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...
extract-audio
by gupsammyThis skill should be used when the user asks to "extract audio", "get the mp3", "strip audio from video", "rip audio", "save audio from video", "convert to audio", "get the soundtrack", "pull the audio track", "save as mp3", "export audio", or "separate audio from video".
make-gif
by gupsammyThis skill should be used when the user asks to "make a GIF", "convert to GIF", "create a GIF from this video", "export as GIF", "turn this clip into a GIF", "make an animated GIF", or "gif this".
claw-advisor
by gupsammyThis skill should be used when the user asks about OpenClaw configuration, troubleshooting, setup, architecture, or any OpenClaw question. Triggers on "how do I configure OpenClaw", "set up telegram in OpenClaw", "gateway configuration", "OpenClaw troubleshooting", "claw advisor", "what's the best way to set up OpenClaw", "OpenClaw docs", "help me with OpenClaw", "openclaw channel setup", "debug OpenClaw", or needs guidance on OpenClaw features, channels, gateway, automation, models, or design decisions.
fetch-page
by gupsammyFetch and summarize a web page
create-claw-skill
by gupsammyThis skill should be used when the user asks to "create an OpenClaw skill", "make a claw skill", "build a skill for OpenClaw", "write a SKILL.md for openclaw", "add a skill to openclaw", "generate openclaw skill frontmatter", "create a clawhub skill", "port a skill to OpenClaw", "convert a Claude Code skill to claw", "migrate my skill to openclaw", or wants to author a new skill or port an existing Claude Code skill for the pi-coding-agent / OpenClaw ecosystem.
clean-branches
by gupsammyThis skill should be used when the user says "clean up branches", "delete merged branches", or "prune stale branches". Use whenever the user mentions branch cleanup, pruning, or stale branch deletion — even if they don't say "clean-branches" explicitly.
commit
by gupsammyThis skill should be used when the user says "commit my changes", "commit this", "create a commit", "git commit", "save my work", or mentions committing code.
get-pr-comments
by gupsammyThis skill should be used when the user says "get PR comments", "show PR feedback", "what comments on my PR", "PR review comments", "show me the review", "what did reviewers say", or asks about feedback on a pull request. Not for creating PRs or responding to comments.
make-changelog
by gupsammyThis skill should be used when the user asks to "create a changelog", "generate a changelog", "update my changelog", "fill in the changelog", "add a changelog", "CHANGELOG is missing entries", "changelog is out of date", "what's missing from my changelog", "changelog from git history", "write changelog", "release notes", or says "my project needs a CHANGELOG".
make-readme
by gupsammyThis skill should be used when the user asks to "create a README", "generate a README", "make a readme", "write a README for my project", "need a README", "add a README", "document my project", "set up project docs", "readme with badges".
push-pr
by gupsammyThis skill should be used when the user wants to create a pull request, or submit code for review. Triggers on "push a PR", "create a PR", "open a pull request", "make a PR", "submit for review".
update-claudemd
by gupsammyThis skill should be used when the user says "update CLAUDE.md", "refresh CLAUDE.md", "sync CLAUDE.md with the codebase", "reorganize CLAUDE.md", "optimize project instructions", or when CLAUDE.md is stale, verbose, or out of sync.
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.