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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proof
by fredrikaverpilProvide proof to your claim(s)
aip
by fredrikaverpilGoogle API Improvement Proposals (AIP) reference. Use BEFORE designing or reviewing APIs, protobuf definitions, or any work involving Google API design standards. Fetches relevant AIP rules from https://google.aip.dev for the task at hand.
codebase-audit
by fredrikaverpilAnalyze a git repo's history to surface high-churn files, ownership risks, bug hotspots, momentum trends, and firefighting patterns. Use this skill whenever the user wants to understand a codebase, assess repo health, or orient themselves before reading code — even if they don't explicitly say "audit".
gh-cli
by fredrikaverpilGitHub CLI (gh) command reference. Use when working with GitHub repositories, PRs, issues, actions, `gh api`, or any GitHub operations from the command line.
gh-pr
by fredrikaverpilThis skill should be used when creating a GitHub pull request via `gh pr create`. Defines PR body format with Why/What/Notes sections and ensures proper assignment.
git-commit
by fredrikaverpilThis skill should be used BEFORE running any git commit command. Triggers when about to run `git commit`. Ensures commit messages follow Conventional Commits specification.
golang-style
by fredrikaverpilUse this skill BEFORE writing or editing any Go (.go) files. Triggers when about to create, modify, or add code to .go files. Enforces happy path coding, error wrapping, sentinel errors, godoc-style comments, and `go doc` usage.
neovim
by fredrikaverpilInteract with the user's running Neovim instance via RPC. Use this skill when you need to execute Lua or Vimscript inside Neovim, query buffer state, send commands, or interact with the Neovim runtime in any way. Triggers when the user asks about their current Neovim session, wants to run something inside Neovim, or when you need to inspect Neovim state (buffers, windows, options, LSP, etc.). Also use when running inside a Neovim terminal and needing to communicate with the parent editor.
nvim-config
by fredrikaverpilNative Neovim config idioms and conventions — use whenever writing, reviewing, or modifying any Neovim configuration that uses Neovim's built-in conventions WITHOUT a plugin manager framework (no lazy.nvim, packer, etc.). Covers directory structure, vim.pack plugin management, lsp/ auto-discovery, plugin/ loading order, keymaps, and standard paths. Trigger on any task involving init.lua, plugin/*.lua, lsp/*.lua, vim.pack.add(), vim.lsp.enable(), or "native neovim config" — even if the user just says "add a plugin" or "configure LSP" in a native-style config.
nvim-plugin
by fredrikaverpilGuide for writing Neovim plugins in Lua following official Neovim conventions (https://neovim.io/doc/user/lua-plugin/). Use this skill whenever the user is creating, modifying, or reviewing a Neovim plugin — including when they mention plugin structure, ftplugin, health checks, keymaps, setup() functions, vimdoc, LuaCATS annotations, or lazy loading in the context of Neovim plugin development. Also trigger when the user is working in a directory that looks like a Neovim plugin (contains plugin/, lua/, ftplugin/ subdirectories).
obsidian
by fredrikaverpilFind, navigate, read, and edit notes in the user's personal Obsidian vault (an iCloud-synced markdown collection on macOS). Use whenever the user mentions "the vault", "my notes", "Obsidian", a daily note, a meeting note, scratchpad, or asks to look up/jot down something that sounds personal-knowledge-base-like (e.g. "what did I write about X", "add a note about Y", "today's daily note") — even if they don't say the word "Obsidian".
plan-interview
by fredrikaverpilBefore creating a plan, interview the user
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.