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...
cms
by ydj515Headless CMS integration guidance — Sanity (native Vercel Marketplace), Contentful, DatoCMS, Storyblok, and Builder.io. Covers studio setup, content modeling, preview mode, revalidation webhooks, and Visual Editing. Use when building content-driven sites with a headless CMS on Vercel.
v0-dev
by ydj515v0 by Vercel expert guidance. Use when discussing AI code generation, generating UI components from prompts, v0 CLI usage, v0 SDK/API integration, or integrating v0 into development workflows with GitHub and Vercel deployment.
vercel-services
by ydj515Vercel Services — deploy multiple services within a single Vercel project. Use for monorepo layouts or when combining a backend (Python, Go) with a frontend (Next.js, Vite) in one deployment.
a11y-audit-guide
by ydj515Use when auditing one or more web URLs with Playwright and axe-core, producing JSON and Markdown accessibility reports for rendered pages across React, Next.js, Vue, Angular, Spring/Thymeleaf, or static HTML.
mise-profiles
by ydj515Choose, review, or refactor project profiles that map mise tools to ecosystem-specific reference sets. Use when Codex needs to classify a repository as a Python, Java, Gradle, uv, or Spring workflow and decide which root references and selector rules should apply.
mise-tools
by ydj515Author, review, or refactor a project's mise `[tools]` section, tool selectors, backends, and lock strategy. Use when Codex needs to create or update tool declarations in `mise.toml`, choose between core tools and registry backends, or normalize version selectors before ecosystem-specific reference rules apply.
generate-karate-from-openapi
by ydj515OpenAPI/Swagger YAML 문서만을 기준으로 이 저장소의 Karate feature 테스트를 생성하거나 업데이트하는 스킬이다. 사용자가 `docs/openapi/mock-rest-api-server/*.yaml`, Swagger 기반 Karate 생성, gateway action path용 feature, 단일 API 테스트, 시나리오 기반 Karate 흐름, negative case 보강, org/service/api 추론, response-to-request 체인 탐색 중 하나라도 언급하면 이 스킬을 우선 사용한다. mock 구현 코드나 seed 데이터에서 검증 규칙을 추론하지 않고 Swagger와 현재 REST API 스펙만 source of truth로 유지한다.
mise-tasks
by ydj515Design, review, or refactor mise task definitions, task arguments, file tasks, and monorepo task composition. Use when Codex needs to create or update `[tasks]` entries, convert shell scripts into structured mise tasks, or validate task argument and dependency patterns.
ai-generation-persistence
by ydj515AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation
nextjs
by ydj515Next.js App Router expert guidance. Use when building, debugging, or architecting Next.js applications — routing, Server Components, Server Actions, Cache Components, layouts, middleware/proxy, data fetching, rendering strategies, and deployment on Vercel.
shadcn
by ydj515shadcn/ui expert guidance — CLI, component installation, composition patterns, custom registries, theming, Tailwind CSS integration, and high-quality interface design. Use when initializing shadcn, adding components, composing product UI, building custom registries, configuring themes, or troubleshooting component issues.
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.