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...
liftoff
by CoderCocoUse when the user wants to build the planned tasks for a mission issue, or when /mission dispatches the build phase. Trigger on "liftoff <N>" or "/liftoff". Thin wrapper around liftoff-workflow.js — Astronauts implement tasks in dependency rounds, Flight Controllers verify, commits land in the worktree. Requires a plan from /pre-launch.
docking
by CoderCocoUse when the mission branch is ready for a pull request, or when /mission dispatches the PR phase. Trigger on "docking <N>" or "/docking". Thin wrapper around docking-workflow.js — pushes the branch, opens a PR with Closes
comms
by CoderCocoUse when the mission PR has review comments to address. Runs a single-pass fetch→triage→fix→reply cycle and saves state. For continuous watching, guide the user to run `/loop 5m /comms <N>`. Trigger on "comms <N>", "/comms", or when the user wants to handle PR review comments.
work-on
by CoderCocoUse when the user wants to start working on a GitHub issue. Trigger whenever the user says "work on issue
swarm
by CoderCocoUse when the user wants to work on a GitHub issue with a coordinated crew of specialised sub-agents. Trigger whenever the user says "swarm issue
open-pr
by CoderCocoUse when the user is ready to put up a pull request for an issue they've been implementing — typically work started via the issue-flow:work-on skill. Trigger whenever the user says "open the PR", "raise a PR", "ship it", "/open-pr", "create a PR for this issue", or otherwise signals that implementation is done and the next step is a pull request. Verifies the issue's checklist is actually complete, discovers any PR conventions present in the current repo (templates, CLAUDE.md notes, sibling PR skills), opens a ready-for-review PR that uses the right closing keyword so GitHub auto-closes the issue on merge, and moves the project card to "In Review" when that column exists. Use proactively whenever a user wraps up work on an issue branch.
mission-debrief
by CoderCocoUse when the user wants to add new code-review findings to the Systems Inspector's rubric, has review feedback to record, pastes a postmortem of issues a reviewer caught, or mentions "mission debrief", "add to rubric", "the Systems Inspector missed these", "update the review checklist". Takes free-form findings input and folds them into references/review-rubric.md with classification, dedup, and a confirmation gate.
mission
by CoderCocoUse when the user wants to start or advance the full end-to-end mission workflow for a GitHub issue. Trigger on "/mission <N>", "mission issue N", "continue mission", "/mission --status", or any signal that the user wants to orchestrate an issue through plan→build→review→PR. Top-level orchestrator — invokes the pre-launch, liftoff, systems-check, and docking skills in order; each phase skill owns its own user interaction and workflow.
pre-launch
by CoderCocoUse when the user wants to plan a GitHub issue in the mission workflow, or when /mission dispatches planning. Trigger on "pre-launch <N>", "/pre-launch", or "replan issue N". Dispatches the Flight Director interactively (branch and worktree are created during planning), answers open questions with the user, persists the flight plan to plan.json, and confirms readiness. Never auto-advances — /mission or the user runs /liftoff next.
systems-check
by CoderCocoUse when the user wants the mission code review phase, or when /mission dispatches it. Trigger on "systems-check <N>" or "/systems-check". Thin wrapper around systems-check-workflow.js — language-bucketed Systems Inspectors review the full branch diff, repair Astronauts fix actionable findings; on exhausted rounds asks the user whether to continue, skip, or stop. Requires a plan from /pre-launch.
setup
by CoderCocoUse when the user wants to configure which models the mission crew uses. Trigger on "mission setup", "configure mission models", "mission config", or "/setup" in a mission context. Interactive walkthrough that writes .claude/mission.local.md with per-role model defaults consumed by all mission skills; doubles as reconfigure when the file already exists.
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.