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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json-serialization
by bkywksjTauri 项目中 JSON 序列化/反序列化技能，覆盖 Rust serde 和 TypeScript 类型系统。 触发场景： - 需要定义 Rust 和 TypeScript 之间的数据传输类型 - 需要处理 JSON 序列化/反序列化 - 需要处理复杂嵌套数据结构 - serde 配置和自定义序列化 触发词：JSON、序列化、serde、类型转换、数据传输、Serialize、Deserialize
api-development
by bkywksjTauri Command (IPC API) 开发技能，指导如何设计和实现 Rust Command 供前端调用。 触发场景： - 需要创建新的 Tauri Command - 需要设计前后端通信接口 - 需要处理 Command 的参数和返回值 - 需要实现异步 Command 触发词：Command、API、invoke、IPC、接口、通信、前后端
architecture-design
by bkywksjTauri 架构设计技能，指导双进程架构下的模块拆分和代码组织。 触发场景： - 需要设计新模块的架构 - 需要重构现有代码结构 - 需要决定功能放在 Rust 还是 React 侧 - 需要设计插件集成方案 触发词：架构、设计、模块、拆分、重构、组织、结构
brainstorm
by bkywksj当需要探索方案、头脑风暴、创意思维时自动使用此 Skill。 触发场景： - 不知道怎么设计 - 需要多种方案 - 架构讨论 - 功能规划 触发词：头脑风暴、方案、怎么设计、有什么办法、创意、讨论、探索、建议、怎么做、如何实现
bug-detective
by bkywksj排查已发生的问题、定位 Bug 原因。 触发场景： - 代码运行报错，需要定位原因 - 功能不正常，需要排查 - Tauri Command 返回错误，需要分析 - 日志分析、调试代码 触发词：Bug、报错、不工作、调试、排查、为什么、出问题、失败、不生效、无效、找不到原因、定位问题
code-patterns
by bkywksj代码模式与最佳实践技能，提供 Tauri 项目中常用的设计模式和编码规范。 触发场景： - 用户需要了解项目的编码规范 - 用户需要应用设计模式解决问题 - 用户需要重构代码以符合最佳实践 触发词：设计模式、编码规范、最佳实践、代码风格、重构
collaborating-with-codex
by bkywksj与 OpenAI Codex CLI 协同开发。将编码任务委托给 Codex 进行原型开发、调试分析和代码审查。 触发场景： - 需要算法实现或复杂逻辑分析 - 需要代码审查和 Bug 分析 - 需要生成 Unified Diff 补丁 - 用户明确要求使用 Codex 协作 - 复杂后端逻辑的原型设计 触发词：Codex、协作、多模型、原型、Diff、算法分析、代码审查、codex协同 前置要求： - 已安装 Codex CLI (npm install -g @openai/codex) - 已配置 OpenAI API Key
collaborating-with-gemini
by bkywksj与 Google Gemini CLI 协同开发。将编码任务委托给 Gemini 进行前端原型、UI设计和代码审查。 触发场景： - 需要前端/UI/样式原型设计 - 需要 CSS/React/Vue 组件设计 - 需要代码审查和 Bug 分析 - 用户明确要求使用 Gemini 协作 - 复杂前端逻辑的原型设计 触发词：Gemini、协作、多模型、前端原型、UI设计、CSS、样式、gemini协同 前置要求： - 已安装 Gemini CLI (npm install -g @google/gemini-cli) - 已配置 Google API Key (GEMINI_API_KEY 环境变量或 gemini auth login) 注意：Gemini 对后端逻辑理解有缺陷，后端任务优先使用 Codex。
database-ops
by bkywksjTauri 本地数据库操作技能，使用 rusqlite 进行 SQLite 数据库操作。 触发场景： - 需要在桌面应用中持久化数据 - 需要使用 SQLite 数据库 - 需要设计本地数据表结构 - 需要执行 CRUD 数据库操作 触发词：数据库、SQLite、SQL、持久化、存储、表、查询、CRUD、数据
docs-management
by bkywksjVitePress 文档站点管理技能。负责初始化文档站点、增量同步代码变更到文档、追踪同步元数据（.docs-meta.json），生成的文档风格模仿同目录 tauri-docs / tauri-cc-docs / knowledge-base-docs。 触发场景： - 需要为项目生成对外文档站点（VitePress） - 需要基于代码变更增量更新已有文档 - 需要从零初始化独立 docs 仓库（同级目录） - 需要在本项目内部生成 ./website/ 文档 - 需要检查文档同步状态（.docs-meta.json） 触发词：文档站点、VitePress、docs 站点、用户手册、更新文档、文档同步、docs-management、.docs-meta.json、website 目录、文档仓库
error-handler
by bkywksjTauri 异常处理技能，覆盖 Rust 错误处理和 React 错误边界。 触发场景： - 需要设计错误处理策略 - 需要处理 Rust Command 中的错误 - 需要处理前端 invoke 调用失败 - 需要实现全局错误处理 触发词：异常、错误处理、Error、Result、try-catch、panic、崩溃、错误边界
file-storage
by bkywksjTauri 文件操作技能,覆盖 Rust std::fs 和 Tauri FS Plugin 的文件读写。 触发场景: - 需要读写本地文件 - 需要选择文件/目录(对话框) - 需要管理应用数据目录 - 需要处理文件拖放 触发词: 文件、读写、保存、打开、目录、文件系统、fs、拖放、导入、导出
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.