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...
regenerative-project-design-orchestrator
by GarethManningPresent project-design pathway options and orchestrate regenerative projects using backwards design, PBL, SEEDS, compassionate systems action, or civic/service pathways.
jutiku-quiz-expert
by diegosouzapw专门用于从文档生成结构化试题的智能体。分析内容属性（大纲型 vs 知识型），提取关键点，并生成 JSON 或 Markdown 格式的高质量试题。当用户要求"根据文件出题"、"创建测验"、"制作试卷"或"提取考题"时使用此技能。
export
by yha9806Convert thesis chapters and reading notes from Markdown to Word (.docx) and package for submission. Use when preparing materials for supervisors or examiners.
ibdp-design-technology-ia-evaluator-and-generator
by gabrielmoreiraEvaluates student work or generates content for the International Baccalaureate Diploma Programme (IBDP) Design Technology Internal Assessment based on specific criteria checklists (A, B, C). Ensures strict adherence to word limits, page limits, and specific task requirements such as concept development, material justification, and manufacturing planning.
scratch
by dkbnullScratch开发专家助手。当用户需要进行Scratch图形化编程、少儿编程教育、互动游戏开发或动画制作时调用。
kaoyan-electronics
by TreasoniThis skill routes 822 electronics learning requests to specialized sub-skills for 湖南大学822电子技术基础考研 preparation, including circuit diagram analysis, SOP templates for 17 problem types, knowledge point structure, and MemOS integration for persistent tracking.
book-navigator
by Vishnu-tppr12th Grade Chapter-to-Resource Mapping (PCMB/PCMC). Automatically recommends the right section of SL Arora, RD Sharma, MS Chauhan, and NCERT Exemplar.
automotive-training-program
by WinbdaDesign automotive technician training programs. TRIGGERS - Use when user needs help with automotive-training-program related tasks.
yanxue-course-manager
by 18816132863📚 研学方案管理与智能生成技能。支持按城市、学段、景点、主题、时长生成完整的研学课程方案，并提供方案的保存、管理、Word 导出及文件导入导出功能。适用于中小学（1-9年级及高中）的研学旅行课程设计。
rcj-soccer-coach
by IITA-ProyectosUse when working in the IITA Soccer Open repo to give technical feedback to students. Frames feedback as tema-a-analizar with risk-no-fix / risk-fix / tiempo, prioritizes P0/P1/P2, and demands a hardware-real test plan. Activates the senior-coach lens (RoboCupJunior Soccer Open + Middle Size League experience).
minecraft-block-crafter
by yadokari1130クラフターの特性、配置方法、および操作に関するガイドラインです。
minecraft-block-detector-rail
by yadokari1130ディテクターレールの特性、配置方法、および操作に関するガイドラインです。
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.