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-fashion-try-on
by SamurAIGPTVirtually try on different outfits by combining a person's photo and a clothing item, then optionally generate a professional fashion model video.
muapi-color-analysis-board
by SamurAIGPTTurn a portrait photo into a high-end editorial "Color Analysis Board" in a luxury fashion-magazine style (Dior / Ralph Lauren aesthetic) — best colors, undertone, makeup guide, capsule wardrobe, hair & jewelry recommendations, all laid out on a clean beige/ivory grid.
icon-designer
by fancyai-officialDesign with a legendary fashion icon — Coco Chanel, Alexander McQueen, Giorgio Armani, Valentino Garavani, Christian Dior, or Yves Saint Laurent. User selects a designer, describes their need, and the icon designs through their signature philosophy. Output is 3-angle on-model imagery (front, side, back). Use when the user wants to design with a fashion legend, mentions icon designer, legendary designer, design with Chanel/McQueen/Armani/Valentino/Dior/YSL, or wants garment design from a specific iconic fashion house perspective.
seedance-fashion-lookbook
by beshuaxianGenerate fashion lookbook, model showcase, and style video prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants fashion video content, lookbook videos, model walks, outfit showcases, style guides, fashion campaigns, runway clips, streetwear content, or any fashion/clothing video. Triggers on: fashion video, lookbook, model showcase, outfit, style guide, fashion campaign, runway, streetwear, collection launch, fashion ad, clothing video, OOTD, fashion film, or any fashion/model video request. Use even for "show off this outfit" or "fashion content for my brand."
seedance-fashion-lookbook
by beshuaxian为 Seedance 2.0（Higgsfield）生成时尚造型书、模特展示和风格视频提示。在用户想要时尚视频内容、造型书视频、模特走秀、服装展示、风格指南、时尚活动、跑道片段、街头风格内容或任何时尚/服装视频时使用。在以下情况下触发：时尚视频、造型书、模特展示、服装、风格指南、时尚活动、跑道、街头风格、系列发布、时尚广告、服装视频、OOTD、时尚电影或任何时尚/模特视频请求。即使是"展示这件服装"或"为我的品牌提供时尚内容"也适用。
dress-score
by nikkigallery前往换装界面，并对当前穿搭进行打分、点评。仅适用于穿搭评分，不适用于照片评分
seedance-fashion-lookbook
by AKCodezGenerate fashion lookbook, model showcase, and style video prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants fashion video content, lookbook videos, model walks, outfit showcases, style guides, fashion campaigns, runway clips, streetwear content, or any fashion/clothing video. Triggers on: fashion video, lookbook, model showcase, outfit, style guide, fashion campaign, runway, streetwear, collection launch, fashion ad, clothing video, OOTD, fashion film, or any fashion/model video request. Use even for "show off this outfit" or "fashion content for my brand."
appraise-gemstone
by pjt222使用四C标准（颜色、净度、切工、克拉重量）、产地评估、处理检测和市场因素分析来 评估宝石价值。仅为教育咨询指导——非认证鉴定。适用于了解决定宝石价值的因素、 在专业鉴定前预筛宝石、评估卖家要价是否合理、学习宝石分级方法论，或了解处理 状态如何影响价值时。
appraise-gemstone
by pjt222Appraise gemstone value using the four Cs (color, clarity, cut, carat), origin assessment, treatment detection, and market factor analysis. Advisory educational guidance only — not a certified appraisal. Use when understanding factors that determine a gemstone's value, pre-screening stones before a professional appraisal, evaluating whether a seller's asking price is reasonable, learning gemstone grading methodology, or understanding how treatment status affects value.
image-outfit-swap
by meituAI 换装，保留人物面部/体型只替换衣物，支持文字描述或服装参考图。当用户提到换装、换衣服、试穿、穿上、改成 xxx 服装、虚拟试穿、服装替换、把裙子改成红色时触发。
eachlabs-fashion-ai
by eachlabsGenerate fashion model imagery, virtual try-on, runway videos, and campaign visuals using EachLabs AI. Use when the user needs fashion content, model photography, or virtual try-on.
image-tryon
by Starchild-ai-agentVirtual try-on: clothing, accessories, hairstyles, makeup, glasses, hats, shoes, watches. Use when the user wants to see how an item looks on a person — e.g. "try on this dress", "put these glasses on me", "show me with this hairstyle", "what would I look like in this outfit".
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.