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...
shipping-changes
by wnzzerUse when src files in the rank-analysis repo (lol-record-analysis-tauri/**/*.ts, .vue, .rs) have just been edited via Edit/Write/MultiEdit, OR before any `git commit`, `git push`, or `gh pr create` in this repo. Symptoms include "I'm done with that change", "实现完了", a finished logical chunk of work, or about to invoke any commit/push/PR command.
sentry
by wnzzer查 rank-analysis 项目的 Sentry 数据（日活/DAU、日志量、错误趋势、release 分布、issue 列表）。直接走 Sentry REST API，不走 mcp.sentry.dev（那玩意的 search_events agent 烂了）。当用户问"DAU""日活""日志量""log 用量""错误数""error 趋势""release 分布""线上 issue""Sentry 上 xx"时触发。
eastmoney-financial-data
by wnzzer东方财富金融数据查询工具。支持行情数据（股价、资金流向、估值）、财务数据（财报、股东结构、高管信息）、关系与经营数据。通过自然语言查询，如"东方财富最新价"、"贵州茅台市盈率"。当用户需要查询股票、基金、指数、板块等金融数据时使用此skill。需要先配置apikey才能使用。
eastmoney-financial-search
by wnzzer东方财富资讯搜索工具。基于东方财富妙想搜索能力，用于获取金融相关的新闻、公告、研报、政策、交易规则、事件分析、影响解读等时效性信息。支持个股资讯、板块新闻、宏观分析等。当用户需要搜索金融资讯、了解市场动态、查看研报解读时使用此skill。需要先配置apikey才能使用。
eastmoney-select-stock
by wnzzer东方财富智能选股工具。支持基于自然语言查询筛选股票，包括行情指标、财务指标等条件；可查询指定行业/板块内的股票；支持A股、港股、美股。当用户需要选股、筛选股票、按条件查找股票时使用此skill。需要先配置apikey才能使用。
file-compression
by wnzzerCompress files to reduce storage and transfer size. Use this skill when users ask to shrink PDFs or images, optimize upload/share size, or balance quality and size. Supports PDF compression and image compression with Python-first workflows plus Node.js fallback when Python dependencies are unavailable.
github-doc-maintainer
by wnzzer全自动检索 GitHub 热门仓库，分析并维护项目文档，自动提交 PR。用于发现文档问题（死链、typo、过时内容等）并自动修复提交。当用户需要批量维护开源项目文档、自动提 PR 修复文档问题时触发此技能。
mcp-integration
by wnzzerUse Model Context Protocol servers to access external tools and data sources. Enable AI agents to discover and execute tools from configured MCP servers (legal databases, APIs, database connectors, weather services, etc.).
session-archive-backup
by wnzzerOpenClaw 会话重置-存档-备份完整工作流管理。自动化处理 Token 超限、闲置超时、手动重置时的四层数据存档体系，支持 GitHub/OneDrive 双备份。 TRIGGERS: - 用户说"设置存档工作流"、"配置自动备份" - 需要建立会话生命周期管理机制 - 想要自动化重置-存档-备份流程
slow-query-analyzer
by wnzzer数据库慢查询日志分析和 SQL 性能优化工具。支持 MySQL、PostgreSQL 慢查询日志解析，SQL 执行计划分析，索引优化建议，生成优化报告。用于诊断数据库性能问题、优化慢 SQL、提升查询效率。
wechat-bridge
by wnzzer微信接入 OpenClaw 的桥接工具。基于 qclaw-wechat-client 实现微信扫码登录、消息收发，将微信用户消息转发到 OpenClaw 处理。当用户需要接入微信、配置微信桥接、启动/停止微信服务时使用此技能。
wechat-mp-cn
by wnzzer微信公众号监控 - 文章监控、阅读量追踪、舆情分析（WeChat Official Account）
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.