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...
ipc-classification-guide
by orientpineIPC/CPC 국제특허분류 체계 가이드. 특허 검색 시 IPC 코드 구조 이해, 기술 분야별 코드 탐색, 검색 전략 수립에 활용. Use when: (1) IPC 코드의 의미나 구조를 파악할 때, (2) 특정 기술 분야의 IPC 코드를 찾을 때, (3) 특허 분류 체계 설계 시 IPC 코드 매핑이 필요할 때, (4) 'IPC code', 'patent classification', '특허 분류', 'CPC code' 관련 질문 시.
learning-methodology
by orientpine48시간 가속 학습 방법론 — 멘탈모델 추출, 논쟁 매핑, 판별 질문 생성, 소크라틱 튜터링의 교육학적 원칙과 실행 지침. Use when 학습 자료 분석, 지식 구조화, 튜터링 세션 수행 시.
hwpx-templates
by orientpineHWPX 템플릿 기반 문서 생성 및 ZIP-level 치환 워크플로우를 제공한다. Use when 사용자 업로드 양식 또는 기본 양식을 기반으로 보고서/공문서를 빠르게 생성해야 할 때.
link-summarizer
by orientpineFetch URLs and generate structured Korean markdown summary notes. Use WHENEVER the user provides one or more URLs — including bare URL lists with minimal instruction like '이 링크들 정리해줘', '아래 url 정리', 'md로 저장해줘' — or any message whose primary content is HTTP(S) URLs. Also triggers on requests containing 'Resource에 넣어줘', '기사 정리', or similar content curation phrases. This skill handles ONLY note generation, not HoneyCombo submission.
chapter3-guide
by orientpineChapter 3 (사업 목표 및 추진 전략) 작성 가이드 - 템플릿 및 요구사항 통합
chapter5-guide
by orientpineChapter 5 (기타 참고자료) 작성 가이드 - 템플릿 및 요구사항 통합
wiki-gen
by orientpineCompile personal data (journals, notes, messages, whatever) into a personal knowledge wiki. Ingest any data format (Day One JSON, Apple Notes, Obsidian, Notion, plain text, iMessage, CSV, email, Twitter), absorb entries into wiki articles, query the wiki, cleanup articles, and expand coverage. Use when the user wants to build a personal knowledge base from raw journal/diary/notes data, compile entries into Wikipedia-style articles, or query their personal history. Activates on mentions of "personal wiki", "knowledge wiki", "wiki-gen", "compile my journal", "ingest my notes", or sub-commands like "wiki ingest", "wiki absorb", "wiki query", "wiki cleanup", "wiki breakdown", "wiki status".
data-collection-guide
by orientpineChapter 2 데이터 수집 품질 기준 및 검증 방법
hwpx-core
by orientpineHWPX XML-first document authoring skill for create/edit/read/validate workflows and template-driven generation. Use when you need deterministic Korean document layout control via section0.xml/header.xml and script-based build pipelines instead of opaque editors.
pptx-design-styles
by orientpineUse this skill whenever creating PPTX slides, presentations, or decks with any of these 30 modern design styles: Glassmorphism, Neo-Brutalism, Bento Grid, Dark Academia, Gradient Mesh, Claymorphism, Swiss International, Aurora Neon Glow, Retro Y2K, Nordic Minimalism, Typographic Bold, Duotone Color Split, Monochrome Minimal, Cyberpunk Outline, Editorial Magazine, Pastel Soft UI, Dark Neon Miami, Hand-crafted Organic, Isometric 3D Flat, Vaporwave, Art Deco Luxe, Brutalist Newspaper, Stained Glass Mosaic, Liquid Blob Morphing, Memphis Pop Pattern, Dark Forest Nature, Architectural Blueprint, Maximalist Collage, SciFi Holographic Data, Risograph Print. Also activate for requests using words like "sleek", "modern", "trendy", "designed", "stylish", or "visually striking" presentations.
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.