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.
Querying local SQLite index...
visual-style-ppt
by irenerachelCreate style-driven slide images strictly with the Image 2 model, assemble those images into image-only PPTX decks, and manage reusable visual style libraries from documents or visual references. Use when the user asks for a "PPT Skill", "风格驱动 PPT", "提炼风格做 PPT", "调用某个风格做 PPT", "图片版 PPT", "保存 PPT 风格", "列出 PPT 风格", "文档生成 PPT", "文章生成 PPT", "把文档做成演示文稿", or wants to extract, save, reuse, and apply visual style keywords specifically for visual slide/image deck creation.
skill-interview-builder
by irenerachel通过分步访谈引导用户理清需求，最终产出完整的Skill文件包（含SKILL.md、参考文档、示例文件等）， 并打包为可直接使用的压缩包。 当用户说"我想通过访谈新建Skill"、"用访谈方式做一个Skill"、"访谈建Skill"、 "通过访谈帮我生成Skill"、"访谈式创建Skill"、"我想访谈做一个XX的技能"时触发。 触发关键词必须包含"访谈"二字，不含"访谈"的Skill创建请求不由本Skill处理。 不用于已有完整SKILL.md只需小改的情况，也不用于一次性提示词请求。
ai-gameplay-pack
by irenerachel阿真的 AI 游戏实机视频提示词包工厂。基于《用 GPT Image 2 + Seedance 2.0 做 AI 游戏实机视频》工作流，给一张参考图或一段角色关键词/IP 名称，一键产出整套关键帧图片提示词 + Seedance 视频提示词 + 镜头序列时间轴；剧情向额外加字幕脚本和 TTS 配音脚本。当用户说"实机视频"、"伪实机"、"AI 游戏实机"、"游戏实机提示词"、"做成游戏"、"游戏化"、"实机演示"、"gameplay video pack"、"gameplay pack"、"游戏化视频"、"做一个 XX 的实机视频"时触发。两种输入模式：① 用户给参考图 → 读图提取角色特征作为锚点；② 用户给角色关键词或 IP 名称 → 先生成"角色参考表" prompt 作为 0 号。默认演示向 + 6 镜头，可切剧情向（带对白和字幕）/ 混合向（前 gameplay 后 cutscene）。输出按 Irene 偏好：图片中文段落、视频 ```plaintext {wrap} 代码块、不写负面词、不用破折号、不带 MJ 后缀、不写 --xx 参数。**严禁抄主教程文档里的具体例子（赛博朋克双角色对峙、雨夜天台、维多角色名等），变量字典只是参考，每次实例化的主体/场景/对白必须原创。**
ai-gameplay-pack-lite
by irenerachel阿真的 AI 游戏实机视频提示词包工厂【极简版】，节省 Token 无废话直接出提示词。给一张参考图或角色关键词 → 只输出 6 镜头图片 + 视频 prompt + 极简 UI 字典，**禁止输出**任何说明性章节（自检清单 / 后期建议 / 跑图操作步骤 / 进度条状态链 / HUD 继承图谱 / 参考图挂图清单 / 科普点 / 变化点 / 任何解释段落）。当用户说"实机视频精简版"、"精简提示词包"、"lite gameplay pack"、"无废话实机视频包"、"省 Token 实机包"、"实机精简版"时触发。与完整版 ai-gameplay-pack skill 共享所有规则（不要破折号 / 不要商业 IP / 不要 Hello Kitty / 必出 0 号四视图 / `{{{图N}}}` 引用语法 / 视频 prompt 含 1 句克制音效 / 主角朝向明示 / 比例 16:9）但 prompt 字数严格压缩：图片 prompt 200 字内、视频 prompt 80 字内、0 号四视图 200 字内。
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.