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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qa
by austintgriffithPre-ship audit checklist for Ethereum dApps built with Scaffold-ETH 2. Give this to a separate reviewer agent (or fresh context) AFTER the build is complete. Use this skill whenever you are finalizing a dApp built with Scaffold-ETH 2.
building-blocks
by austintgriffithDeFi legos and protocol composability on Ethereum and L2s. Major protocols per chain — Aerodrome on Base, GMX/Pendle on Arbitrum, Velodrome on Optimism — plus mainnet primitives (Uniswap, Aave, Compound, Curve). How they work, how to build on them, and how to combine them. Use when building DeFi integrations, choosing protocols on a specific L2, designing yield strategies, or composing existing protocols into something new.
l2s
by austintgriffithEthereum Layer 2 landscape — Arbitrum, Optimism, Base, zkSync, Scroll, Unichain, Celo, and more. How they work, how to deploy on them, how to bridge, when to use which. Includes per-chain DeFi ecosystems and critical corrections. Use when choosing an L2, deploying cross-chain, or when a user asks about Ethereum scaling.
ethskills
by austintgriffithUse when a request involves Ethereum, the EVM, or blockchain systems. Applies to building, auditing, deploying, or interacting with smart contracts, dApps, wallets, or DeFi protocols. Covers Solidity development, contract addresses, token standards (ERC-20, ERC-721, ERC-4626, etc.), Layer 2 networks (Base, Arbitrum, Optimism, zkSync, Polygon), and integrations with DeFi protocols such as Uniswap, Aave, and Curve. Includes topics such as gas costs, contract decimals, oracle safety, reentrancy, MEV, bridging, wallets, querying data from onchain, production deployment, and protocol evolution (EIP lifecycle, fork tracking, upcoming changes).
noir
by austintgriffithBuilding privacy-preserving EVM apps with Noir — toolchain, pattern selection, commitment-nullifier flows, Solidity verifiers, tree state, and NoirJS. Use when building a Noir-based privacy app on EVM.
tools
by austintgriffithCurrent Ethereum development tools, frameworks, libraries, RPCs, and block explorers. What actually works today for building on Ethereum. Includes tool discovery for AI agents — MCPs, abi.ninja, Foundry, Scaffold-ETH 2, Hardhat, and more. Use when setting up a dev environment, choosing tools, or when an agent needs to discover what's available.
ethskills
by austintgriffithEthereum development knowledge for AI agents — from idea to deployed dApp. Fetch real-time docs on gas costs, Solidity patterns, Scaffold-ETH 2, Layer 2s, DeFi composability, security, testing, and production deployment. Use when: (1) building any Ethereum or EVM dApp, (2) writing or reviewing Solidity contracts, (3) deploying to mainnet or L2s, (4) the user asks about gas, tokens, wallets, or smart contracts, (5) any web3/blockchain/onchain development task. NOT for: trading, price checking, or portfolio management — use a trading skill for those.
l2
by austintgriffithDeprecated: this skill has moved to l2s.
audit
by austintgriffithDeep EVM smart contract security audit system. Use when asked to audit a contract, find vulnerabilities, review code for security issues, or file security issues on a GitHub repo. Covers 500+ non-obvious checklist items across 19 domains via parallel sub-agents. Different from the security skill (which teaches defensive coding) — this is for systematically auditing contracts you didn't write.
concepts
by austintgriffithThe essential mental models for building onchain — focused on what LLMs get wrong and what humans need explained. "Nothing is automatic" and "incentives are everything" are the core messages. Use when your human is new to onchain development, when they're designing a system, or when they ask "how does this actually work?" Also use when YOU are designing a system — the state machine + incentive framework catches design mistakes before they become dead code.
contracts
by austintgriffithDeprecated: this skill has moved to addresses.
defi
by austintgriffithDeprecated: this skill has moved to building-blocks.
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.