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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hig-components-layout
by raintree-technologyApple Human Interface Guidelines for layout and navigation components. Use this skill when the user asks about "sidebar", "split view", "tab bar", "tab view", "scroll view", "window design", "panel", "list view", "table view", "column view", "outline view", "navigation structure", "app layout", "boxes", "ornaments", or organizing content hierarchically in Apple apps. Also use when the user says "how should I organize my app", "what navigation pattern should I use", "my layout breaks on iPad", "how do I build a sidebar", "should I use tabs or a sidebar", or "my app doesn't adapt to different screen sizes". Cross-references: hig-foundations for layout/spacing principles, hig-platforms for platform-specific navigation, hig-patterns for multitasking and full-screen, hig-components-content for content display.
aptos-dapp-integration
by raintree-technologyExpert on building decentralized applications on Aptos with frontend integration, wallet connectivity (Petra, Martian, Pontem), TypeScript SDK, transaction submission, and wallet adapter patterns. Triggers on keywords wallet connect, petra, martian, pontem, typescript sdk, aptos sdk, dapp, frontend integration, wallet adapter, transaction, sign
aptos-gas-optimization
by raintree-technologyExpert on Aptos gas optimization, performance tuning, storage costs, execution efficiency, inline functions, aggregator usage, parallel execution, table vs vector tradeoffs, and gas profiling tools. Triggers on keywords gas optimization, performance, gas cost, storage fee, inline, aggregator, parallel execution, gas profiling, optimization
aptos-framework
by raintree-technologyExpert on Aptos Framework (0x1 standard library) - account, coin, fungible_asset, object, timestamp, table, event, vector, string, option, error, and other core modules. Triggers on keywords aptos framework, 0x1, account module, table, smarttable, event, timestamp, randomness, aggregator, resource account
aptos-move-language
by raintree-technologyExpert on Move programming language - abilities (copy/drop/store/key), generics, phantom types, references, global storage operations, signer pattern, visibility modifiers, friend functions, inline optimization, and advanced type system. Triggers on keywords move language, abilities, generics, phantom type, borrow global, signer, friend, inline, type parameter
aptos-token-standards
by raintree-technologyExpert on Aptos token standards including fungible tokens (Coin, Fungible Asset), non-fungible tokens (Digital Asset standard, Token V1/V2), collections, metadata, minting, burning, and transfer patterns. Triggers on keywords token, nft, fungible asset, coin, digital asset, collection, mint, burn, metadata, royalty
aptos-expert
by raintree-technologyExpert on Aptos blockchain, Move language, smart contracts, NFTs, DeFi, and Aptos development. Triggers on keywords aptos, move, blockchain, smart contract, nft, defi, web3, mainnet, testnet, devnet
aptos-object-model
by raintree-technologyExpert on Aptos Object Model - ObjectCore, Object<T> wrapper, constructor references, ExtendRef/DeleteRef/TransferRef capabilities, object ownership, named vs generated objects, composability, and migration from resource-only patterns. Triggers on keywords object model, objectcore, constructorref, extendref, deleteref, transferref, named object, object ownership, composable object
aptos-move-testing
by raintree-technologyExpert on testing Move smart contracts on Aptos, including unit tests, integration tests, Move Prover formal verification, debugging strategies, and test coverage. Triggers on keywords move test, unit test, integration test, move prover, formal verification, debug, coverage, assert, expect
supabase-expert
by raintree-technologySupabase integration expert — Postgres schema design with RLS policies, Auth (email/OAuth/magic link, JWT claims), Realtime (Postgres changes, Broadcast, Presence), Storage (buckets + RLS), Edge Functions (Deno runtime, secrets, scheduled jobs), pgvector for embeddings, and client libraries (`@supabase/supabase-js`, SSR auth helpers for Next.js). Invoke when user mentions Supabase, PostgreSQL + RLS, Supabase Auth, realtime subscriptions, Edge Functions, or pgvector. Example queries — "write an RLS policy so users only see their own rows", "set up Google OAuth with Supabase Auth and SSR cookies", "subscribe to INSERTs on a table from a React component", "store embeddings in pgvector and do similarity search".
stripe-expert
by raintree-technologyDeep Stripe integration expert — Checkout, Payment Intents, Elements, subscriptions (including metered and tiered), Connect (Express/Standard/Custom + marketplace flows), Terminal, Radar, Identity, Tax, Issuing, Treasury, Climate, webhooks (signature verification, idempotency keys, retries). Invoke when user mentions Stripe, payments, subscriptions, checkout, webhooks, Connect/marketplace, refunds, invoices, or 3DS. Example queries — "accept a card payment with Payment Intents", "set up a metered subscription for usage-based billing", "build a marketplace where sellers onboard via Express", "verify a webhook signature and handle payment_intent.succeeded".
stripe-expert
by raintree-technologyComprehensive Stripe API expert with access to 3,253 official documentation files covering all payment processing, billing, subscriptions, webhooks, Connect, Terminal, Radar, Identity, Tax, Climate, and integrations. Invoke when user mentions Stripe, payments, subscriptions, billing, payment processing, checkout, invoices, or any payment-related features.
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.