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...
disear-gua-de-proyecto-para-recetario-viajero-emocional-infantil
by ECNU-ICALKGenera instrucciones operativas y la estructura de capítulos para un proyecto de libro viajero de recetas donde niños asocian platos con emociones específicas para desarrollar inteligencia emocional.
three-part-lesson-designer
by GarethManningDesign a Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.
seeds-regenerative-inquiry-cycle
by GarethManningDesign a SEEDS regenerative inquiry cycle connecting place-based learning to ecological awareness for young learners. Use when building early childhood or primary inquiry around ecosystems and community.
english-swe-daily
by wquguruDaily English expression coach for intermediate software engineers. Use this skill whenever the user wants to improve their spoken or written English in a software engineering work context — especially for standups, Slack messages, 1:1s, meetings, giving feedback on code, asking for help, disagreeing politely, or casual small talk with teammates. Trigger this skill when the user says things like "teach me English", "practice English", "how do I say X at work", "daily English", "help me sound more natural", "English for standups", "how should I phrase this in Slack", or any similar request about sounding more natural or professional in English at a tech job. Also trigger when the user gives you a draft message or spoken phrase and asks you to improve it or make it sound more natural.
ieyc-kindergarten-report-writer
by gabrielmoreiraGenerates positive, professional progress reports for IEYC and kindergarten students, strictly adhering to word counts and incorporating specific evidence, attributes, or skills.
hypothetical-ladybird-book-content-generator
by gabrielmoreiraGenerates outlines, sample pages, and facing image descriptions for hypothetical Ladybird Books, ensuring age-appropriate, educational, and safe content.
early-childhood-educator
by HaibarakikuExpert Early Childhood Educator specializing in child development, play-based learning, emergent curriculum, and family partnerships. Expert in developmentally appropriate practice, Reggio Emilia, and assessing young children through observation. Use when: early-childhood-education, child-development, play-based-learning, emergent-curriculum, preschool, kindergarten,
montessori-teacher
by HaibarakikuExpert Montessori Teacher specializing in the Montessori Method, prepared environments, self-directed learning, and sensitive periods. Expert in mixed-age classrooms, Montessori materials, and cosmic education. Use when: montessori, montessori-method, prepared-environment, self-directed-learning, sensitive-periods, cosmic-education, mixed-age-classroom.
early-literacy-program
by WinbdaDesign early literacy programs for young children. TRIGGERS - Use when user needs help with early-literacy-program related tasks.
interviewer
by kevinaimonster模拟技术面试官。出题、追问、评分，帮用户准备技术面试。当用户说「模拟面试」「面试我」「出道面试题」「帮我练面试」「技术面试」「mock interview」「面试准备」「面试官模式」「考考我」「出题」「前端面试」「后端面试」「算法面试」「系统设计面试」时触发。关键词：面试、mock interview、面试官、出题、追问、评分、前端面试、后端面试、算法、系统设计、八股文、面试题、面试准备、模拟面试、技术面试、面经、leetcode、手撕代码、场景题、项目深挖
carls-math-thinking-coach
by lvzhengbin卡尔数学思维出题教练。为5-12岁（幼儿园中班到小学六年级）儿童生成趣味数学思维训练题。每次生成一道题目（含答案解析），并可输出适合A4打印的图片生成Prompt。当用户提到以下场景时使用本技能：(1) 需要给孩子出一道数学思维题, (2) 需要生成儿童逻辑训练/数学启蒙题目, (3) 提到"卡尔数学"、"思维训练"、"逻辑题"等关键词, (4) 需要生成可打印的儿童数学题图片。
kids-learning-creator
by fracabuGenera contenuti didattici interattivi per bambini di 10 anni su qualsiasi argomento. Include spiegazioni semplici, quiz, esperimenti, attività pratiche e prompt per immagini AI. Usa quando vuoi spiegare un concetto a un bambino in modo divertente e coinvolgente.
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.