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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react-admin-i18n
by marmelabGuidelines for internationalizing a react-admin or shadcn admin kit application. Use when adding i18n support, creating translation files, translating hardcoded English strings in JSX, or reviewing i18n PRs in a react-admin project. Covers namespace conventions, interpolation, pluralization, locale-aware formatting, and the full workflow for converting an English-only app to multi-language. Trigger this skill whenever the user mentions i18n, internationalization, translation, localization, locale switching, or multi-language in a react-admin or shadcn admin kit context — even if they just say "translate this component" or "add French support".
react-admin
by marmelabThis skill should be used when building, modifying, or debugging a react-admin application — including creating resources, lists, forms, data fetching, authentication, relationships between entities, custom pages, or any CRUD admin interface built with react-admin.
agent-team
by marmelabMulti-agent team workflow for implementing tickets with peer-to-peer communication inside a single shared team. Used by chat-orchestrator for COMPLEX requests only (planner → wave → teardown). Single source of truth for cross-agent messaging.
shadcn-customization
by marmelabShadcn/ui theming and component customization — CSS variables, OKLCH colors, dark mode, variants, wrappers. Load for any ticket involving colors, theme, UI layout, or component styling.
e2e-conventions
by marmelabWhen to write e2e tests, where to put them, and how to verify them. Apply to any task touching UI, filters, forms, or interactions.
update-branding
by marmelabRebrand Atomic CRM — change the application logo (the wordmark in the header and on the login/signup pages) and/or the title/name. Use when the user wants to change, swap, update, or rebrand the CRM logo or title. Handles the two light/dark-mode logo variants, the three places the title is hardcoded, the config that points at them, and — optionally — the browser favicon and PWA app icons.
frontend-dev
by marmelabCoding practices for frontend development in Atomic CRM. Use when creating or modifying React components, forms, list pages, detail views, filters, data fetching, or responsive layouts.
playwright-testing
by marmelabPlaywright E2E testing patterns — web-first assertions, user-visible locators, network interception, fixtures, authentication, and parallel execution. Use when building or reviewing E2E tests with Playwright, when setting up browser testing for a web app, or when migrating from Cypress or Selenium.
setup-interview
by marmelabDomain-by-domain interview to produce $CLAUDE_PROJECT_DIR/docs/project-context.json. Invoked once by the orchestrator; the orchestrator then conducts all turns directly using Read/Write/Edit — no agent dispatching.
delete-initial-resource
by marmelabRemove one or more of the initial CRM resources (contacts, companies, deals, tags, tasks) from the codebase. Use when the user asks to delete, remove, or strip out one or several of these built-in resources. Runs the delete-initial-resource.ts script to drop each resource's own folder, then guides cleanup of every file that references them.
writing-migrations
by marmelabGenerate Supabase SQL migrations at deploy time from the session branch diff. Used by simple-developer in the deploy-time migration round only.
backend-dev
by marmelabCoding practices for backend development in Atomic CRM. Use when deciding whether backend logic is needed, or when creating/modifying database migrations, views, triggers, RLS policies, edge functions, or custom dataProvider methods that call Supabase APIs.
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.