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.
Querying local SQLite index...
tavily
by tommy-wangbot【首选搜索工具】Tavily AI 搜索，专为 AI Agent 优化，返回高质量摘要和来源，搜索结果比 web-search/multi-search-engine 更准确、更快。触发词：搜索、查一下、帮我找、search、找资料、查数据、查文献、最新消息、新闻、调研、research、查询、检索。注意：需要 API Key（tvly-xxxx）。未配置时降级使用 multi-search-engine。
skywork-document
by tommy-wangbotUse when the task is to create a polished document in `docx`, `pdf`, `md`, or `html`, especially from prompts, reference files, or source materials. This skill authenticates to the Skywork Office Doc API, can parse input files first, and then generates a formatted document from a structured content prompt.
uml
by tommy-wangbot【首选画图工具】用 PlantUML 生成各类技术图表，自动布局、效果好、输出 Markdown 文件可直接用 Markdown Viewer 渲染。覆盖绝大多数图表需求。触发词：架构图、流程图、时序图、序列图、类图、组件图、状态图、活动图、用例图、部署图、系统设计图、技术图、画图、diagram、UML、PlantUML。不处理：网络拓扑→network；云架构→cloud；企业架构→archimate；业务流程→bpmn；象限图/泳道图/咨询图→diagram-design；需要PNG文件→fireworks-tech-graph。
tasks
by tommy-wangbotGlobal multi-step task tracking. Create, update, and monitor long-running tasks across threads. Tasks persist across restarts and are visible in all conversations.
ljg-writes
by tommy-wangbot写作引擎，带着观点出发在写作过程中把它想透，适合文章、报告、分析稿、观点文。触发词：帮我写、写一篇、写文章、写报告、起稿、写一个分析、帮我起草、写稿、我想写一篇。
drawio-skill
by tommy-wangbot生成高质量 draw.io 图表并导出 PNG/SVG/PDF，含自检循环（最多5轮）和321个 AI/LLM 品牌 Logo。触发词：drawio图、导出PNG、导出PDF、AI架构图（带品牌Logo）、ML模型图、神经网络图、Transformer图、CNN图、代码结构图、代码转图、自定义样式图、精品图。注意：普通架构图/流程图/时序图请优先用 uml skill；网络拓扑→network；云架构→cloud。需要 draw.io 桌面版（brew install --cask drawio）。
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.