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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compose
by simonwong基于主题或参考文章进行中文创作。自动检索素材库、加载主力风格档案，产出符合个人风格的文章。支持公众号、Twitter、小红书、博客等多种场景。当用户说"写一篇关于XX的文章""围绕XX创作""基于这篇文章写一篇""参考这篇英文写个中文版""帮我写""出一篇稿子"时使用此技能。即使用户只给了一个主题或一篇参考文章，也应考虑使用。
material-ingest
by simonwong拆解文章，提取可复用的素材（观点、数据、案例、金句、类比、方法论），分类标注后存入素材库。当用户说"拆解素材""入库""收集这篇文章""提取素材""分析这篇文章的要点"或投喂文章希望留存有价值内容时使用此技能。即使用户只是分享文章并表达"这篇不错"，也应考虑是否需要入库。
material-retrieve
by simonwong从素材库中按主题、标签、类型检索可复用的写作素材。当用户说"找素材""查素材""关于XX的素材""检索素材库""有没有关于XX的东西""翻翻素材库"时使用此技能。当用户在写作过程中需要支撑材料时也应主动建议使用。
rewrite-en2zh
by simonwong将英文内容重写为简体中文。用于英文文章、文档、博客的中文重写。使用 deverbalization 技巧，理解原意后脱离英文外壳，用中文自然表达，而非逐字对照。保留 Markdown 格式、AI 专有名词。
rewrite
by simonwong润色和改写文章，去除 AI 感，按主力风格档案调整文风。当用户说"润色""改稿""去 AI 感""打磨一下""修改文风""帮我改改""这篇读起来太像 AI 了"时使用此技能。即使用户只是说"这篇文章哪里不好"或抱怨文章读起来不自然，也应考虑使用。
style-extract
by simonwong分析文章的写作风格特征，提取风格维度存入风格素材库。可融合多篇风格素材生成或更新主力风格档案（my_style.json）。当用户说"分析风格""提取写作风格""学习这个语气""分析我的文风""吸收这个风格""更新我的风格"时使用此技能。即使用户只是分享一篇文章并表达对其风格的兴趣，也应考虑使用。
title-gen
by simonwong为文章生成多个候选标题，覆盖不同策略类型，标注适用平台。当用户说"帮我起个标题""生成标题""取标题""想个标题""这个标题不好，换几个"时使用此技能。当用户刚完成创作并需要标题时也应主动建议使用。
strudel-repl
by simonwongWrite, debug, and explain Strudel REPL code for browser-based live coding music. Strudel is a JavaScript port of TidalCycles — a pattern language for algorithmic music. Use this skill whenever the user mentions Strudel, TidalCycles, live coding music, mini-notation, algorithmic composition, or wants to create music with code in the browser. Also trigger on: drum patterns, euclidean rhythms, pattern transformations, sample manipulation, synth patches in Strudel, or any question about strudel.cc syntax and functions.
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.