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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muapi-floor-plan-rendering
by SamurAIGPTDesign a 2D floor plan and convert it into a realistic, high-quality 3D architectural rendering.
muapi-interior-design
by SamurAIGPTCreate professional interior design visualizations — redesign existing rooms, generate new room concepts, or visualize specific furniture styles in a space.
color-harmony-engine
by revfactoryA color harmony engine for designing interior color palettes. The 'moodboard-designer' and 'style-analyst' agents must use this skill's color theory, palette formulas, and style-specific palette database when composing color palettes and analyzing styles. Used for 'color palette design', 'color harmony', 'color psychology', etc. Furniture selection and budget management are outside this skill's scope.
space-concept-board
by revfactoryA full pipeline where an agent team collaborates to generate an interior space concept board all at once. Use this skill for 'interior concept design', 'living room makeover', 'room atmosphere change', 'interior moodboard', 'furniture recommendations', 'space styling', 'color palette design', 'interior budget', 'DIY interior', 'home styling', 'studio interior', 'office interior', and all other space decoration tasks. Also supports item curation and budget management when an existing moodboard is provided. Actual construction work (tile installation, electrical wiring), 3D rendering, and AR furniture placement app integration are outside this skill's scope.
spatial-layout-guide
by revfactoryA layout guide for optimizing furniture placement and spatial traffic flow. The 'item-curator' and 'style-analyst' agents must use this skill's placement rules, dimension standards, and traffic flow design methods when selecting and arranging furniture. Used for 'furniture placement', 'spatial layout', 'traffic flow design', etc. Color design and budget management are outside this skill's scope.
color-harmony-engine
by revfactory인테리어 컬러 팔레트의 조화와 배색을 설계하는 색채 조화 엔진. 'moodboard-designer'와 'style-analyst' 에이전트가 컬러팔레트를 구성하고 스타일을 분석할 때 이 스킬의 색채 이론, 배색 공식, 스타일별 팔레트 DB를 반드시 활용해야 한다. '컬러팔레트 설계', '배색 조화', '색채 심리' 등에 사용한다. 단, 가구 선정이나 예산 관리는 이 스킬의 범위가 아니다.
space-concept-board
by revfactory공간 인테리어 컨셉보드를 에이전트 팀이 협업하여 한 번에 생성하는 풀 파이프라인. '인테리어 컨셉 잡아줘', '거실 꾸미고 싶어', '방 분위기 바꾸고 싶어', '인테리어 무드보드', '가구 추천해줘', '공간 스타일링', '컬러팔레트 짜줘', '인테리어 예산', '셀프 인테리어', '홈스타일링', '원룸 인테리어', '사무실 인테리어' 등 공간 꾸미기 전반에 이 스킬을 사용한다. 기존 무드보드가 있는 경우에도 아이템 큐레이션이나 예산 관리를 지원한다. 단, 실제 시공 시행(타일 시공, 전기 배선), 3D 렌더링, AR 가구 배치 앱 연동은 이 스킬의 범위가 아니다.
spatial-layout-guide
by revfactory가구 배치와 공간 동선을 최적화하는 레이아웃 가이드. 'item-curator'와 'style-analyst' 에이전트가 가구를 선정하고 배치를 제안할 때 이 스킬의 배치 규칙, 치수 기준, 동선 설계법을 반드시 활용해야 한다. '가구 배치', '공간 레이아웃', '동선 설계' 등에 사용한다. 단, 색채 설계나 예산 관리는 이 스킬의 범위가 아니다.
ui-ux-pro-max
by doccker专业级 UI/UX 设计规范，需要高质量界面设计时手动触发或描述"设计感/专业UI"时自动触发。 覆盖视觉层次、配色体系、排版节奏、交互微动效、响应式适配等。 日常前端开发由 frontend-dev skill 覆盖。
nw-design-methodology
by nWave-aiApple LeanUX++ design workflow, journey schema, emotional arc patterns, and CLI UX patterns. Load when transitioning from discovery to visualization or when designing journey artifacts.
color-expert
by meodaiUse when working with color naming, color theory, color spaces, color definitions, or any task involving color knowledge - palettes, ramps, gradients, conversions, accessibility, perceptual matching, pigment mixing, print-vs-screen color, CSS color syntax, or historical color terminology. Use this skill whenever the user is choosing, comparing, generating, naming, converting, or explaining colors, even if they do not explicitly ask for "color theory."
style-extractor
by Lucent-SnowExtract evidence-based style guides and motion appendices from websites or web apps. Use when Codex needs reusable visual language, semantic tokens, component/state rules, runtime animation evidence, or style references that preserve design signal while stripping product-specific structure and content.
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.