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...
xhs-note-creator
by zrt-ai-lab小红书笔记素材创作技能。当用户需要创建小红书笔记素材时使用这个技能。技能包含：根据用户的需求和提供的资料，撰写小红书笔记内容（标题+正文），生成图片卡片（封面+正文卡片），以及发布小红书笔记。
uni-agent
by zrt-ai-lab统一智能体协议适配层。一套 API 调用所有 Agent 协议（ANP/MCP/A2A/AITP 等）。当用户需要调用 Agent、跨协议通信、连接工具时触发此技能。
auto-weixin-video
by zrt-ai-lab微信视频号自动发布技能。当用户需要发布视频到微信视频号时使用这个技能。技能包含：获取登录Cookie、上传视频、设置标题话题、定时发布、原创声明等功能。
auto-douyin
by zrt-ai-lab抖音视频自动发布技能。当用户需要发布视频到抖音时使用这个技能。技能包含：获取登录Cookie、上传视频、设置标题话题、定时发布等功能。
videocut-subtitle
by zrt-ai-lab字幕生成与烧录。转录→词典纠错→审核→烧录。触发词：加字幕、生成字幕、字幕
videocut-install
by zrt-ai-lab环境准备。安装依赖、下载模型、验证环境。触发词：安装、环境准备、初始化
videocut-self-update
by zrt-ai-lab自更新 skills。记录用户反馈，更新方法论和规则。触发词：更新规则、记录反馈、改进skill
build-project-docs
by zrt-ai-lab为项目构建分层式LLM友好文档体系。已有项目：扫描项目结构→架构分类→生成CLAUDE.md主索引→基础模块文档→业务模块API/数据模型/坑点文档→配置文档→git变更日志(含风险评估+回滚指南)→交叉验证。新项目：解析PRD→按脚手架规范设计架构→需求拆解为开发任务→融合代码规范生成CLAUDE.md开发指南→模块级API设计+数据模型+开发清单。当用户需要为项目创建AI编程文档、建立项目索引、梳理代码、新建项目规划、或提到llm.txt/CLAUDE.md时使用。
csv-data-summarizer
by zrt-ai-labCSV数据分析技能。使用Python和pandas分析CSV文件，生成统计摘要和快速可视化图表。当用户上传或提到CSV文件、需要分析表格数据时自动使用。
deep-research
by zrt-ai-lab当用户要求"调研"、"深度调研"、"帮我研究"、"调研下这个"，或提到需要搜索、整理、汇总指定主题的技术内容时，应使用此技能。
feishu-chat-history
by zrt-ai-labFetch and summarize Feishu group chat history. Use when the user asks to read, review, or summarize messages from a Feishu group chat. Triggers: "看群聊记录", "群里聊了啥", "帮我看看这个群", "群消息历史", "chat history", "what did the group discuss". NOT for: sending messages (use message tool), reading documents (use feishu-doc skill), or wiki operations (use feishu-wiki skill).
feishu-cron-reminder
by zrt-ai-labCreate cron jobs that reliably deliver reminders to Feishu (飞书) chats. Use when the user asks to set up scheduled reminders, periodic notifications, or any recurring task that should send messages to a Feishu conversation. Triggers: '飞书定时提醒', '定时任务发飞书', 'cron reminder to feishu', '每小时提醒', 'scheduled feishu message'.
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.