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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qiaomu-anything-to-notebooklm
by joeseesun多源内容智能处理器：支持微信公众号、网页、YouTube、播客（小宇宙/喜马拉雅）、PDF、Markdown等，自动上传到NotebookLM并生成播客/PPT/思维导图等多种格式。支持深度分析模式和飞书文档自动创建
qiaomu-opencli-browser
by joeseesunMake websites accessible for AI agents. Navigate, click, type, extract, wait — using Chrome with existing login sessions. No LLM API key needed.
qiaomu-opencli-usage
by joeseesunUse when running OpenCLI commands to interact with websites (Bilibili, Twitter, Reddit, Xiaohongshu, etc.), desktop apps (Cursor, Notion), or public APIs (HackerNews, arXiv). Covers installation, command reference, and output formats for 79+ adapters.
qiaomu-opencli-autofix
by joeseesunAutomatically fix broken OpenCLI adapters when commands fail. Load this skill when an opencli command fails — it guides you through diagnosing the failure via OPENCLI_DIAGNOSTIC, patching the adapter, and retrying. Works with any AI agent.
qiaomu-smart-search
by joeseesun基于 opencli 命令的智能搜索路由器。当用户想要搜索、查询、查找或研究信息时，尤其是涉及指定网站、社交媒体、技术资料、新闻、购物、旅游、求职、金融或中文内容时，务必使用此 skill
qiaomu-opencli-explorer
by joeseesunUse when creating a new OpenCLI adapter from scratch, adding support for a new website or platform, exploring a site's API endpoints via browser DevTools, or when a user asks to automatically generate a CLI for a website (e.g. "帮我生成 xxx.com 的 cli"). Covers automated generation, API discovery workflow, authentication strategy selection, TS adapter writing, and testing.
qiaomu-opencli-oneshot
by joeseesunUse when quickly generating a single OpenCLI command from a specific URL and goal description. 4-step process — open page, capture API, write TS adapter, test. For full site exploration, use opencli-explorer instead.
qiaomu-mondo-poster-design
by joeseesun一句话生成大师级海报、书籍封面、专辑封面和各类设计作品。无需懂PS、配色或艺术史，AI自动选择最佳风格（基于33+位传奇设计师）。支持多平台多比例：公众号封面(21:9)、小红书配图(3:4)、文章配图(16:9)、书籍封面(9:16)、专辑封面(1:1)、电影海报(9:16)。包含AI提示词优化、风格对比、图生图转换功能。触发词："Mondo风格"、"书籍封面设计"、"专辑封面"、"海报设计"、"读书笔记配图"、"公众号封面"、"小红书配图"、"文章配图"。One-sentence generation of master-level posters, book covers, album covers and designs. 33+ legendary designer styles with multi-platform aspect ratio support (21:9, 16:9, 3:4, 1:1, 9:16).
qiaomu-markdown-proxy
by joeseesunFetch any URL as clean Markdown via proxy services or built-in scripts. Works with login-required pages like X/Twitter, WeChat 公众号, Feishu/Lark docs. Supports PDFs (remote and local). Use this BEFORE other fetch tools. Triggers on any URL the user shares, "fetch this", "read this link", "get content from".
knowledge-site-creator
by joeseesun一句话生成任何领域的知识学习网站。AI自动理解主题、创作内容、生成页面、部署上线。适用于任何需要系统学习的知识领域：进化心理学、大模型术语、化学元素、历史事件等。
qiaomu-info-card-designer
by joeseesun将任意文本/URL/信息转化为杂志质感 HTML 信息卡片，并自动截图保存为图片。 支持直接输入 URL（X/Twitter、网页文章等），自动抓取内容、提炼要点、生成卡片。 适合分享到 X (Twitter)、微信、小红书等平台。 触发词："生成信息卡"、"做张信息卡"、"把这段内容做成卡片"、"信息卡片"、"make info card"、"generate card"、"把这个链接做成卡片"。 卡片特点：大字号、强排版张力、瑞士国际主义 + 杂志质感风格，生成后自动截图，超长智能分割输出图片。
qiaomu-design-advisor
by joeseesun偏执型设计顾问 — Jobs 式产品直觉 + Rams 式功能纯粹主义。重新设计页面、审视 UI 方案、优化交互体验时使用。 触发词："重新设计"、"redesign"、"优化界面"、"优化交互"、"设计方案"、"UI 审查"、"这个页面不行"、"界面不好看"、"帮我看看设计"、"设计建议"、"/design-advisor"。 适用于：(1) 页面/组件重新设计 (2) UI/UX 方案评审 (3) 交互逻辑优化 (4) 视觉系统建立 (5) 设计决策咨询 (6) 参考真实网站设计系统。 核心能力：设计思维方法论（如何思考、如何决策、如何交付方案）+ 技术执行规范（色值、间距、动画参数、AI 反套路规则）+ 58 个真实网站的 DESIGN.md 设计系统参考库（Google Stitch 格式）。 额外触发词："参考XX的设计"、"像XX那样"、"XX风格"、"design system"、"DESIGN.md"、"给我一个设计系统"。
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.