，对内有参考价值，但不是独立基准。
• 不是每个领域都覆盖完整。分类很广但深浅不一：有些领域有完整角色树，有些只有一个占位文件。

目录结构

skills/                   956 个技能文件，按 kind 分层组织
  persona/                827 个角色 persona 技能
    <domain>/<role>/
      SKILL.md            Frontmatter + 系统提示词（目标 ≤ 300 行）
      references/         按需加载的深度内容（workflow / scenarios / …）
      EVALUATION_REPORT.md  可选：自评质量报告

  tool/                   116 个技术工具专家技能（kind: tool）
    <technology>/
      <skill-name>/SKILL.md

  workflow/               8 个流程驱动工作流技能（kind: workflow）
    engineering/          TDD、debug-diagnose、zoom-out、architecture-review、
                          issue-triage、to-prd
    meta/                 write-skill、caveman

external/                 精选第三方 skill 仓库的注册表
                          （anthropics/skills、mattpocock/skills、VoltAgent 等）
                          通过 scripts/sync_external.py 按需拉取，不随仓库 vendored
benchmarks/               评估数据集 + 评分脚本
packages/                 按领域打包的 skill 合集
roadmap/                  职业路径文档（独立于 skills/）
taxonomy.yml              21 个分类、80 个别名的唯一真源
tools/                    Skill 分析工具 Python 包（评分、token、反模式）
scripts/                  维护脚本（catalog 重生成、external 同步、taxonomy 检查等）
.github/                  CI 工作流与脚本、CI/CD 文档

快速开始

最通用的安装方式是让 agent 读 SKILL.md 的 URL：

Read https://github.com/theneoai/awesome-skills/blob/main/skills/persona/executive/ceo/SKILL.md 并安装为 skill

各平台（OpenCode 原生命令、Claude Code、Cursor、Cline、Codex、Kimi）详细步骤见 INSTALL-GUIDE.md。

Skill 合集

按领域打包，见 packages/：

精选企业技能

仿照特定公司方法论建模的角色（从 skills/enterprise/ 100+ 条中选 7 条）：

完整列表见 CATALOG.md。

外部生态集散地

external/ 是业界优秀 skill / subagent / plugin 仓库的注册表（非镜像），按需拉取任意子集：

python3 scripts/sync_external.py --list                 # 查看注册的仓库
python3 scripts/sync_external.py --all                  # 全量浅拉取
python3 scripts/sync_external.py --slug anthropics-skills

已注册仓库（完整表格见 external/README.md）：

提议新增来源：编辑 external/sources.yml 并提交 PR。 external/ 下的本地克隆已加入 .gitignore，也不被 CI 扫描——我们是指针，不是上游的审阅者。

工具

仓库附带 Python 分析包：

pip install -e ./tools/
python -m tools.skill_analyzer.cli score        # 8 维度评分
python -m tools.skill_analyzer.cli tokenizer    # Token 预算与 API 成本
python -m tools.skill_analyzer.cli antipattern  # 反模式扫描

CI（.github/workflows/quality.yml）会在修改了 skills/、tools/ 或 .github/scripts/ 的 PR 上运行这些工具，若新改动的 SKILL.md 达不到阈值则阻塞合并。

已知局限

• 绝大多数 skill 是纯文本 persona，并不随包携带可执行 scripts/ 或 assets/。
• 描述重叠严重：60+ 分类 × 956 个技能，许多 description 字段相似度高，会降低 agent 运行时的 skill 自动发现准确度。描述相似度检查已在 CI 中运行（仅提示，不阻塞）。
• 质量分是自评：EVALUATION_REPORT.md 反映仓库自家规则的评分，不是独立评审。
• 三套分类互不一致：packages/（14 类）、roadmap/（22 类）、skills/（60 类）仍在统一中。

文档

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• Awesome MCPs - 115+ MCP 服务器，一键安装
• GitHub - Awesome MCPs 项目源码

许可证

MIT，见 LICENSE。

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