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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hans-zimmer-perspective
by khanhhuyenngo985-sys汉斯·季默的电影配乐思维框架。基于季默配乐体系, 提炼6个核心心智模型、6条决策启发式和完整的表达DNA。 用途:作为创作顾问,用季默的视角分析电影配乐,判断美学方向、评估声音方案。 触发词(符合任一即激活): - 核心触发:「季默视角」「汉斯·季默」「盗梦空间」「星际穿越」「黑暗骑士」 - 进阶触发:「电子×管弦」「Hybrid Orchestra」「主题动机」「音效设计」「Hans Zimmer」 - 场景触发:「盗梦空间」「星际穿越」「蝙蝠侠:黑暗骑士」「勇闯黄金国」「角斗士」 - 方法论触发:「电子是我的乐器」「声音设计是叙事」「动机即角色」「混音作为作曲」 - 即使用户只是说"这个有没有季默的感觉""帮我用配乐的角度想想""随便聊聊电影音乐"也应触发
tarkovsky-perspective
by khanhhuyenngo985-sys塔可夫斯基的雕刻时光思维框架。基于《塔可夫斯基深度研究》《塔可夫斯基美学风格指南》, 提炼6个核心心智模型、6条决策启发式和完整的表达DNA。 用途:作为影像创作顾问,用塔可夫斯基的视角分析时间艺术、长镜头凝视、诗意现实。 触发词(符合任一即激活): - 核心触发:「塔可夫斯基视角」「雕刻时光」「长镜头凝视」「潜行者」「乡愁」「镜子」 - 进阶触发:「时间作为物质」「存在的电影」「自然元素语言」「空气」「诗的逻辑」 - 场景触发:「雨/水/火/风」「超慢节奏」「神圣感」「超现实」
cai-guoqiang-perspective
by khanhhuyenngo985-sys蔡国强的爆炸艺术思维框架。基于蔡国强艺术体系, 提炼6个核心心智模型、6条决策启发式和完整的表达DNA。 用途:作为创作顾问,用蔡国强的视角分析视觉艺术,判断美学方向、评估创作方案。 触发词(符合任一即激活): - 核心触发:「蔡国强视角」「火药艺术」「爆炸」「天梯」「东方哲学」 - 进阶触发:「火药×水墨」「瞬间×永恒」「自然×人工」「视觉震撼」「爆破」 - 场景触发:「天梯」「草船借箭」「烽火」「龙」「白日焰火」 - 方法论触发:「火药即语言」「爆破作为冥想」「东方×当代」「自然参与」 - 即使用户只是说"这个有没有蔡国强的感觉""帮我用火药的角度想想""随便聊聊爆炸艺术"也应触发
kurosawa-perspective
by khanhhuyenngo985-sys黑泽明的史诗戏剧思维框架。基于公开研究资料, 提炼6个核心心智模型、6条决策启发式和完整的表达DNA。 用途:作为影像创作顾问,用黑泽明的视角分析武士道、社会批判、道德困境、戏剧光影。 触发词(符合任一即激活): - 核心触发:「黑泽明视角」「武士道」「日本电影天皇」「七武士」「罗生门」「生之欲」 - 进阶触发:「戏剧光」「高对比」「自然作为角色」「社会批判」「主观真相」 - 场景触发:「战国时代」「幕府」「武士片」「农民与武士」
ozu-perspective
by khanhhuyenngo985-sys小津安二郎的日常永恒美学思维框架。基于《小津安二郎深度研究》《小津安二郎美学风格指南》, 提炼6个核心心智模型、6条决策启发式和完整的表达DNA。 用途:作为影像创作顾问,用小津的日常永恒、物哀、沉默美学视角分析视觉叙事、判断美学方向、评估家庭/仪式主题的创意方案。 触发词(符合任一即激活): - 核心触发:「小津视角」「榻榻米视角」「物哀」「日常永恒」「空脸」 - 进阶触发:「家庭仪式」「缺席比在场更有力」「沉默是语言」「全景深」 - 场景触发:「出嫁」「老年孤独」「餐桌」「空荡房间」「凝视窗外」 - 方法论触发:「日常即神圣」「物哀」「周遭(Sonnets)」「镜头组」
mempalace
by khanhhuyenngo985-sysAI跨会话记忆系统,96.6% LongMemEval得分,自动保存关键上下文到记忆宫殿。触发词:「记住这个」「保存到记忆」「调取记忆」「mempalace」「跨会话记忆」。
sound-design-to-prompt
by khanhhuyenngo985-sys将声音设计概念转化为AI视频生成提示词。覆盖音效(Foley)、环境音、环境音层、声音质感、空间感、声音节奏。 配合 ai-music-generator(音乐)和 rhythm-to-prompt(视觉节奏)使用。 触发词:「声音设计」「音效」「环境音」「Foley」「声音质感」「AI配音」「音效提示词」「sound effects」「ambient」。
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」
video-analysis
by khanhhuyenngo985-sys对视频进行结构化拉片分析,提取镜头语言、色调、节奏、叙事结构等创作参数,存入案例库并优化提示词矩阵。触发词:「拉片」「分析视频」「视频分析」「拆解这个视频」「学习这个广告」「提取创作参数」。
wechat-reader
by khanhhuyenngo985-sys读取微信群聊记录,提取任务、决策、需求信息。触发词:「读取群聊」「微信记录」「剧组群」。
ai-music-generator
by khanhhuyenngo985-sys为视频项目生成AI音乐/BGM,处理风格选择、提示词生成、音量标准和音乐生成平台质量控制(Suno/Seedance Audio/Udio)。触发词:「生成音乐」「BGM」「配乐」「AI音乐」「作曲」「Suno」。
araki-perspective
by khanhhuyenngo985-sys荒木经惟的摄影美学思维框架。基于荒木经惟摄影体系, 提炼6个核心心智模型、6条决策启发式和完整的表达DNA。 用途:作为创作顾问,用荒木经惟的视角分析视觉叙事、判断美学方向、评估摄影方案。 触发词(符合任一即激活): - 核心触发:「荒木经惟视角」「私写真」「捆绑」「街拍」「情感写真」 - 进阶触发:「花系列」「阳子」「生死」「情色×艺术」「东京」「即时性」 - 场景触发:「东京日和」「感伤之旅」「遗作」「写真页」「花曲」 - 方法论触发:「摄影是余情」「拍就是爱」「日常即圣域」「快门即告白」 - 即使用户只是说"这个有没有荒木的感觉""帮我用私写的角度想想""随便聊聊摄影"也应触发
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.