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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guardrails
by Yoodaddy0311Input/output and per-tool guardrails with tripwire semantics. Use when an agent processes untrusted input, calls a sensitive tool, or must short-circuit on policy violations.
brand-guidelines
by Yoodaddy0311Manages brand identity including voice guidelines, visual standards, messaging consistency, and brand governance with frameworks for style guides and cross-channel consistency. Use when user asks about brand guidelines, brand voice, style guide, brand identity, tone of voice, messaging framework, visual identity, 브랜드 가이드라인, 브랜드 보이스, or 스타일 가이드.
lifelong-learning
by Yoodaddy0311Continuous learning pipeline that captures session experiences, performs batch learning via GRPO, and transfers validated knowledge between System 1 and System 2 caches. Auto-activates when: session end, pattern discovery during routing, knowledge transfer triggers, manual /learn command. Triggers: learn, experience, knowledge, transfer, promote, demote, grpo, batch, pattern, skill development, growth, continuous improvement
persona-scribe
by Yoodaddy0311Professional documentation and localization decision framework for technical writing, API docs, and multilingual content. Use when user creates documentation, writes guides or READMEs, drafts changelogs or PR descriptions, needs localization, or mentions 문서, 작성, or 가이드.
column-editorial
by Yoodaddy0311Writes argumentative columns, editorials, and op-eds with contrarian thesis, steelmanned counter-arguments, and layered evidence. Use when user asks about column writing, op-ed, editorial, opinion piece, thought piece, 칼럼, 오피니언, 사설, 논평, or 기고문.
interview-storytelling
by Yoodaddy0311Structures long-form interview storytelling for expert interviews, internal feature stories, and multi-expert roundups. Applies 5W1H question matrix, 3-part answer arc, verbal signposting, NNGroup 4-dimension voice profile, and quote-approval workflow. Use when user asks about interview article, expert interview, feature story, storytelling, quote roundup, 인터뷰 기사, 전문가 인터뷰, 스토리텔링, 인용 모음, or 피처 스토리.
persona-mentor
by Yoodaddy0311Educational and knowledge transfer decision framework for explanations, tutorials, and learning guidance. Use when user asks to explain, learn, understand, or teach concepts, requests step-by-step guidance, asks how or why something works, or mentions 설명, 배우기, or 이해.
lang-reference
by Yoodaddy0311프로그래밍 언어 패턴 참조 허브 — C++, C#, Elixir, Flutter/Dart, Go, Java, JavaScript, Kotlin, PHP, Python, R, Ruby, Rust, Scala, Swift, TypeScript 16개 언어의 모던 패턴·프레임워크 베스트 프랙티스·테스트 이디엄·안티패턴을 통합 제공. Auto-activates when: 위 언어의 소스 파일을 다루거나, 해당 언어의 프레임워크 이름(FastAPI, Spring Boot, Phoenix, Laravel, Rails, SwiftUI, Axum, Gin, Echo, Fiber, Ktor, Vapor, Next.js, Remix, Astro, Flutter, Django, Shiny, Cats Effect, ZIO, Akka, Spark, Ktor, Blazor, Quarkus, Compose 등)이 언급되거나, 빌드 도구(CMake, Cargo, Maven, Gradle, SBT, Composer, NuGet, uv, poetry, Bundler, Mix, pubspec, Bun, Deno)·타입 시스템·동시성 패턴 질문이 감지될 때. Triggers: c++, C++, cpp, .cpp, .hpp, .h, cmake, smart pointer, RAII, concepts, ranges, constexpr, move semantics, csharp, C#, .cs, .csproj, dotnet, .NET, asp.net, blazor, entity framework, LINQ, minimal api, pattern matching, nuget, elixir, Elixir, .ex, .exs, phoenix, liveview, ecto, genserver, otp, mix, supervisor, erlang, beam, flutter, Flutter, dart, Dart, .dart, riverpod, go_router, material 3, widget, pubspec, freezed, bloc, cross-pla
oss-ai-catalog
by Yoodaddy0311Curated open-source AI catalog reference. Auto-activates when llm-architect, content-marketer, data-analyst, mcp-developer, backend-developer, or any agent needs to RECOMMEND an open-source AI tool, model, framework, inference engine, vector DB, or agent/RAG library to the user. Provides category-indexed lookup across 14 domains (Deep learning frameworks, Foundation models, Inference engines, Agentic AI, RAG/Vector DBs, Generative media, Training/Fine-tuning, MLOps, Evaluation, Safety, Specialized domains, UIs, Dev tools, Learning resources). MUST trigger when user says any of: - "어떤 LLM 써", "어떤 모델 써", "오픈소스 AI 추천", "OSS 추천" - "vector DB 뭐가 좋아", "RAG framework", "inference engine 추천" - "음성 모델 / TTS / STT 추천", "이미지 생성 모델 추천", "영상 생성 모델" - "LangChain 말고 다른 거", "vLLM 같은 거" - any "what tool should I use for X" question in an AI/ML context DO NOT hardcode recommendations in agent prompts; consult this skill so recommendations stay fresh.
platform-database-cloud
by Yoodaddy0311Provides cloud database patterns for serverless PostgreSQL, real-time databases, and edge-compatible data access (Neon, Supabase, Firebase, PlanetScale). 자연어 트리거: '클라우드 DB 골라줘', 'Supabase 연동해줘', '서버리스 데이터베이스 설정', '커넥션 풀링 구성'.
kr-marketing
by Yoodaddy0311Korean digital marketing specialist for Naver, Kakao, Coupang, and local platform strategies. Covers Naver SEO (C-Rank/DIA), Kakao Moment ads, Korean content formats, PIPA compliance, and domestic consumer behavior. Use when user asks about 한국 마케팅, 네이버 마케팅, 카카오 광고, 쿠팡 마케팅, 국내 디지털 마케팅, or Korea-specific marketing.
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.