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.
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reading
by prashantbhudwalResource-grounded reading workflow for books, papers, articles, and long-form workspace resources. Use when the learner asks to read, understand, summarize, analyze, close-read, discuss, or study a resource, or when active reading context is present and the request concerns that resource.
resolve-confusions
by prashantbhudwalUse when a learner's answer suggests a stable but faulty way of thinking, such as a hidden rule, misleading analogy, reasoning shortcut, misplaced procedure, or concept used outside its proper boundary. This skill helps Buddy uncover the learner's current model, replace it with a more reliable one, and verify transfer with a nearby case. Do not use for ordinary explanations or one-off corrections.
whiteboard-authoring
by prashantbhudwalAuthor clear, editable whiteboards with Buddy's compact append-program tool. Use when the teaching problem depends on canvas workflow, board layout, visual explanation, cumulative lesson records, worked examples, concept maps, diagrams, representation bridging, learner-work comparison, retrieval prompts, error analysis, or preserving a visual trail. Use this whenever the user mentions whiteboard, canvas, Excalidraw, draw, diagram, visual explanation, board work, or how much the agent should write or draw. Do not use for generic lesson planning, classroom management, or artifact authoring unless the visual canvas is central. MUST READ before using any whiteboarding tools.
compare-concepts
by prashantbhudwalContrast two related concepts so the learner stops conflating them.
explain
by prashantbhudwalTeach a concept directly and concisely before moving into application.
learn
by prashantbhudwalTeach for conceptual understanding, then move the learner into meaningful practice.
practice
by prashantbhudwalGive the learner concrete practice tasks that build expert thinking.
worked-example
by prashantbhudwalShow a complete example with explicit reasoning and transition to learner action.
teach-mathematics
by prashantbhudwalMathematics teaching protocol for sessions that require geometric figures, diagrams, calculations, or formal proof guidance. Use ONLY when the learner is working on mathematics that would benefit from rendered figures, computational verification via python_calculator, or structured proof scaffolding — not for simple arithmetic or casual math questions.
analogy
by prashantbhudwalGuidelines for writing good analogies to make your explanations better. Use when the learner needs a concrete bridge from something already known to something new, or when the learner needs an intuitive anchor for an abstract or unfamiliar idea or when the learner has a misconception that needs a bridge from something they already understand.
align-teaching-topics-to-grade-level-and-age
by prashantbhudwalUse when Buddy needs to answer what material, concept, skill, topic, task type, or depth is appropriate for a learner's age, grade, year level, school band, developmental level, or current readiness. Use for questions like what to teach at age X, what level to teach a topic, whether material is too advanced or too basic, what vocabulary or sentence complexity fits a grade band, how complex text should be, what prerequisite comes next, or how to scale material for K-2, 3-5, 6-8, 9-12, preschool, elementary, middle school, secondary, adolescents, or adults. Do not use for generic teaching-method selection; use teaching-models. Do not use for broad lesson/framework design; use learning-design-frameworks. Do not use for authoring worksheets, quizzes, rubrics, or handouts; use teaching-resource-authoring.
assess
by prashantbhudwalconduct better assessments. has subject matter expertise about about condu
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.