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...
dz-matrix-agent
by cjrain-12505614"IA 설계" · "MATRIX IA 설계" · "정보 구조 설계" · "GNB/LNB 매핑" · "메뉴 계층 설계" 트리거. 요구사항을 기반으로 정보 구조 (IA) · 메뉴 계층 · 화면 분류를 설계한다. Forge 14 업무별 서브에이전트 중 IA 설계 영역 담당.
dz-matrix-agent
by cjrain-12505614"IA 설계" · "MATRIX IA 설계" · "정보 구조 설계" · "GNB/LNB 매핑" · "메뉴 계층 설계" 트리거. 요구사항을 기반으로 정보 구조 (IA) · 메뉴 계층 · 화면 분류를 설계한다. Forge 14 업무별 서브에이전트 중 IA 설계 영역 담당.
a10-matrix-agent
by cjrain-12505614"IA 설계" · "MATRIX IA 설계" · "정보 구조 설계" · "GNB/LNB 매핑" · "메뉴 계층 설계" 트리거. 요구사항을 기반으로 정보 구조 (IA) · 메뉴 계층 · 화면 분류를 설계한다. Forge 14 업무별 서브에이전트 중 IA 설계 영역 담당.
kf-timeline
by cjrain-12505614This skill should be used when the user says "타임라인 보여줘", "기업 히스토리", "timeline", "OpenAI 타임라인", "Claude 변천사", "기업별 연표", "제품 히스토리 정리해줘", "특정 기업 추적", or when viewing or managing company/product timelines. Manages auto-cumulative timelines per company and product, updated on every collection.
kf-status
by cjrain-12505614This skill should be used when the user says "아카이브 현황", "status", "지금까지 몇 건 수집했어", "통계 보여줘", "아카이브 대시보드", or when an overview of the archive's cumulative status is needed. Shows archive statistics including total entries, category distribution, and collection history.
kf-session
by cjrain-12505614This skill should be used when the user says "세션 시작", "세션 종료", "이어서 하자", "어디까지 했지", "세션 저장", "오늘 여기까지", or when session lifecycle management is needed for the knowledge archive project. Manages session start/end protocols, progress tracking, and lessons learned.
kf-search
by cjrain-12505614This skill should be used when the user says "아카이브 검색", "이전에 수집한 거 찾아줘", "search", "키워드 검색", "Claude 관련 기사 모아줘", "지난주 뉴스 찾아줘", or when searching across the accumulated archive for specific topics, companies, or keywords. Searches the entire knowledge archive by keyword, company, date range, or category.
kf-report
by cjrain-12505614This skill should be used when the user says "월간 리포트 만들어줘", "분기 보고서", "report", "이번 달 정리", "3월 트렌드", "Q1 리포트", "월간 요약", "분기별 트렌드 분석", "지난달 AI 동향", or when generating periodic (weekly/monthly/quarterly) trend reports. Aggregates collected data over a period and produces structured trend analysis reports.
kf-publish
by cjrain-12505614This skill should be used when the user says "SNS에 올려줘", "게시해줘", "publish", "트위터 올려줘", "인스타 올려줘", "페북 올려줘", "kf-publish", "콘텐츠 게시해줘", "오늘 콘텐츠 올려줘", "SNS 자동 게시", "카드뉴스 올려줘", or when kf-content has been created and the user wants to post it to social media. Uses Claude in Chrome to post card news images to X, Instagram, and Facebook — no API keys needed. Always use this skill after kf-content or kf-daily when the user asks to publish, post, or share.
kf-link
by cjrain-12505614This skill should be used when the user says "정보 연결해줘", "관계 분석해줘", "link", "수집된 거 연결", "뉴스랑 논문 연결", "트렌드 분석", or when collected entries need cross-referencing and relationship analysis. Analyzes relationships between collected entries, tags connections, and identifies emerging trends.
kf-daily
by cjrain-12505614This skill should be used when the user says "데일리 실행", "daily", "오늘 파이프라인 돌려", "전체 실행", "수집부터 콘텐츠까지", or when the full daily pipeline needs to run (collect → link → briefing → content). Orchestrates the complete daily intelligence pipeline in sequence.
kf-content
by cjrain-12505614This skill should be used when the user says "콘텐츠 만들어줘", "SNS 글 작성해줘", "content", "페이스북 포스트", "X 트윗", "인스타 카드뉴스", "소셜미디어 콘텐츠", "공유할 글 만들어줘", or when SNS content needs to be created from the daily briefing. Creates platform-specific content for X (Twitter), Facebook, and Instagram from briefing data.
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.