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...
backend-system
by joo6077프로젝트에 백엔드 아키텍처 기반(에러 처리 패턴, 인증 구조, API 규격 등)을 세팅한다. 기존 아키텍처가 있으면 리서치 기준과 비교하여 개선점을 제안한다. 스택 무관 — 원칙만 정의하고, 구체적 코드 생성은 프로젝트 스택에 맞게 적용. "백엔드 아키텍처 세팅", "API 규격 정하자", "에러 핸들링 패턴 세팅", "backend system init" 같은 요청 시 트리거. 단순 API 추가, 기존 패턴 내 코드 작성에는 트리거하지 않는다.
design-system
by joo6077프로젝트에 디자인 토큰 체계(컬러, 타이포, 스페이싱, 라디우스 등)를 세팅한다. 기존 디자인 시스템이 있으면 리서치 기준과 비교하여 개선점을 제안한다. 스택 무관 — 원칙만 정의하고, 구체적 코드 생성은 해당 toolkit에 위임한다. "디자인 시스템 세팅", "디자인 토큰", "컬러 팔레트 만들어줘", "design system init", "토큰 체계" 같은 요청 시 트리거. 단순 색상 변경, 기존 토큰 값 수정에는 트리거하지 않는다.
plan-ideate
by joo6077제품 기획의 0단계 — 막연한 생각 덩어리를 발산(divergent) · 정리(organize) · 수렴(convergent)하여 plan-discover 로 넘길 수 있는 수준으로 만든다. How-Might-We, Crazy 8s, SCAMPER, Brainwriting 으로 발산하고, Affinity Diagram · Mermaid mindmap 으로 정리, Dot Voting · Impact-Effort Matrix 로 수렴한다. "아이디어 정리", "brainstorm", "ideation", "발산", "수렴", "마인드맵", "How Might We", "HMW", "Crazy 8s", "아이디어 발전", "생각 정리", "아이디어 덩어리" 같은 요청 시 트리거. 이미 문제/사용자가 명확하면 plan-discover 사용. 구현 단계는 건드리지 않는다.
react-l10n
by joo6077Lingui v5 매크로 기반으로 번역 문자열을 추가하고 codegen 흐름을 자동화한다. Trans 컴포넌트, t 매크로, Plural 컴포넌트 사용 패턴 안내 및 locale 전환 패턴 세팅. "번역 추가", "i18n 키", "다국어 지원", "Lingui", "l10n", "locale 전환" 같은 요청 시 트리거. 하드코딩된 문자열을 매크로로 교체할 때도 트리거한다.
rust-l10n
by joo6077Rust 백엔드 프로젝트에 i18n을 설정하거나 번역 키를 추가/수정한다. rust-i18n 또는 fluent 기반으로 로케일 파일을 관리하고 Accept-Language 미들웨어를 구성한다. "다국어", "번역", "i18n", "l10n", "국제화", "rust l10n" 같은 요청 시 사용한다.
flutter-l10n
by joo6077i18n 파일에 번역 문자열을 추가/수정하고 codegen을 재생성한다. slang, easy_localization, intl(ARB), flutter_localizations 등 프로젝트의 i18n 라이브러리를 자동 감지하여 적용한다. 다국어, 번역, 로컬라이제이션, localization, 문자열 추가, i18n 키, "번역 추가해줘", "다국어 지원", "l10n", "i18n key" 같은 요청 시 사용한다.
plan-data-model
by joo6077기획 단계에서 개념 데이터 모델을 Mermaid erDiagram + classDiagram 으로 작성한다. DDD 관점의 Bounded Context, Aggregate, Entity, Value Object, Domain Event 를 식별하고 Event Storming 산출물과 Data Dictionary 까지 한 벌로 정리. 구현 레벨 스키마가 아닌 **개념 모델** — SQL 컬럼 타입이나 인덱스는 다루지 않는다. "데이터 모델", "ERD", "도메인 모델", "data model", "entity relationship", "도메인 이벤트", "bounded context", "event storming", "aggregate" 같은 요청 시 트리거. 실제 DB 스키마는 rust-model, backend-system 등 구현 kit 사용.
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.