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...
excalidraw-diagram
by xstongxue基于文字说明或结构信息，生成可直接在 Excalidraw 中打开的手绘风 `.excalidraw` 图表；支持系统架构图、流程图、数据结构图与自由白板草图，输出标准 Excalidraw JSON。
frontend-design
by xstongxue创建具有高设计品质、可交付生产的前端界面。当用户要求构建 Web 组件、页面、海报或应用（如官网、落地页、仪表盘、React/Vue 组件、HTML/CSS 布局，或对任意 Web UI 进行样式/美化）时使用本技能。产出有创意、打磨到位且避免「AI 通用审美」的代码与界面设计。
wechat-article-writer
by xstongxue公众号/自媒体全流程。根据用户表述自动匹配：撰写文章、封面图、正文插图、风格提取。支持多种写作风格。当用户提到写公众号、技术博客、公众号封面、正文插图、步骤图、演示图、流程示意、分析写作风格、克隆文风、模仿爆款、提取风格时使用。详见 reference 目录。
codegen-diagram
by xstongxue基于当前项目/代码生成 Draw.io 图表，支持技术栈图、系统架构图、数据结构图、E-R 图四种类型。输出符合 Draw.io 语法的 .drawio 文件（mxGraph XML），可直接导入 Draw.io 编辑。当用户提到技术栈、系统架构、数据结构、E-R 图时使用。
codegen-doc
by xstongxue基于当前项目/代码生成各类文档，支持论文章节、项目梳理、重点问题、简历项目描述四种类型。当用户提到生成论文章节、项目梳理、技术难点、简历项目描述时使用。
dev-workflow
by xstongxue开发流程五步法。支持需求理解、方案设计、代码实现、代码审查、Bug 修复。当用户提到「需求分析」「方案设计」「代码实现」「代码审查」「理解需求」「技术设计」「开始写代码」「Review」「检查代码」「bug」「报错」「崩溃」「异常」「出错了」时使用。
drawio-diagram
by xstongxue为深度学习模型、网络架构、算法流程等生成标准 Draw.io (.drawio) 格式的可视化图表；支持从零生成与风格迁移两种模式。从零生成：模型架构图、流程图、感受野示意图等；风格迁移：参考图 + 内容描述/项目 → 按参考图风格生成新图。确保 XML 格式正确，可直接在 Draw.io 中打开编辑。
md-report-summary
by xstongxue生成高质量 Markdown 周报、工作汇报、总结、介绍等文档。无草稿时从 Web 搜索并总结；有草稿时结合草稿整理、润色、补充。当用户提到周报、工作汇报、总结、介绍、述职、复盘时使用。
paper-write
by xstongxue本科与硕士学位论文全流程撰写辅助。支持大纲审核（理工科/文科）、结构仿写（通用/实验/绪论/摘要；文科含文献综述、案例分析、对策建议、文科绪论与摘要）、参考文献获取、融合、润色（含实验章节/文科章节）、缩写、扩写、防 AIGC、中英互译、结构化信息提取。当用户提到论文撰写、大纲审核、论文章节仿写、参考文献、论文润色、防 AIGC、论文翻译、文科论文、文献综述、对策建议时使用。
patent-write
by xstongxue基于用户提供的技术方案、草稿或参考专利，生成、改写与统稿中文发明专利的题目、摘要、背景技术、发明内容、权利要求、附图说明和具体实施方式。用于专利撰写、专利摘要、权利要求、说明书、专利润色、专利仿写、参考专利蒸馏等场景。
pptgen-drawio
by xstongxue根据论文或汇报内容生成多页 Draw.io 格式 PPT，支持论文答辩与通用汇报两种模式，自动导出为 .pptx。当用户提到论文答辩 PPT、答辩幻灯片、通用 PPT、汇报 PPT、根据模板生成 PPT、drawio2pptx 时使用。
create-skill
by xstongxueGuides users through creating effective Agent Skills for Cursor. Use when the user wants to create, write, or author a new skill, or asks about skill structure, best practices, or SKILL.md format.
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.