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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deepsop-genvis
by 2393970875AI 图片与视频异步生成技能，调用 AI Artist API 根据文本提示词生成图片或视频，自动轮询直到任务完成。 ⚠️ 使用前必须设置环境变量 DEEPSOP_API_KEY 为你自己的 API Key！ 需要 API Key 授权：已有账号请前往 https://ai.deepsop.com/login?source=2 登录获取；没有账号请前往 https://ai.deepsop.com/register?source=2 注册后获取。 支持图片模型：3.1Nano2-Evo、S5.0L、N2、W2.7、W2.7Pro、Nano2-Beta-Evo、**Image2（GPTimage-2）**。 支持视频模型：V3.1FB、S1.5Pro、V3.1PB、V3.1Fast、W2.6t / W2.6i / W2.6r、klingV3Omni、W2.7t / W2.7i / W2.7r、**S2.0 / S2.0Fast**（Seedance2.0 系列，支持多音频参考与联网搜索）、**HappyHorse**（高效短视频，支持文生/首帧/参考图/视频编辑模式）。 默认模型从接口 `consumeSource/list` 实时获取（第一个非 `auto` 的可用模型），无本地硬编码兜底；默认模型生成失败时不得自动切换到其他模型，必须把失败模型与失败原因告知用户；查看当前服务端激活的模型请运行：`python3 scripts/generate_image.py --list-models`。 触发场景： - 用户要求生成图片，如"生成一匹狼"、"画一只猫"、"风景画"、"帮我画"等。 - 用户要求生成视频，如"生成视频"、"文生视频"、"图生视频"、"生成一段...的视频"等。 - 用户指定模型：N2、S5.0L、W2.7、W2.7Pro、3.1Nano2-Evo、Nano2-Beta-Evo、Image2、GPTimage-2、gpt-image-2、S1.5Pro、V3.1FB、V3.1PB、V3.1Fast、W2.6t、W2.6i、W2.6r、klingV3Omni、W2.7t、W2.7i、W2.7r、S2.0、S2.0Fast、Seedance2.0、HappyHorse。 - 用户上传参考图/参考视频时，自动先调用文件上传 API 转换为可访问 URL。
deepsop-tiktokflow
by 2393970875TikTok 视频 AI 生成与发布技能（数字员工 Toby）。用户输入自然语言指令，AI 自动拆解任务参数，调用 deepsop 平台接口生成 AI 视频并发布到 TikTok，等待后查询并推送播放/点赞/评论/分享等数据。触发场景：用户说「发 TikTok 视频」「生成视频发布到 TikTok」「抖音国际版发视频」等与 TikTok 视频生成发布相关的指令；或收到包含 [DeepSOP-AutoQuery-Toby] 标记的系统定时事件（cron 回调）。⚠️ 调用本 SKILL 前必须先完整阅读 SKILL.md。提交 agentSubmitTask **必须**走 scripts/submit_task.py（通过 heredoc 把 body 喂给 stdin），脚本内部串行跑 validate_employee_params.py + UTF-8 安全 HTTP 提交，**禁止**直接写 curl 命令（会因 Windows cp936 代码页导致 taskName/taskDescription 中文乱码）。脚本退出码 0 才算成功；非 0 必须把 summary/errors 原样回给用户后修正重试，禁止绕过校验或假装成功。
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.