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...
addressing-pr-feedback
by oryanmosheFetches, organizes, and addresses PR review comments from GitHub. Use when user asks to review PR comments, fix PR feedback, check what reviewers said, address review comments, or handle bot suggestions on a pull request. Triggers on "review PR", "fix comments", "PR feedback", "what did reviewers say", "address PR feedback", "check PR comments".
committing-code
by oryanmosheWrites git commit messages using conventional commits format with gitmoji. Use when creating git commits, preparing commit messages, or when the user asks to commit changes. Triggers on "commit", "git commit", "save changes", or any request to record changes to version control.
exploring-in-parallel
by oryanmosheParallelizes codebase exploration and research by launching multiple subagents simultaneously. Use when exploring codebases, researching questions, investigating bugs, gathering context from multiple sources, or any task requiring search across multiple files, patterns, or directories. Triggers on research, exploration, debugging, "why does this happen", "how does X work", or multi-file investigation.
grooming-sprint-tasks
by oryanmosheGrooms sprint tasks by cross-referencing Monday.com board with GitHub issues across multiple repos, then produces subitems, descriptions, linked issues, and SP estimates. Use when grooming a sprint, planning sprint tasks, preparing for sprint planning, or when user says "groom", "sprint planning", "cross-reference tasks and issues", "add subitems", or "create missing issues".
managing-agents-md
by oryanmosheCreates and maintains AGENTS.md documentation files that guide AI coding agents through a codebase. Use when adding a significant new feature or directory, changing project architecture, setting up a new project or monorepo, noticing a codebase has no AGENTS.md, or after major refactors that change project structure. Also triggers on "document this for agents", "update agents.md", or "create agents.md".
organizing-files
by oryanmosheOrganizes macOS files across Desktop, Documents, Downloads, and iCloud Drive into a consistent structure. Use when the user asks to organize files, clean up folders, sort downloads, declutter desktop, tidy up documents, or structure their filesystem. Triggers on "organize", "clean up", "sort files", "declutter", "file mess", "tidy", or any request about file/folder structure on macOS.
preserving-context
by oryanmosheCaptures working state before it is lost. Use before context compaction, when switching between unrelated tasks, after completing a logical phase of multi-step work, or when work will resume in a new session. Triggers on long tasks, multi-file changes, context warnings, or "continue later" scenarios.
reviewing-code
by oryanmosheReviews code changes for bugs, performance issues, security problems, and best practice violations. Use when reviewing PRs, before committing, after making code changes, or when user asks to review, check, or look over code. Catches N+1 queries, missing error handling, React hooks issues, test coverage gaps, and security vulnerabilities.
tracking-tasks
by oryanmosheEnforces disciplined task tracking across context boundaries. Use when starting any coding task, receiving a new user request mid-work, planning multi-step work, discovering sub-tasks or issues, before context compaction, switching between tasks, or resuming a previous session. Skip for purely informational questions with no code changes.
writing-skills
by oryanmosheGuides creation and editing of SKILL.md files following Anthropic best practices and this repo's conventions. Use when creating a new skill, editing an existing skill, porting a skill from another source, or reviewing skill quality. Triggers on "create skill", "new skill", "write skill", "edit skill", "improve skill", or any work that adds or modifies files under skills/.
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.