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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docker
by mattwoodcoSetup Docker Compose local development stack with Postgres, Redis, S3 (RustFS), Mailpit, Meilisearch, Ollama, and Jaeger. Use this skill when the user says "setup docker", "docker compose", "start containers", or "docker local dev".
storage
by mattwoodcoUnified file storage — S3-compatible (RustFS/MinIO) for local dev, Vercel Blob for production. Simple upload/download/delete/list API with presigned URLs.
group-chat
by mattwoodcoReal-time group chat with threads, reactions, mentions, and file attachments — Drizzle for persistence, Liveblocks for real-time delivery. Use this skill when the user says "add group chat", "setup chat", "add messaging", "team chat", or "group chat".
realtime
by mattwoodcoReal-time collaboration with Liveblocks — presence cursors, conflict-free state sync, and room-based access. Use this skill when the user says "add realtime", "setup collaboration", "add liveblocks", "multiplayer", "live cursors", or "real-time sync".
search
by mattwoodcoSetup Meilisearch full-text search with Redis caching, rate limiting, input sanitization, and API key management. Use this skill when the user says "setup search", "add search", "setup meilisearch", "full-text search", or "search integration".
ai-rag-ingest
by mattwoodcoPDF ingestion pipeline — chunked upload of large PDFs (up to 2GB) to S3 storage, parallel PDF parsing with unpdf, page-level text extraction, and processing status tracking with Postgres. Use this skill when the user says "setup PDF upload", "add PDF ingestion", "setup ai-rag-ingest", or "add document upload".
audio-room
by mattwoodcoAudio-only room with LiveKit — circular avatar grid, speaking indicators, raise hand queue. Twitter Spaces / Clubhouse style. Use this skill when the user says "add audio room", "audio chat", "voice room", "spaces", "clubhouse", or "audio-only room".
recording
by mattwoodcoServer-side room recording with LiveKit Egress — composite and track-based recording, S3 storage, recording metadata in Postgres. Use this skill when the user says "add recording", "record room", "record video", "record meeting", "egress recording", or "save recording".
screen-share
by mattwoodcoScreen sharing components for LiveKit video rooms — toggle button, full-width screen share view with PiP camera overlay, and an auto-switching presenter layout. Use this skill when the user says "add screen share", "setup screen sharing", "presenter mode", "screen share view", or "setup screen-share".
video-room
by mattwoodcoLiveKit video room infrastructure — server client, JWT token generation, room management API routes, and a client-side hook for joining rooms. Use this skill when the user says "add video", "setup video rooms", "add livekit", "video calling", or "setup video-room".
video-ui
by mattwoodcoLiveKit video UI components — room provider, participant grid, speaker view, controls bar, prejoin screen, and participant tile using @livekit/components-react and shadcn/ui. Use this skill when the user says "add video UI", "video components", "setup video-ui", "video call UI", or "participant grid".
livestream
by mattwoodcoGo-live from a LiveKit room to MUX via RTMP egress — supports MUX, YouTube, Twitch, and custom RTMP destinations. Auto-archives to MUX VOD when stream ends. Use this skill when the user says "add livestream", "go live", "stream to mux", "rtmp stream", "broadcast", or "live streaming".
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.