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...
yeet
by PaulRBergThis skill should be used when the user asks to create or update a GitHub PR, file or update an issue, post a comment, or start a discussion. Trigger phrases include "create PR", "open PR", "file an issue", "update issue", "yeet a PR/issue/discussion", "comment on an issue".
effect-ts
by PaulRBergUse for Effect-TS code and patterns including services, layers, errors, Schema/JSONSchema, @effect/ai tools, code importing from `effect`, or @prb/effect-next.
bump-deps
by PaulRBergThis skill should be used when the user asks to "update dependencies", "update npm packages", "bump dependencies", "upgrade node packages", "check for outdated packages", "update package.json", or mentions dependency updates, npm/pnpm/yarn/bun package upgrades, or taze CLI usage.
cli-just
by PaulRBergThis skill should be used when the user asks to "create a justfile", "write just recipes", "configure just settings", "add just modules", "use just attributes", "set up task automation", mentions justfile, just command runner, or task automation with just.
code-simplify
by PaulRBergThis skill should be used when the user asks to "simplify code", "clean up code", "refactor for clarity", "reduce complexity", "improve readability", "make this easier to maintain", or asks to simplify recently modified code.
evm-chains
by PaulRBergThis skill should be used when the user asks to resolve an EVM chain name or chain ID; find chain metadata such as a default public RPC, native currency symbol, or block explorer URL; determine whether a chain is supported by RouteMesh; or read on-chain account data for any EVM chain — "check ETH balance", "query ERC-20 balance", "get wallet balance", "check token holdings", "fetch NFT transfers", "ERC-721 or ERC-1155 transfer history", "transaction history", "find first funding transaction", "trace fund origin", "who funded this address", "query Etherscan", "query Blockscout", or "look up a chain on Chainscout". It routes each data query through Etherscan API V2 (preferred) or the Blockscout/Chainscout APIs (fallback for chains Etherscan doesn't serve), with direct JSON-RPC as a last resort. Also use it for chain resolution before fetching data from or interacting with an EVM chain.
md-docs
by PaulRBergUse ONLY to update or initialize README.md, CLAUDE.md, or AGENTS.md. Do not trigger for other Markdown files.
sweep-sablier-lockup
by PaulRBergCheck whether the wallet in repo-root dotenvx `.env` has recipient-owned Sablier Lockup streams with assets due for withdrawal. Use when Codex needs to inspect Sablier streams broadly or Sablier Lockup specifically, query the Lockup Hyperindex GraphQL endpoint, confirm exact withdrawable balances onchain with cast, prepare zero-value `withdrawMultiple` transactions per contract, simulate them, and wait for user confirmation before broadcasting.
sweep-sablier-flow
by PaulRBergCheck whether the wallet in repo-root dotenvx `.env` has recipient-owned Sablier Flow streams with assets due for withdrawal. Use when Codex needs to inspect Sablier streams broadly or Sablier Flow specifically, query the Flow Hyperindex GraphQL endpoint, confirm exact withdrawable balances onchain with cast, prepare zero-value per-contract `batch(bytes[])` withdrawals, simulate them, and wait for user confirmation before broadcasting.
scan-defi
by PaulRBergScan one Ethereum Mainnet address for withdrawable / claimable DeFi balances across known protocols — like Rabby, DeBank, Etherscan, or Octav. Use when asked to check what an address can withdraw, find or audit its DeFi positions, discover supplied / staked / LP / vault / savings balances or claimable rewards, or "what is this wallet holding in DeFi". Discovers ERC-20 holdings via Blockscout, confirms exact balances on-chain via Multicall3, classifies them against a per-protocol registry (Aave, Compound, Lido, Uniswap, Curve, Convex, Maker/Sky, Rocket Pool, EigenLayer, Ethena, Morpho, Yearn, Sablier), runs bespoke checks for NFT LPs, withdrawal queues, cooldowns and claimable rewards, then prints a report table with amounts, USD, status, and how to withdraw. Ethereum Mainnet only; strictly read-only (never signs or broadcasts).
zsh-completions
by PaulRBergAdd custom zsh completions for a tool.
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.