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...
vhs-terminal-gifs
by gwenwindflowerCreate terminal GIFs/MP4s with charmbracelet vhs from a .tape script. Use when the user wants to record, demo, or capture a terminal workflow for docs, blogs, or READMEs.
developing-in-lightdash
by gwenwindflowerUse when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, sql, set-warehouse), or creating/editing charts, dashboards, metrics, and dimensions as code
learning-opportunities
by gwenwindflowerOffer 10–15 minute learning exercises after architectural work — new files, schema changes, refactors, unfamiliar patterns. Use when wrapping a meaningful chunk of work or when the user asks to understand code more deeply.
shadcn-ui
by gwenwindflowerGuide for working with shadcn/ui components in any project. Use when: (1) Adding shadcn components, (2) Customizing components with variants or styles, (3) Understanding the shadcn system architecture, (4) Troubleshooting shadcn setup or styling issues.
shadcn-ui
by gwenwindflowerAdd, customize, and troubleshoot shadcn/ui components in any project. Use when working with shadcn components, variants, components.json, or the shadcn CLI.
lazygit-custom-commands
by gwenwindflowerBuild and troubleshoot lazygit customCommands in config.yml. Use for prompt-driven commands, context-specific keybindings, commandMenus, template-based construction, and conventional-commit style workflows.
lazygit-custom-commands
by gwenwindflowerBuild and debug lazygit customCommands in config.yml — prompts, contextual keybindings, commandMenus, template-based construction, and conventional-commit flows. Use when editing lazygit config or designing a custom command.
electron
by gwenwindflowerAutomate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol
electron
by gwenwindflowerAutomate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify) via agent-browser over Chrome DevTools Protocol. Use when scripting or inspecting an Electron app from the terminal.
fallow
by gwenwindflowerCodebase intelligence for JavaScript and TypeScript. Free static layer finds unused code (files, exports, types, dependencies), code duplication, circular dependencies, complexity hotspots, architecture boundary violations, and feature flag patterns. Runtime coverage merges production execution data into the same health report for hot-path review, cold-path deletion confidence, and stale-flag evidence - a single local capture is free, while continuous/cloud runtime monitoring is paid. 94 framework plugins, zero configuration, sub-second static analysis. Use when asked to analyze code health, find unused code, detect duplicates, check circular dependencies, audit complexity, check architecture boundaries, detect feature flags, clean up the codebase, auto-fix issues, merge runtime coverage, or run fallow.
learning-goal
by gwenwindflowerGuide the learner through a structured goal-setting exercise grounded in research on Mental Contrasting with Implementation Intentions (MCII). The exercise helps developers set concrete learning goals, visualize meaningful outcomes, anticipate realistic obstacles, and build if-then plans to overcome them.
learning-goal
by gwenwindflowerFacilitate a 10–15 minute Mental Contrasting + Implementation Intentions (MCII) goal-setting exercise. Use when the user asks to set a learning goal, structure skill development, or plan learning for a new project.
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.