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...
realtime-express-tracking
by qq5855144实时快递查询，支持获取快递公司列表和查询快递物流轨迹。需要查快递、物流跟踪时使用。
company-info-lookup
by qq5855144通过公司名称、注册号或统一社会信用代码查询企业全维度工商信息，适用于企业尽调、商务合作评估、风险识别与市场分析场景。
kling-omni-image-generation
by qq5855144基于可灵 Kling Omni 模型文生图或参考图生图，支持多种分辨率和画面比例，生成结果需轮询获取；适用于创意设计、宣传海报制作、日常作图等场景
minimax-text-to-image
by qq5855144调用 MiniMax 文生图接口，根据文本描述生成图片并转存至 Supabase Storage，适用于创意设计、海报生成、游戏场景绘制等场景。
driving-exam-questions
by qq5855144驾考题库查询，获取科目一/科目四题目（含选项、答案、解析），支持按车型、章节、分页、随机排序筛选。适合驾考备考辅助和题目练习场景。
ai-ppt-generator
by qq5855144根据用户输入的主题自动生成完整 PPT 文件，返回封面图和下载链接；适用于办公汇报、教学课件、产品展示等需要快速生成 PPT 的场景。
minimax-subject-to-video
by qq5855144基于 MiniMax S2V-01 模型，上传人物主体图片和文本描述生成动态视频；支持异步轮询获取结果和下载链接。
wenxin-text-generation
by qq5855144调用百度文心大模型（ERNIE 4.5 Turbo）进行流式文本生成；适用于内容创作、摘要提炼、翻译、润色等 AI 文本处理场景。
train-ticket-query
by qq5855144火车票查询：查询车次时刻表、站站余票及票价信息。适用于出行规划、车票信息展示、列车站点查询等场景。
vehicle-traffic-restriction
by qq5855144查询全国多城市汽车尾号限行信息，获取指定日期的限行尾号、限行时段和限行区域；适用于出行规划、生活服务类应用场景
weather-forecast
by qq5855144查询全国各地今日实时天气、未来7天及未来15天天气预报，支持实时气象、生活指数、天气预警；适用于天气应用、出行规划等场景。
accurate-ocr
by qq5855144调用百度通用文字识别高精度版 OCR，对图片全部文字内容进行高精度检测识别，支持中英日韩等 20+ 语种，适用于文档数字化、多语言文本提取场景。
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.