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...
changeset
by OpenZeppelinAdd a changeset file for a package version bump
setup-solidity-contracts
by OpenZeppelinSet up a Solidity smart contract project with OpenZeppelin Contracts. Use when users need to: (1) create a new Hardhat or Foundry project, (2) install OpenZeppelin Contracts dependencies for Solidity, (3) configure remappings for Foundry, or (4) understand Solidity import conventions for OpenZeppelin.
setup-stellar-contracts
by OpenZeppelinSet up a Stellar/Soroban smart contract project with OpenZeppelin Contracts for Stellar. Use when users need to: (1) install Stellar CLI and Rust toolchain for Soroban, (2) create a new Soroban project, (3) add OpenZeppelin Stellar dependencies to Cargo.toml, or (4) understand Soroban import conventions and contract patterns for OpenZeppelin.
upgrade-cairo-contracts
by OpenZeppelinUpgrade Cairo smart contracts using OpenZeppelin's UpgradeableComponent on Starknet. Use when users need to: (1) make Cairo contracts upgradeable via replace_class_syscall, (2) integrate the OpenZeppelin UpgradeableComponent, (3) understand Starknet's class-based upgrade model vs EVM proxy patterns, (4) ensure storage compatibility across upgrades, (5) guard upgrade functions with access control, or (6) test upgrade paths for Cairo contracts.
setup-stylus-contracts
by OpenZeppelinSet up a Stylus smart contract project with OpenZeppelin Contracts for Stylus on Arbitrum. Use when users need to: (1) install Rust toolchain and WASM target for Stylus, (2) create a new Cargo Stylus project, (3) add OpenZeppelin Stylus dependencies to Cargo.toml, or (4) understand Stylus import conventions and storage patterns for OpenZeppelin.
develop-secure-contracts
by OpenZeppelinDevelop secure smart contracts using OpenZeppelin Contracts libraries. Use when users need to integrate OpenZeppelin library components — including token standards (ERC20, ERC721, ERC1155), access control (Ownable, AccessControl, AccessManager), security primitives (Pausable, ReentrancyGuard), governance (Governor, timelocks), or accounts (multisig, account abstraction) — into existing or new contracts. Covers pattern discovery from library source, CLI contract generators, and library-first integration. Supports Solidity, Cairo, Stylus, and Stellar.
setup-cairo-contracts
by OpenZeppelinSet up a Cairo smart contract project with OpenZeppelin Contracts for Cairo on Starknet. Use when users need to: (1) create a new Scarb/Starknet project, (2) add OpenZeppelin Contracts for Cairo dependencies to Scarb.toml, (3) configure individual or umbrella OpenZeppelin packages, or (4) understand Cairo import conventions and component patterns for OpenZeppelin.
upgrade-stylus-contracts
by OpenZeppelinUpgrade Stylus smart contracts using OpenZeppelin proxy patterns on Arbitrum. Use when users need to: (1) make Stylus Rust contracts upgradeable with UUPS or Beacon proxies, (2) understand Stylus-specific proxy mechanics (logic_flag, WASM reactivation), (3) integrate UUPSUpgradeable with access control, (4) ensure storage compatibility across upgrades, or (5) test upgrade paths for Stylus contracts.
upgrade-stellar-contracts
by OpenZeppelinUpgrade Stellar/Soroban smart contracts using OpenZeppelin's upgradeable module. Use when users need to: (1) make Soroban contracts upgradeable via native WASM replacement, (2) use Upgradeable or UpgradeableMigratable derive macros, (3) implement atomic upgrade-and-migrate patterns with an Upgrader contract, (4) ensure storage key compatibility across upgrades, or (5) test upgrade paths for Soroban contracts.
upgrade-solidity-contracts
by OpenZeppelinUpgrade Solidity smart contracts using OpenZeppelin proxy patterns. Use when users need to: (1) make contracts upgradeable with UUPS, Transparent, or Beacon proxies, (2) write initializers instead of constructors, (3) use the Hardhat or Foundry upgrades plugins, (4) understand storage layout rules and ERC-7201 namespaced storage, (5) validate upgrade safety, (6) manage proxy deployments and upgrades, or (7) understand upgrade restrictions between OpenZeppelin Contracts major versions.
wiremock-test
by OpenZeppelinManage WireMock proxy for RPC testing. Starts the proxy, arms scenarios to simulate transient errors (timeouts, 500s, connection resets, and network-specific errors like Stellar TRY_AGAIN_LATER), and checks metrics. Use to verify relayer behavior under failure conditions.
speckitclarify
by OpenZeppelinIdentify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
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.