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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network-proxy
by didiHelps when network-related commands (like curl, git, npm, pip, brew) are failing, timing out, or running slowly due to network issues. It suggests and applies proxy environment variables to fix connectivity problems.
mpx2rn-gene
by didiMpx 跨端输出 RN 开发适配的 Gene 表达形式——基于 Gene Evolution Protocol (GEP) 将文档导向的 Skill 蒸馏为紧凑的 Strategy Gene 集合。当用户要求对已有 Mpx 组件进行 RN 跨端适配改造、创建符合 RN 跨端兼容规范的 Mpx 组件时调用。与 mpx2rn skill 的区别：mpx2rn 提供完整文档参考，本 skill 提供紧凑的行为控制指令，适合执行阶段直接注入上下文。
mpx2rn
by didiMpx 跨端输出 RN（简称 Mpx2RN 或 Mpx2DRN）的开发适配指南，覆盖模板、脚本、样式、JSON 配置四大维度。当用户要求对已有 Mpx 组件进行 RN 跨端适配改造、创建符合 RN 跨端兼容规范的 Mpx 组件、排查 Mpx2RN 编译报错或查询某项能力（模板指令、基础组件、样式属性、生命周期、环境 API、JSON 字段等）在 RN 平台的支持情况时强制调用。当用户问题不涉及 Mpx 跨端输出 RN 时不应调用，如小程序原生开发问题，纯 RN 原生开发问题、Web 端样式问题等。
skill2gene
by didi将传统文档导向的 Procedural Skill 转换为紧凑的 Strategy Gene 格式。基于论文 "From Procedural Skills to Strategy Genes" 的 Gene Evolution Protocol (GEP)，将 ~2500 token 的文档型 Skill 蒸馏为 ~200-300 token 的控制型 Gene 集合。当用户要求将 skill 转为 gene、优化 skill 的 token 效率、将经验知识蒸馏为紧凑控制指令、或提到 gene/GEP/strategy-gene 时调用。
doc-add-simple-hash
by didimarkdown文档编辑时，为标题添加简单的哈希锚点，当用户提到添加简单哈希锚点时强制调用。
changelog-generator
by didi通过查看当前最新版本与上一版本间的git提交记录与代码变更，生成版本变更日志，当用户询问“创建/生成变更日志”、“创建/生成changelog”时使用。
didi-ride-skill
by didi中国城市出行服务。当用户表达任何交通出行需求时必须使用此技能——包括打车/叫车/网约车、查价格、路线规划（公交/驾车/步行/骑行）、周边搜索、查询订单/司机位置/取消订单。关键词："打车"、"叫车"、"去[地点]"、"回家"、"上班"、"下班"、"查价格"、"多少钱"、"路线"、"怎么走"、"步行到"、"附近"、"周边"、"司机"、"订单"、"查询订单"。注意：即使用户未明确说"打车"，只要涉及从A地到B地、通勤、或交通方式选择，都应触发。不触发场景：开发打车应用、使用其他导航app、订外卖、查公交时刻表、股票/财报查询。
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.