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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seedance-lighting
by Emily2040This skill should be used when the user asks for lighting design, atmosphere, time of day, color temperature, shadow, reflections, weather light, practical lights, or mood transitions in Seedance 2.0.
relight
by agentspace-soRelight a still image — change the lighting setup, color temperature, direction, or mood — on RunComfy via the `runcomfy` CLI. Routes to Qwen Edit 2509's dedicated `relight` LoRA endpoint for purpose-built relighting, with fallback to identity-preserving edit endpoints (Nano Banana 2 Edit, GPT Image 2 Edit, FLUX Kontext Pro) when prose lighting language is enough. Use for product relighting (studio softbox → window light), portrait mood shift (overcast → golden hour), or color-grade change. Triggers on "relight", "relighting", "change the lighting", "make it golden hour", "studio lighting", "rim light", "blue hour", "soft window light", "change light direction", "color temperature", or any explicit ask to alter how a still is lit.
lighting-setup-planner
by Eli-yu-firstPlan studio and outdoor lighting setups with equipment recommendations and diagrams
soft-pastel-animation-lighting
by 0xzgbotSoft pastel animated-feature lighting with warm, happy, family-safe visual tone.
blender-lighting
by loonghaoBlender lighting — create, configure and manage lights and world background
ai-colorist
by HaibarakikuAI调色师，专精为Seedance 2.0生成内容设计色彩方案并确保跨镜头色彩一致性。涵盖LUT风格设计、色彩情感调度、Seedance色调Prompt规范和后期DaVinci Resolve调色工作流。Use when: 调色, color grading, LUT, 色彩一致性, 色调, post-production color.
seedance-lighting
by Osgaa444Specify lighting, atmosphere, and light transitions for Seedance 2.0 prompts using named light sources, core parameters, and atmosphere contracts. Use when the scene needs a specific mood, time of day, or lighting style, or when lighting is flat, inconsistent across shots, or clipping.
color-assist
by jenkinsm13AI-powered color grading assistant. Exports the current frame in sRGB, visually analyzes it, and makes CDL adjustments directly on the Color page nodes. Works regardless of project color space (HDR, P3, ACES, etc.) because the frame is converted to sRGB for analysis — the color space LLMs are trained on.
lighting-design-plan
by WinbdaDesign lighting plans with fixtures and placement. TRIGGERS - Use when user needs help with lighting-design-plan related tasks.
lighting-setup-guide
by WinbdaGuide lighting setups for different scenarios. TRIGGERS - Use when user needs help with lighting-setup-guide related tasks.
tint-to-prompt
by khanhhuyenngo985-sys将色彩理论、情绪色调、导演美学配方转化为AI视频平台可执行的提示词。 核心功能：输入色调描述（色相/情绪/风格）→ 输出结构化AI提示词（含参数、色值、平台适配） 触发词： - 核心触发：「色调转提示词」「色彩参数」「色值生成」「颜色配方」 - 进阶触发：「橙蓝撞色」「单色系」「低饱和灰」「高饱和撞色」「黑白灰」 - 情绪触发：「冷调孤独」「暖调怀旧」「压抑暗调」「明亮活力」「复古色调」 - 导演触发：「王家卫色调」「北野武色调」「胡金铨色调」「李安色调」「侯孝贤色调」 - 场景触发：「雨夜霓虹」「晨雾山林」「海边日落」「极简空间」「废墟」 - 方法触发：「色彩心理学」「色相环」「互补色」「邻近色」「饱和度」「明度」
rapid-flow-designer
by abossardDesign rapid directional lighting scenes that read as lightning-bullet motion with controlled intensity. Use when creating high-velocity LED looks with scroll/scan carriers and safe duration/pacing constraints.
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.