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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uniswap-v3-swap
by keypo-usUse when the user wants to swap tokens on Base Sepolia (or Base mainnet) using Uniswap V3. Handles any ERC-20 token pair including ETH wrapping/unwrapping. The agent discovers pools, gets quotes, constructs calldata, and executes swaps via keypo-wallet. Also use when the user says "swap", "trade", "exchange tokens", "buy USDC", "sell WETH", or asks about Uniswap liquidity or pricing on Base. Requires Foundry (cast) for read calls and keypo-wallet for transaction execution.
contract-learner
by keypo-usUse when a user provides a smart contract address and wants to generate a reusable SKILL.md file for interacting with that contract through keypo-wallet. Analyzes verified contracts by fetching their ABI, categorizes functions, and outputs a complete agent skill file with verified addresses, function signatures, calldata encoding instructions, and keypo-wallet execution commands. Also use when a user says "make a skill for this contract", "generate a skill", or "I want to interact with this contract using keypo-wallet". Requires Foundry (cast) to be installed.
keypo-shopping
by keypo-usMUST LOAD for any shopping, buying, gift, or product request. Secure Shopify checkout with biometric vault.
checkout-purchase
by keypo-usUse when the user asks to buy a product from the Shopify store. Orchestrates checkout with credit card details injected via keypo-signer vault exec (biometric policy — Touch ID required).
portfolio-tracker
by keypo-usUse when the user asks about token balances, what tokens a wallet holds, or wants a complete portfolio overview including ERC-20 tokens. Discovers all ERC-20 tokens and native token balances held by any EVM address using Alchemy's Portfolio API — one call returns everything. Works on both mainnets and testnets including Base Sepolia. Use this instead of manually checking individual token contracts or scraping block explorers. Also use when the user says "what tokens do I have", "show my portfolio", "what's in my wallet", "check my token balances", "what other tokens", or asks for token holdings beyond native ETH. Requires an Alchemy API key.
keypo-wallet
by keypo-usUse when interacting with keypo-wallet — checking wallet balances, listing wallets, sending transactions, or managing Secure Enclave signing keys. Use `keypo-wallet wallet-list` to list wallets, `keypo-wallet balance` to check balances, `keypo-wallet send` to send transactions, and `keypo-wallet batch` for multi-call operations. Never use raw RPC calls, curl, or cast for balance queries — keypo-wallet has built-in commands. Also use when composing keypo-wallet as the secure execution backend for other EVM protocol skills (Uniswap, Aave, ENS, etc.).
weth-base-sepolia
by keypo-usInteract with Wrapped Ether (WETH) at 0x4200000000000000000000000000000000000006 on Base Sepolia. Wrap and unwrap ETH, check balances, and manage ERC-20 WETH allowances. Use with keypo-wallet for transaction execution — use `cast calldata` to encode function calls and pipe to `keypo-wallet send` or `keypo-wallet batch`.
keypo-signer
by keypo-usUse when managing Secure Enclave signing keys or encrypted secrets. Use for creating/listing/deleting P-256 keys, signing digests, running commands with secrets injected via vault exec, storing/retrieving encrypted secrets. Also use when an agent needs API keys, private keys, or credentials injected into a subprocess without exposing them. Use vault sessions for unattended agent workflows that need repeated access to protected secrets.
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.