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...
pw-redbook-image
by plugins-world将文章内容拆解为小红书风格的系列配图。支持从 URL、文件或文本生成封面图、内容图和结尾图，自动创建提示词并调用图片生成工具。
pw-embedded-c-style
by plugins-world嵌入式 C 代码风格助手, 基于 51 单片机教学项目的代码规范。默认使用蛇形命名 (snake_case), 可选驼峰命名。用于创建符合嵌入式开发规范的项目结构, 优化代码风格, 提供硬件驱动模板。适用于 51 单片机、STM32 等嵌入式 C 项目开发。
pw-image-generation
by plugins-worldAI 图像生成和处理工作流。通过提示词生成图像，支持文生图、图生图、批量生成、图床管理、长图合并、PPT 打包。核心特性是逐张确认生成，避免浪费 API 额度。
pw-post-to-wechat
by plugins-world发布内容到微信公众号的自动化工具。 核心功能: - 图文发表: 多图配文模式, 支持最多 9 张图片 - 文章发表: 完整 Markdown 格式化, 保留样式和排版 - 自动化流程: 使用 Chrome CDP 自动登录、填充、发布 - 智能压缩: 标题和内容超限时自动压缩 - 主题支持: 文章发表支持 default、grace、simple 三种主题 使用时机: - 用户明确要求 "发布到微信公众号"、"发送到公众号" - 用户提供 markdown 文件或内容需要发布 - 用户需要批量发布图文内容 不适用场景: - 用户只是询问如何发布 (提供建议即可) - 用户需要编辑现有公众号文章 (手动操作) - 用户需要发布到其他平台 (使用对应平台工具)
pw-aippt-old
by plugins-world基于 PPT 模板生成新内容。PDF 自动转图片 → 分析模板风格 → 拆分文章内容 → 生成提示词 → AI 生图 → 打包 PPTX。
pw-cover-image
by plugins-world为文章内容生成精美封面图的专用工具。 核心功能: - 分析文章内容并自动选择最适合的视觉风格 - 生成手绘风格的高质量封面图片 - 支持 19 种预设风格 (elegant, blueprint, bold-editorial 等) - 支持多种宽高比 (2.35:1 电影感, 16:9 宽屏, 1:1 方形) - 自动提取核心主题并生成标题文字 使用时机: - 用户明确要求 "生成封面图"、"创建文章封面"、"制作封面" - 用户提供文章内容并需要配图 - 用户需要为博客、公众号、社交媒体制作封面 不适用场景: - 用户只是询问如何制作封面 (提供建议即可) - 用户需要编辑现有图片 (使用图片编辑工具) - 用户需要生成非封面类型的图片 (使用通用图片生成工具)
pw-danger-gemini-web
by plugins-world通过逆向工程的 Gemini Web API 进行文本和图像生成。 核心能力: - 文本生成: 使用 Gemini 模型生成文本响应 - 图像生成: 从文本提示生成图像并保存到本地 - 视觉输入: 支持参考图像进行图生图或图生文 - 多轮对话: 通过 sessionId 保持上下文连续性 使用时机: - 需要生成高质量 AI 图像时 (封面图、配图、插画等) - 需要 Gemini 模型进行文本生成时 - 需要基于参考图像生成变体或描述时 - 作为其他技能 (pw-cover-image、pw-redbook-image) 的图像生成后端 不适用场景: - 需要官方 API 支持和稳定性保证的生产环境 - 对 API 变更敏感的关键业务流程 - 需要批量高频调用的场景 (可能触发限流) 重要提醒: - 首次使用需要用户同意免责声明 - 需要 Google 账号认证 (自动打开浏览器登录) - 在中国大陆需要配置代理访问 - 这是非官方 API, 可能随时失效
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.