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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harness-lab
by jikime하네스 엔지니어링 실습·설계·생성 스킬. 사용자가 일상 업무, 학습 과제, 문서 작업, 콘텐츠 제작, 리포트·계획서·체크리스트·HTML 시각 리포트 같은 결과물을 반복 가능하게 만들고 싶어 할 때, 그 일을 Agent, Skill, Orchestrator, Test, Evolution 구조로 바꿔준다. "하네스 만들어줘", "에이전트와 스킬 설계해줘", "내 업무를 멋진 산출물로 만들어줘", "기존 하네스 점검/개선해줘", "하네스 성숙도 진단해줘", "내 업무를 하네스 설계 카드로 바꿔줘" 같은 요청에서 반드시 사용한다. 단순한 일반 설명이나 하네스와 무관한 코딩 질문에는 사용하지 않는다.
harness-lab
by jikimeCodex용 하네스 엔지니어링 실습·설계·생성 스킬. 사용자가 일상 업무, 학습 과제, 문서 작업, 콘텐츠 제작, 리포트·계획서·체크리스트·HTML 시각 리포트 같은 결과물을 반복 가능하게 만들고 싶어 할 때, 그 일을 Agent, Skill, Orchestrator, Test, Evolution 구조로 바꿔준다. "하네스 만들어줘", "에이전트와 스킬 설계해줘", "내 업무를 멋진 산출물로 만들어줘", "Codex용 AGENTS.md와 .agents/skills, .codex/agents 구성해줘", "기존 하네스 점검/개선해줘", "하네스 성숙도 진단해줘", "내 업무를 하네스 설계 카드로 바꿔줘" 같은 요청에서 반드시 사용한다. 실행 하네스 생성 시에는 반드시 Codex의 AGENTS.md, AGENTS.override.md, .agents/skills, .codex/agents 경로를 기준으로 한다. 단순한 일반 설명, 하네스와 무관한 코딩 질문, Claude Code 전용 .claude 파일 생성에는 사용하지 않는다.
revfactory-harness
by jikime하네스를 구성합니다. 전문 에이전트를 정의하며, 해당 에이전트가 사용할 스킬을 생성하는 메타 스킬. (1) '하네스 구성해줘', '하네스 구축해줘' 요청 시, (2) '하네스 설계', '하네스 엔지니어링' 요청 시, (3) 새로운 도메인/프로젝트에 대한 하네스 기반 자동화 체계를 구축할 때, (4) 하네스 구성을 재구성하거나 확장할 때 사용.
jikime-lang-java
by jikimeJava 21 LTS development specialist covering Spring Boot 3.3, virtual threads, pattern matching, and enterprise patterns. Use when building enterprise applications, microservices, or Spring projects.
jikime-migration-jquery-to-react
by jikimejQuery to React 19 migration specialist. Converts legacy jQuery applications to modern React with hooks, TypeScript, and component-based architecture. Use when migrating jQuery projects, converting $() patterns to React, or asking about jQuery to React conversion.
jikime-lang-flutter
by jikimeFlutter 3.24+ / Dart 3.5+ development specialist covering Riverpod, go_router, and cross-platform patterns. Use when building cross-platform mobile apps, desktop apps, or web applications with Flutter.
naver-search
by jikimeSearch Korean web content via Naver Open API — news, shopping, blogs, books, local places, Q&A, encyclopedia, and more. Use when the user explicitly requests Naver or needs Korea-specific information (local restaurants, Korean news, Korean shopping prices, etc.).
python3-finance-scripts-finance-py
by jikimeRecord and query income/expense transactions with optional location. Use for: 가계부, 지출, 수입, 얼마 썼어, 이번 달 지출, 카드값, 식비, expense logging, spending history, 어디서 썼어, 영수증, receipt OCR, 주소, 이미지
connect-activity
by jikimeRecord and sync Connect (인맥) activity timeline entries. Pulls from Google Calendar + Gmail (shares google-workspace OAuth token), or accepts manual entries like '김철수와 어제 점심 먹었어'. Use for: 인맥 활동, 활동 기록, 만남 기록, 미팅 기록, 점심, 저녁, 통화 기록, 일정 동기화, 인맥 동기화, connect sync, connect activity, meeting log, met with, had lunch with
connect-memo
by jikimeAdd or replace Context Memo (context_notes) for a Connect (인맥) entry. Looks up the target connection by exact or fuzzy name when the agent doesn't already know the connection UUID. Use for: 인맥 메모, 인맥 메모 추가, 메모 추가, 메모 수정, 인물 메모, 사람 메모, connect memo, add note, update note, context note
connect-ocr
by jikimeScan a business card image with Gemini Vision and extract structured contact fields (name, role, company, email, phone, address, etc.) to create a new Connect (인맥) entry. Use for: 명함, 명함 스캔, 명함 등록, 명함 인식, 명함 저장, 명함 추가, 인맥, 인맥 추가, 인맥 등록, 연락처 등록, 연락처 추가, 비즈니스 카드, business card, business card ocr, scan business card, contact card, register contact
python3-planner-reflection-scripts-planner-reflection-py
by jikimeRecord notes in the Planner's '노트' section. Use for: 노트, 노트에 기록, 메모, 메모해줘, 노트 저장, 노트 추가, note, memo
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.