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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fix-sentry
by polarsourceAnalyze and fix issues reported by Sentry in the Polar codebase.
handbook-backend-development
by polarsourceA skill to add a new entry in the section "Backend Development" of the Polar Handbook. Those entries are there to explain concepts, tooling and best practices related to backend development, and are meant to be read by Polar developers.
interview-task
by polarsourcePrepare an interview task for a candidate, as part of our hiring process.
vercel-react-best-practices
by polarsourceReact and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
render-env
by polarsourceAdd a new Terraform Cloud variable for the Render-hosted backend across production, sandbox, and test. Declares the tfe_variable in terraform/global/{production,sandbox,test}.tf and the matching variable {} block in terraform/{production,sandbox,test}/variables.tf, then reminds the user to wire it into render.tf / the render_service module.
local-environment
by polarsourceLocal development environment management for Polar using Docker
add-locale
by polarsourceAdd a new translation locale to the Polar frontend. Walks through registering the locale, generating translations, wiring the generated file into the i18n package, and updating the public-facing docs. Expects the target locale's ISO 639-1 alpha-2 code (optionally with a BCP-47 region suffix, e.g. `pt-PT`).
polar-integration
by polarsourceAdd Polar billing to a TypeScript/JavaScript app using the @polar-sh/sdk package. Use this skill whenever the user wants to add a Checkout endpoint, a Customer Portal endpoint, or a Webhooks endpoint for Polar to any framework — Next.js, Express, Hono, Astro, SvelteKit, Remix, TanStack Start, Nuxt, Fastify, Elysia, Deno, Supabase Edge Functions, Cloudflare Workers, Bun, etc.
polar-migration
by polarsourceGuide for migrating to Polar from other payment platforms (Stripe Billing, Paddle, Lemon Squeezy, Gumroad). Use this skill when: (1) Planning a migration from another payment provider to Polar; (2) Migrating customer data and subscriptions; (3) Running parallel systems during transition; (4) Mapping products and pricing between platforms; (5) Handling payment method migration; (6) Communicating migration to customers; (7) Testing migration in sandbox before production.
polar-testing
by polarsourceGuide for testing Polar payment integrations using the sandbox environment. Use this skill when: (1) Setting up the Polar sandbox for development; (2) Testing checkout flows without real payments; (3) Using Stripe test cards with Polar; (4) Writing integration tests for payment flows; (5) Testing webhooks locally with ngrok; (6) Mocking Polar in unit tests; (7) Setting up CI/CD pipelines with Polar sandbox; (8) Debugging payment issues in sandbox.
setup-polar
by polarsourceInteractive onboarding wizard to set up Polar payment integration from scratch. Use this skill when: (1) User wants to "set up Polar" or "integrate Polar" in their project; (2) User is starting fresh with Polar and needs guided setup; (3) User asks "how do I get started with Polar"; (4) User wants to add payments/subscriptions/checkout to their app using Polar; (5) User needs help creating their first Polar product and checkout. This skill walks through MCP installation, authentication, product creation, and generates framework-specific integration code.
python-api-sdk-group
by polarsourceCreate or update a group of endpoints in the Python API SDK. Use this skill when the user asks to generate or modify a set of related API endpoints in the Python SDK, based on the OpenAPI schema.
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.