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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nitro-fetch
by margeloUse this skill whenever an agent is working in a project that uses react-native-nitro-fetch, react-native-nitro-websockets, or react-native-nitro-text-decoder. Covers the fetch API, global replacement, prefetching and cold-start cache warming, the NitroWebSocket class and pre-warming, migrating from React Native's built-in WebSocket, the in-process NetworkInspector, native Perfetto / Instruments tracing, the native TextDecoder, and plugging nitro-fetch into axios via a custom adapter.
react-native-runtimes
by margeloInstall, configure, and write code with the @react-native-runtimes/core and @react-native-runtimes/state packages — named secondary React Native (Hermes) runtimes for rendering components, scheduling awaitable functions, and running headless background work, plus the C++-backed shared Zustand store. Use whenever the user mentions threaded runtimes, secondary runtimes, OnRuntime, ThreadedScreen, threadedComponent, runtimeFunction, the `'background'`/`'main'` function directives, ThreadedRuntime.prewarm/runHeadlessTask, the `.threaded-runtime/entry.js` Metro generated file, createSharedStore / store.path, or wants to move long lists, chat screens, or sync engines off the main JS thread. Also use when migrating from react-native-worklets-core, react-native-multithreading, or raw JSI worklets.
react-native-vision-camera
by margeloBest-practices guide for react-native-vision-camera v5 (Nitro rewrite, April 2026) and migrating from v4. Use when installing, configuring, or writing code with the Camera, outputs, frame processors, recording, or barcode/depth/RAW features. Also use when converting v4 code (photo={true}, takePhoto, useCameraFormat, useFrameProcessor) to the new v5 API.
nitro-fetch
by margeloFast native networking primitives for React Native built on Nitro Modules — react-native-nitro-fetch, react-native-nitro-websockets, and react-native-nitro-text-decoder. Covers the fetch API, global replacement, prefetching and cold-start cache warming, the NitroWebSocket class and pre-warming, migrating from React Native's built-in WebSocket, the in-process NetworkInspector, native Perfetto / Instruments tracing, the native TextDecoder, and plugging nitro-fetch into axios via a custom adapter.
react-native-mmkv
by margeloFast synchronous key-value storage for React Native via react-native-mmkv (Nitro-backed). Covers creating and configuring MMKV instances, reading/writing all value types (string, number, boolean, buffer), React hooks for reactive UI, value-change listeners, encryption, AsyncStorage migration, and state-management integrations (zustand, redux-persist, jotai, react-query). Also covers storage limits, multi-process mode, and the V4 upgrade from the class-based API.
cpp
by margeloDesign, implement, and review modern C++ APIs and native implementation code. Use when working on .cpp, .hpp, CMake, RAII ownership, std::variant, callbacks, async work, platform bridges, or C++-backed React Native Nitro Module implementations.
build-nitro-modules
by margeloBuilds and designs React Native Nitro Modules with Nitrogen, HybridObject TypeScript specs, generated native implementations, zero-copy and native-state APIs, Swift/Kotlin/C++ bindings, example apps, and testing. Use when creating a Nitro Module, adding or reviewing HybridObjects, designing Nitro-specific public APIs, implementing native functionality, or setting up the nitrogen codegen pipeline. Pair with api-design for general library API shape.
api-design
by margeloDesign and review predictable public APIs for TypeScript, JavaScript, React, and React Native libraries. Use when shaping exported functions, classes, hooks, options objects, event and listener APIs, error behavior, naming, cross-platform abstractions, or JS-only packages. Pair with build-nitro-modules when the library is backed by Nitro.
kotlin
by margeloDesign, implement, and review Kotlin and Android APIs. Use when working on .kt files, Kotlin nullability, sealed classes/interfaces, coroutines, Android threading, Java interop, or Kotlin-backed React Native Nitro Module implementations.
swift
by margeloDesign, implement, and review Swift APIs and Apple-platform code. Use when working on .swift files, Swift types, Foundation or AVFoundation APIs, DispatchQueue, async/await, Task, actors, MainActor, thread-affine state, or Swift-backed React Native Nitro Module implementations.
coding-standards
by margeloProvides coding standards for React Native — performance patterns, consistency rules, and clean React architecture. Use when writing, modifying, or reviewing code.
pr-readiness-check
by margeloCheck branch changes for common PR readiness issues (missing tests, missing JSDoc, guideline violations). Use when the user asks to verify changes before opening a PR, check code quality, or audit a branch for missing items.
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.