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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nvim-test
by S1M0N38Execute tests and diagnose failures for love2d.nvim. Use when the user says "run tests", "run the suite", or asks to execute the test suite (full or single file). Also use when the user pastes test error output, asks what a test failure means, or needs help fixing a broken test. The test stack is mini.test via lazy.minit with *_spec.lua files in tests/. Do not trigger for writing tests, learning test APIs, setting up testing from scratch, or non-Neovim tools.
nvim-help
by S1M0N38Search and read Neovim's built-in :help documentation to look up API signatures, parameter types, option values, and event specifications from the user's installed runtime. Use when the user wants to consult reference material — function docs, help tags, option descriptions — not when they want to write, create, or debug something. Pairs with Context7 (neovim/neovim) for code examples; this skill provides exact local signatures and docs.
nvim-doc
by S1M0N38Write, update, and improve love2d.nvim help documentation (vimdoc) in doc/love2d.txt. Use when the user asks to write docs, update docs, generate the help file, add documentation for a function, or mentions vimdoc, help tags, or plugin documentation. Reads the plugin source code to extract API, commands, configuration, and other info from LuaCATS annotations and code structure, then writes a properly formatted doc/love2d.txt following vimdoc conventions. Do not use for general Neovim :help lookups (use nvim-help skill) or for writing README.md, CHANGELOG.md, or other non-vimdoc documentation.
nvim-commit
by S1M0N38Create conventional commits for love2d.nvim that are compatible with release-please and follow SemVer. Use when the user asks to commit changes, make a git commit, or says "/commit" while working on love2d.nvim. Analyzes the diff to produce correctly scoped, typed commit messages that release-please can parse into changelog entries and semantic version bumps.
nvim-plugin
by S1M0N38Neovim plugin development best practices and patterns for love2d.nvim. Use when planning, editing, implementing, or reviewing Neovim Lua plugin code — structuring a new plugin, writing setup/config, highlights, autocmds, keymaps, health checks, type annotations, debounce, state management, or user commands. Also use when the user asks about plugin architecture, conventions, or "how should I implement" a Neovim plugin feature. Do not use for general Lua development unrelated to Neovim plugins, Neovim configuration (init.lua), or running/debugging tests (use nvim-test).
nvim-init
by S1M0N38Initialize a Neovim plugin project after cloning from base.nvim template. Run once at the start of development to verify the development environment is set up correctly. Use when the user says "init", "setup", "initialize", "check environment", "nvim-init", or asks to verify their Neovim plugin development setup. Also use when the user says they just cloned the template or wants to start developing a Neovim plugin. Do not use for general Neovim plugin development tasks (use nvim-plugin) or for running tests (use nvim-test).
nvim-commit
by S1M0N38Create conventional commits for Neovim plugins that are compatible with release-please and follow SemVer. Use when the user asks to commit changes, make a git commit, or says "/commit" while working in a Neovim plugin project. Analyzes the diff to produce correctly scoped, typed commit messages that release-please can parse into changelog entries and semantic version bumps. Also use when the user asks about commit message format for their Neovim plugin or wants to know what type a change should be.
nvim-doc
by S1M0N38Write, update, and improve Neovim plugin help documentation (vimdoc) in doc/<plugin>.txt. Use when the user asks to write docs, update docs, generate the help file, add documentation for a function, or mentions vimdoc, help tags, or plugin documentation. Reads the plugin source code to extract API, commands, configuration, and other info from LuaCATS annotations and code structure, then writes a properly formatted doc/<plugin>.txt following vimdoc conventions. Do not use for general Neovim :help lookups (use nvim-help skill instead) or for writing README.md, CHANGELOG.md, or other non-vimdoc documentation.
nvim-help
by S1M0N38Search and read Neovim's built-in :help documentation to look up API signatures, parameter types, option values, and event specifications from the user's installed runtime. Use when the user wants to consult reference material — function docs, help tags, option descriptions — not when they want to write, create, or debug something. For writing Neovim Lua code, composing Treesitter queries, or fixing config issues, use general coding tools instead. Pairs with Context7 (neovim/neovim) for code examples; this skill provides exact local signatures and docs.
nvim-plugin
by S1M0N38Neovim plugin development best practices and patterns. Use when planning, editing, implementing, or reviewing Neovim Lua plugin code — structuring a new plugin, writing setup/config, highlights, autocmds, keymaps, health checks, type annotations, debounce, state management, or user commands. Also use when the user asks about plugin architecture, conventions, or "how should I implement" a Neovim plugin feature. Do not use for general Lua development unrelated to Neovim plugins, Neovim configuration (init.lua), or running/debugging tests (use nvim-test).
nvim-test
by S1M0N38Execute tests and diagnose failures for this Neovim plugin. Use when the user says "run tests", "run the suite", or asks to execute the test suite (full, single file, or offline). Also use when the user pastes test error output, asks what a test failure means, or needs help fixing a broken test. The test stack is mini.test + luassert with _spec.lua files. Do not trigger for writing tests, learning test APIs, setting up testing from scratch, or non-Neovim tools.
skill-creator
by S1M0N38Create new skills, modify and improve existing skills, and measure skill performance for the pi coding agent. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. Also use when the user mentions skills, SKILL.md, or wants to package capabilities for reuse.
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.