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...
e2e-default
by gronxbRun a fixed OTA regression flow for `examples/v0.85.0` with `agent-device`. Use when the caller wants a built-in end-to-end scenario instead of defining one manually: deploy a known-good OTA bundle, verify a visible UI change plus the deployed `bundleId`, then deploy an intentionally crashing OTA bundle and verify rollback to the previous stable bundle with `RECOVERED` and `crashedBundleId` evidence.
e2e
by gronxbRun end-to-end OTA verification for `examples/v0.85.0` with `agent-device`. Use when validating iOS or Android release builds, deploying OTA bundles with `pnpm hot-updater deploy`, checking stable update application, reproducing rollback after a crash bundle, or reading bundle-store metadata and crash history for the v0.85.0 example app.
e2e-current-pr
by gronxbGenerate and execute PR-aware OTA E2E scenarios for `examples/v0.85.0` by diffing the checked-out branch against its PR base branch or default branch, inferring the affected runtime, rollout, and recovery surfaces, and then running the scenario through `../e2e`. Use when the caller wants current-branch OTA validation without hand-writing the scenario.
hot-updater
by gronxbUse when working with Hot Updater CLI setup, deployment, patch artifacts, bundle inventory/state, rollback, channels, code signing keys, database migration, diagnostics, or AI-assisted React Native OTA operations.
hot-updater-agent
by gronxbUse when an AI agent needs to run, monitor, diagnose, or iterate HotUpdater E2E jobs through the local `hot-updater-agent` dashboard CLI instead of running device flows directly.
agent-device
by gronxbAutomates interactions for iOS simulators/devices and Android emulators/devices. Use when navigating apps, taking snapshots/screenshots, tapping, typing, scrolling, or extracting UI info on mobile targets.
fix-ci
by gronxbRun a local pnpm monorepo CI loop, fix failures, and stop only when the full sequence is green. Use when Codex is asked to fix CI, make a repository green, or resolve failures from `pnpm -w build`, `pnpm -w test:type`, `pnpm -w lint`, `pnpm -w test`, or `pnpm -w test:integration`.
agent-cdp
by gronxbChrome DevTools Protocol CLI workflow for runtime, console, network, trace, memory, and JavaScript CPU profiling analysis. Use when Codex needs to inspect debuggable Chrome/Chromium tabs, Node processes started with --inspect, React Native/Metro/Hermes targets, or to combine agent-device UI driving with CDP evidence for performance, heat, re-render, network, memory, or runtime-state diagnosis.
deploy-ios
by gronxbDeploy the Modex/Codex Relay mobile app iOS OTA update with Hot Updater. Use when the user invokes $deploy-ios or asks to deploy the current iOS app version from apps/mobile.
hot-updater-qa-ship
by gronxbDeploy the current Modex iOS app version with Hot Updater for QA-only delivery. Use when the user invokes $hot-updater-qa-ship or asks to ship the current iOS OTA to one target cohort only, with rollout 0.
hot-updater
by gronxbUse when working with Hot Updater CLI setup, deployment, bundle inventory, bundle enable/disable, rollback, channels, database migration, diagnostics, or AI-assisted React Native OTA operations.
react-devtools
by gronxbReact DevTools CLI for AI agents. Use when the user asks you to debug a React or React Native app at runtime, inspect component props/state/hooks, diagnose render performance, profile re-renders, find slow components, or understand why something re-renders. Triggers include "why does this re-render", "inspect the component", "what props does X have", "profile the app", "find slow components", "debug the UI", "check component state", "the app feels slow", or any React runtime debugging task.
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.