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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wecom-notify
by Xueheng-LiSend messages to WeCom (企业微信) via the WeCom API. Use when the user asks to "send wecom message", "notify via wecom", "发企业微信", "给我发企业微信消息", "wecom通知", "发文件到企业微信", "发图片到企业微信", or when a task completes and the user wants notification on WeCom.
fetch4ai
by Xueheng-LiMUST USE THIS SKILL when the user asks or an agent needs to "fetch web content", "crawl a page", "use crawl4ai", "extract content from URL", "fetch with filtering", "get clean markdown from webpage", "research with content filtering", or needs to fetch web pages with customizable noise removal for LLM processing.
chat-history-summarizer
by Xueheng-LiExtract and summarize Claude Code chat history into structured documentation. Use when the user asks to export, summarize, or document a conversation session, extract prompts and actions from chat logs, or create a record of what was accomplished in a session.
chinese-quote-converter
by Xueheng-LiConvert English straight quotation marks ("...") to Chinese curved quotation marks ("..." U+201C/D). Use when processing Chinese text documents, markdown files, or any content that needs proper Chinese typography with directional quotes. Triggers on keywords like "转换引号", "中文引号", "英文引号转中文", "quote conversion", "convert quotes".
marp-slides-creator
by Xueheng-Li专业Marp演示文稿制作助手。支持完整工作流程：工作空间初始化、内容分析、slides制作、多维度审阅、中文语言规范审阅（中文演示文稿）、PNG转换检查、终稿确定。所有产出物集中管理在项目工作文件夹中。当用户提到"制作slides"、"做PPT"、"演示文稿"、"Marp"、"幻灯片"、"presentation"等关键词时自动启用。Professional Marp presentation assistant with complete workflow: workspace initialization, content analysis, slide creation, multi-dimensional review, Chinese language review (for Chinese presentations), PNG conversion check, and finalization. All outputs organized in project workspace.
github-trending
by Xueheng-Li获取 GitHub 热门项目信息。当用户说"获取 github trending"、"今日/本周/本月热门项目"、"github 上有什么热门"时使用。
frontend-design
by Xueheng-Li创建独特、生产级的高质量前端界面。当用户要求构建 Web 组件、页面、作品、海报或应用程序时使用此技能（例如网站、着陆页、仪表板、React 组件、HTML/CSS 布局，或对任何 Web UI 进行样式/美化）。生成富有创意、精致的代码和 UI 设计，避免通用的 AI 美学风格。
mineru-pdf-converter
by Xueheng-LiThis skill should be used when the user asks to "convert PDF to markdown", "use MinerU to convert [file]", "extract text from PDF", "PDF转Markdown", "转换PDF [路径]", "MinerU转换 [file]", "/mineru [file path]", or needs high-quality document conversion with formula and table recognition.
arxiv
by Xueheng-Li搜索 arxiv 论文并总结。当用户说"找寻XX的论文"、"搜索XX的论文"、"找arxiv上XX主题的论文"时使用。
cc-insights
by Xueheng-LiThis skill should be used when the user asks to "归档聊天记录", "archive my chats", "分析我与CC的交互", "analyze my Claude Code usage", "反思我的CC使用习惯", "生成CC洞察报告", "深度分析CC使用模式", "更新聊天归档", or mentions keywords like "交互日志", "使用模式分析", "CC insights", "deep analysis". Provides automated archiving and deep analysis of Claude Code interaction history.
md-to-docx
by Xueheng-LiConvert Markdown files to Word documents (.docx) with proper formatting, Chinese font support (FangSong for all text including headings), black font color, 1.5x line spacing, precise first-line indent (24pt), heading spacing after (1 line), no italic headings, and automatic superscript conversion for citation numbers. Use when converting .md files to .docx, creating Word documents from markdown, or when user mentions Word, DOCX, or document conversion. Requires pandoc.
web-research
by Xueheng-LiUse this skill for requests related to web research; it provides a structured approach to conducting comprehensive web research
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.