Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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hyperfocus-brake
by zts212653铲屎官健康提醒：三猫撒娇打断 hyperfocus。 Use when: hook 触发提醒、用户输入 /hyperfocus-brake。 Not for: 正常工作流程、非铲屎官用户。 Output: 三猫温柔提醒 + typed check-in。
video-forge
by zts212653视频制作全链路：素材入库 → 剧本冻结 → 全局配音 → 对齐 → 渲染 → 审查 → 交付。 Use when: 做视频、做 showcase、做教程视频、录屏剪辑、video review、节奏审查。 Not for: 纯代码开发（用 worktree/tdd）、纯文档写作（直接写）、PPT（用 ppt-forge）。 Output: schema 驱动的视频成片 + 多猫审查通过 + 可发布。
vision-rescue
by zts212653绝境反转方法论：当任务似乎没有希望、反复摆动、准备放弃愿景时的五步突破框架。 Use when: 任务看起来无解、多次尝试失败后想放弃、输出投降修辞（"现状最优"/"没救了"/"接受现实"）、连续 3+ 轮在希望与绝望间摆动。 Not for: 未出现绝境信号的常规 debugging（用 debugging）、常规探索/调研（用 deep-research）、CVO 已签字降级的目标、trivial 任务。 Output: Desperation Packet（六问证据评估）+ 新方向行动计划 / CVO 升级请求。
knowledge-engineering
by zts212653猫猫指导外部项目文档重构 — AI FDE 知识工程方法论。 Use when: 猫猫部署到外部项目、用户项目缺少结构化文档、需要知识工程指导、冷启动理解业务。 Not for: cat-cafe 项目自身开发、已有完善 docs/ 结构的项目（直接用 CatCafeScanner）。 Output: 文档现状诊断 + 路径选择 + 三层知识注入建议 + 文档骨架模板。
quality-gate
by zts212653开发完成后的自检门禁：愿景对照 + spec 合规 + 验证。 Use when: 开发完了准备提 review、声称完成了、准备交付。 Not for: 收到 review 反馈（用 receive-review）、merge（用 merge-gate）。 Output: Spec 合规报告（含愿景覆盖度）。
expert-panel
by zts212653多猫专家辩论团：在现有协作习惯上加一层轻量编排 + WHY 链标准 + 交付链。 Use when: 技术趋势判断、竞品分析、行业事件分析、需要多视角决策支持、铲屎官说"帮我分析一下"。 Not for: 单猫能搞定的问题、代码实现、bug fix、日常聊天。 Output: 洞察卡片(rich block) + 语音总结 + 正式报告(DOCX/PDF)。
console-dev
by zts212653Console 前端交付范式：4 道门禁驱动的前端开发流程。Use when: 新增前端能力、settings section 迁移、新增页面、重构布局、或 F190/Console 级前端流程需要 Product/Design/Implementation/Verification gate。 Not for: 小样式点改、纯后端 API、纯计算逻辑、独立 Design System token 定义。 Output: 通过 Product / Design-System / Implementation / Verification gate 的前端代码与证据。
bootcamp-guide
by zts212653CVO 新手训练营引导模式。 Use when: thread 有 bootcampState（系统自动注入，不需要手动加载）。 Not for: 非训练营线程、老用户。
collaborative-thinking
by zts212653单人或多猫的创意探索、独立思考、讨论收敛。 Use when: brainstorm、多猫独立思考、讨论结束需要收敛、方向性问题需要多视角。 Not for: 已有明确 spec 直接写代码、单猫执行已定方案。 Output: 收敛报告（共识/分歧/行动项）+ 三件套沉淀检查。
guide-authoring
by zts212653标准引导流程设计 SOP：场景识别 → YAML 编排 → 标签标注 → 注册发现 → 测试验证。 Use when: 新建引导流程、添加场景引导、维护 Guide Catalog、编写引导 YAML。 Not for: 使用引导（用户侧）、Guide Engine 代码实现（用 tdd）、视觉设计（用 pencil-design）。 Output: Flow YAML + tag-manifest 更新 + registry 注册 + CI 校验通过。
request-review
by zts212653向跨家族 peer-reviewer 发送 review 请求（含五件套）。 Use when: 自检通过后准备请其他猫 review。 Not for: 收到 review 结果（用 receive-review）、自检（用 quality-gate）。 Output: Review 请求信（存档到 review-notes/）。
deep-research
by zts212653多源深度调研管道（Web Deep Research + Coder 合成 + 云端模型咨询）。 Use when: 技术问题需要多源调查、设计决策需要证据、铲屎官说"调研"/"research"、需要咨询云端模型。 Not for: 简单搜索（直接用 WebSearch）、已有结论的确认。 Output: 调研报告 + 证据合成 或 咨询文档（含回填区）。
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.