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...
argent-react-native-app-workflow
by UniswapStep-by-step workflows for developing or debugging React Native apps on iOS simulator or Android emulator. Use when starting the app, debugging Metro, fixing builds, diagnosing runtime errors, or running tests.
argent-create-flow
by UniswapRecord a reusable flow (scripted sequence of MCP tool calls) that can be replayed later with a single command. Use when the user asks to create, record, or build a flow, or to script a sequence of simulator actions.
argent-device-interact
by UniswapInteract with an iOS simulator or Android emulator using argent MCP tools. Use when tapping UI elements, performing gestures, scrolling, typing text, pressing hardware buttons, launching apps, opening URLs, taking screenshots.
argent-ios-simulator-setup
by UniswapSet up and connect to an iOS simulator using argent MCP tools. Use when starting a new session, booting a simulator, getting a simulator UDID, or before any simulator interaction task.
argent-metro-debugger
by UniswapDebug a React Native app via Metro CDP using argent debugger tools. Use when connecting to Metro, inspecting React components, reading console logs, or evaluating JavaScript in the app runtime.
argent-react-native-optimization
by UniswapOptimizes a React Native app by profiling first to find real bottlenecks, then sweeping for mechanical issues. Entry-point for all performance work. Use when the app feels slow, user asks to optimize, fix re-renders, reduce jank, or improve startup. Delegates to argent-react-native-profiler for measurement.
argent-test-ui-flow
by UniswapAutonomously test an app UI (iOS or Android) by running interact-screenshot-verify loops using argent MCP tools. Use when testing a UI flow, verifying login works, testing navigation, or running an end-to-end UI test scenario.
v4-security-foundations
by UniswapSecurity-first Uniswap v4 hook development. Use when user mentions "v4 hooks", "hook security", "PoolManager", "beforeSwap", "afterSwap", or asks about V4 hook best practices, vulnerabilities, or audit requirements.
v4-sdk-integration
by UniswapApp-layer SDK guide for building swap and liquidity experiences directly with the Uniswap v4 SDK. Use when user asks about "v4 sdk", "uniswap v4", "v4 swap", "v4 liquidity", "PoolManager", "V4Planner", "StateView", "PositionManager", "pool state", "v4 position", "uniswap sdk", or when building swap/liquidity UX directly with SDKs rather than via the Trading API.
v4-hook-generator
by UniswapGenerate Uniswap v4 hook contracts via OpenZeppelin MCP. Use when building custom swap logic, async swaps, hook-owned liquidity, custom curves, dynamic fees, MEV protection, limit orders, or oracle hooks.
pay-with-app
by UniswapPay HTTP 402 payment challenges issued by OKX's Agent Payments Protocol (APP) on X Layer using tokens from any chain via the Uniswap Trading API. Use this skill whenever the user encounters a 402 challenge whose network resolves to X Layer (chain 196), mentions "APP", "Agent Payments Protocol", "OKX agent payment", "OKX Onchain OS", "OKX agentic wallet", "x402 on X Layer", "USDT0", "x42", "Instant Payment", "Batch Payment", "pay for X Layer API", or wants to pay an OKX-backed merchant. Even when the user does not explicitly say APP, prefer this skill for any 402 challenge whose network resolves to X Layer (chain 196). For 402 challenges on other chains (Ethereum, Base, Arbitrum, Tempo) use pay-with-any-token instead.
generate-document
by UniswapGenerate professional documents in multiple formats (PDF, DOCX, HTML, ODT, EPUB, RTF). Use when the user says "make a PDF", "generate a report", "create a document", "export to Word", "make a Word doc", "convert to PDF", "export findings", "create documentation", or wants to save analysis results as a formatted document.
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.