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...
review-react
by daangnReact code review guidelines covering Rules of React, re-render optimization, rendering performance, and advanced patterns. Activates when writing, reviewing, or refactoring React components, hooks, or state management code.
create-component
by daangnEnd-to-end SEED component implementation guide for React Web, Lynx, and cross-platform component work. Starts by deciding the target platform, then clarifies requirements, makes architecture decisions, follows the platform/category-specific pattern, and runs verification. Use this whenever the user is adding a new component, changing behavior across component layers, extending a snippet, or touching component docs — even if they don't explicitly say "create component". Always invoke before touching rootage YAML, qvism or lynx-qvism recipes, react/lynx-react/lynx-react-headless packages, docs registry snippets, or component docs. Covers all 5 component categories and refuses to skip platform gating or requirements brainstorming.
snapshot-release
by daangnDrives the snapshot-release flow for the current branch's PR. Posts a `/snapshot` comment on the PR if one isn't already there (with confirmation), waits for the `Continuous Releases` workflow to finish, and reports the tarball URLs from the resulting `📦 Snapshot Release` comment. Use for both triggering and waiting — phrases like "trigger a snapshot", "ship a snapshot release", "post `/snapshot` for me", "let me know when snapshot is done", "wait for the snapshot and show me the tarballs", "did the snapshot finish?", "snapshot 해줘", "snapshot 완료되면 알려줘", "snapshot 결과 보여줘", "tarball 받아와".
migrate-component-docs-from-figma
by daangnWrite or rewrite SEED Design component guideline docs (MDX) by extracting content and images from Figma documentation layers. Use this skill whenever the user wants to create, update, or migrate component documentation from Figma into docs/content/docs/components/, provides a Figma node URL for documentation, or asks to write/update a component guideline page. Also triggers for "write the docs for X component", "create guideline from Figma", "update the MDX for [component]", "가이드라인 문서 작성", or "피그마에서 문서 가져오기".
seed-design
by daangnSEED Design 통합 가이드. 프로젝트 셋업, 컴포넌트 탐색/사용, 파운데이션(색상·타이포·스페이싱) 활용, 테마/스타일링, CLI 워크플로우(init/add/add-all/compat/docs/upgrade), 스니펫 버전 호환성, 업그레이드 진단까지 커버. SEED Design 관련 질문이면 이 스킬을 사용한다. 사용자가 "SEED 어떻게 써?", "컴포넌트 뭐 있어?", "색상 토큰 쓰는 법", "디자인시스템 셋업" 같은 질문을 하면 반드시 이 스킬을 로드한다.
changeset
by daangnbun changeset CLI가 감지한 변경 패키지를 기반으로 changeset 파일을 자동 생성합니다. 패키지별 bump를 사용자에게 확정받고, 한국어 유저향 메시지를 작성합니다.
deprecation
by daangnManage deprecation lifecycle for components, interfaces, and tokens with versioned notices, replacement guidance, migration docs, and removal tracking. Use when deprecating, migrating, or removing API/spec options.
dev-figma-v3-migration-plugin
by daangnDevelop and maintain the Figma V3 migration plugin, including metadata extraction, mapping updates, and type-safe property conversion. Use when updating V2 to V3 migration mappings in tools/figma-v3-migration.
graplix
by daangnRelation-Based Access Control (ReBAC) with the Graplix TypeScript toolkit. Use when defining .graplix schemas, building engines, writing resolvers, checking permissions, or explaining traversals with buildEngine, check, explain, resolveType, and the Resolver interface.
ventyd
by daangnEvent sourcing with the ventyd TypeScript library. Use when defining schemas, reducers, entities, mutations, repositories, adapters, or plugins with ventyd. Covers defineSchema, defineReducer, Entity, mutation, createRepository, and the Adapter/Plugin interfaces.
lynx-trace-analysis
by daangnSpecializes in analyzing Lynx trace data to diagnose performance issues and provide actionable optimization strategies. Key Scenarios: - Loading Performance: Diagnosing slow startup metrics (FCP, FMP, TTI) and white screen issues. - Smoothness Analysis: Investigating root causes for scroll jank, frame drops, and interaction lag. - Regression Detection: Comparing traces to identify performance degradation or verify optimization gains between versions. - Pipeline Deep Dive: Pinpointing bottlenecks in specific rendering stages like Layout, Paint, JS execution, and background threads. - Native Module Analysis: Investigating performance issues related to native module calls.
lynx-devtool
by daangnInteract with Lynx DevTool to inspect and debug Lynx applications. Use this skill to list connected clients and sessions, send Chrome DevTools Protocol (CDP) commands, send App commands, and open URLs in Lynx. This is useful for debugging UI issues, inspecting runtime state, or automating interactions with Lynx apps.
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.