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...
dutch-corporate-law
by FutureAtomsBV/NV formation, corporate governance, M&A, KVK registration, and BW Book 2 analysis. Use this skill whenever the user asks about Dutch companies, BV incorporation, shareholder agreements, director liability, bestuurdersaansprakelijkheid, aandeelhoudersovereenkomst, corporate governance, or KVK registration.
dutch-case-law-research
by FutureAtomsSearch, retrieve, and analyze Dutch case law using ECLI identifiers and Rechtspraak.nl. Use this skill whenever the user asks about Dutch court decisions, jurisprudentie, rechtspraak, case law, ECLI numbers, Hoge Raad rulings, or wants to find legal precedents under Dutch law.
arch-compliance
by FutureAtomsFull-cycle architecture compliance with truthfulness audit for Kartix. Goes beyond "tests pass" to verify what is genuinely implemented vs stubbed, fake, or partially wired. Use this skill whenever the user wants to check architecture compliance, find what's still missing, finish implementing remaining gaps, keep going until in-scope work is actually closed or blocked, add or fix tests, audit what's real vs fake, rerun verification, measure honest feature coverage, or ensure the repo honestly matches the architecture and plan. Trigger on "check architecture", "what's left", "finish implementing", "complete everything else", "verify everything", "run the full cycle", "what's real vs stubbed", "audit implementation", "close the gaps", "add tests and coverage", or similar.
react-native-2026-nativewind-setup
by FutureAtomsSet up and configure NativeWind v4 with Expo for Tailwind CSS styling in React Native apps
livekit-noise-cancellation
by FutureAtomsImplement AI-powered noise cancellation with Krisp for LiveKit voice AI and video calls
livekit-ingress
by FutureAtomsImport RTMP, WHIP, and media streams into LiveKit rooms from OBS, encoders, and external sources
livekit-agents-setup
by FutureAtomsSet up LiveKit Agents framework for building AI voice and video agents with Python or Node.js
livekit-expo
by FutureAtomsSet up LiveKit with Expo SDK for audio/video rooms using development builds and expo-dev-client
livekit-data-channels
by FutureAtomsImplement real-time messaging, RPC, and data streaming in LiveKit rooms
livekit-voice-pipeline
by FutureAtomsBuild voice AI agents with STT-LLM-TTS pipeline, turn detection, and interruption handling in LiveKit
livekit-server-sdk
by FutureAtomsUse LiveKit Server SDKs for Python, Node.js, and Go to manage rooms, participants, tokens, and backend operations
livekit-tts
by FutureAtomsConfigure Text-to-Speech models for LiveKit agents with Cartesia, ElevenLabs, OpenAI, and custom voices
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.