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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product-photoshoot-workflow
by jau123Multi-angle product imagery workflow. Use when the user wants to "shoot a product", "make e-commerce product images", "product photography set", "电商产品图", "产品多角度图", "brand product visuals", or provides a single product photo and asks for marketing-ready variations. Produces 4 distinct directions (lifestyle scene, macro detail, scale/context, marketing layout) from one reference image. NOT for: portraits, generic illustration, logo design, video creation — use other skills or generate_image directly.
social-thumbnail-workflow
by jau123Vertical-format thumbnail and poster workflow for short-video platforms and social feeds. Use when the user asks for a "video thumbnail", "短视频封面", "竖版海报", "TikTok cover", "Reels cover", "YouTube Shorts thumbnail", "social media poster" — anything optimized for 9:16 mobile feed scrolling with prominent headline space. Produces high-contrast, headline-friendly cover art in 9:16 by default. NOT for: full posters meant for print, photorealistic portraits without text overlay intent, horizontal banners — use other skills.
meigen-visual-creative-expert
by jau123This skill should be used when the user asks to "generate an image", "create artwork", "design a logo", "make a poster", "draw something", "find inspiration", "search for reference images", "enhance my prompt", "improve prompt", "brand design", "product mockup", "batch generate images", "multiple variations", "generate a video", "make a video", "animate this photo", "image-to-video", or discusses AI image/video generation, visual creativity, prompt engineering, reference images, style transfer. Also activate when user mentions MeiGen, image models, aspect ratios, or art styles. NOT for: generic chat/text tasks, code generation, document writing, video editing of existing footage, audio/TTS, real-photo retouching of user files outside the generation flow, or any task unrelated to AI image/video creation.
ai-image-generator-editor-gpt-image-2-nanobanana-comfyui
by jau123Generate images and videos from text with multi-provider routing — supports GPT Image 2.0 (near-perfect text rendering), Nanobanana 2, Seedream 5.0, Midjourney V8.1 (unified photorealistic + anime), Flux 2 Klein (cheap drafts), Seedance 2.0 / Happyhorse 1.0 / Veo 3.1 video, and local ComfyUI workflows. Includes 1,446 curated prompts and style-aware prompt enhancement. Use when users want to create images/videos, design assets, animate photos, enhance prompts, or manage AI art workflows. NOT for: generic chat, code generation, document writing, video editing of existing footage, audio/TTS, or any task unrelated to AI image/video creation.
memory-management
by jau123Guide Claude Code memory and CLAUDE.md management — what to record, how to write, when to update vs create new, and how to organize. Use when user asks to "记一下"、"新增记忆"、"更新记忆"、"沉淀本次经验"、"看看本次有什么值得记的"、"改 schema"、"加新 type"、"改记忆结构"、"review memory"、"audit memory"、"复盘"、"开发完了"、"总结一下" or discusses memory system design / CLAUDE.md 管理 / memory schema / memory hub / 防记忆系统漂移. Also use after feature work / debug / 对抗审查 when new insights worth recording.
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.