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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tuya-smart-control
by tuyaControl Tuya smart home devices via natural language. Use when the user asks to control smart devices (turn on/off lights, AC, plugs, adjust brightness/temperature/mode), query device status or list devices, manage homes and rooms, rename devices, check weather by location, send notifications (SMS, voice call, email, or App push), view device data statistics (e.g. energy/power consumption), capture snapshots/short videos from IPC cameras, or subscribe to real-time device events (property changes, online/offline status) via WebSocket. Requires TUYA_API_KEY.
git-flow
by tuyaGit branch model for the tyutool repository, covering branch creation, merging, releases, and hotfixes. Triggers on git-flow, branch management, release, feature, hotfix, and related keywords.
tuyaopen-env-setup
by tuyaSet up and activate the TuyaOpen development environment, install system dependencies, and initialize tos.py. Use when the user mentions environment setup, activating the SDK, installing dependencies, export.sh, or when tos.py is not found. 环境搭建、环境初始化、激活开发环境、安装依赖。
tuyaopen-project-config
by tuyaCreate new TuyaOpen projects and platforms, manage build configurations, update platform dependencies, and use tos.py subcommands. Use when the user mentions creating a project, tos.py new, saving or choosing a config, tos.py update, or general tos.py usage. 创建项目、新建工程、配置管理、保存配置、选择配置、更新依赖。
tuyaopen-debug-helper
by tuyaAgent-friendly non-blocking serial log capture for TuyaOpen devices. Runs tos.py monitor in the background, writes logs to file, and lets the agent tail or stop it at any time. Use when the agent needs to capture device logs without blocking, run a debug session, or monitor UART output during a build-flash-analyze loop. 后台串口日志、非阻塞监控、设备日志捕获、agent日志分析。
tuyaopen-dev-loop
by tuyaAutomated build-flash-monitor-analyze development loop for TuyaOpen devices. Covers log analysis, error patterns, CLI testing, and iterative debugging. Use when the user mentions dev loop, automated testing, log analysis, debug cycle, iterative development, or CI loop. 开发闭环、自动化测试、日志分析、调试循环、迭代开发。
tuyaopen-device-auth
by tuyaConfigure device authorization credentials (UUID, AuthKey, PID) and network provisioning for TuyaOpen devices. Use when the user mentions device auth, authorization, UUID, AuthKey, tuya_config.h, provisioning, pairing, or cloud connection. 设备授权、授权码、配网、UUID、AuthKey、云连接。
tuyaopen-add-board
by tuyaAdd new board (BSP) support to TuyaOpen, including board directory structure, Kconfig, drivers, and config files. Use when the user mentions adding a board, new board, BSP, board support, hardware adaptation, or tos.py new board. 添加开发板、板级适配、新增BSP、硬件适配。
tuyaopen-build
by tuyaBuild and compile TuyaOpen projects, select build configurations, edit Kconfig options, clean artifacts, and run Linux ELF binaries. Use when the user mentions compiling, building, tos.py build, config choice, menuconfig, Kconfig, build error, or running a project. 项目编译、构建、编译配置、清理编译、编译错误、menuconfig、Kconfig。
tuyaopen-code-check
by tuya>-
tuyaopen-peripheral-touch
by tuyaTDL touch panel (touchpad) usage for TuyaOpen: discover the touch device, poll for touch coordinates, and handle multi-touch points. Touch shares the display's device name. 触摸、触摸屏、触控、触点、手势、点击、滑动、touchpad、touch、GT911、GT1151、CST816。
tuya-aivoice-ios-integration
by tuya涂鸦 AIVoice iOS SDK 接入指南。当用户需要在 iOS 项目中集成涂鸦 AI 音频业务包 （AI 笔记、AI 翻译、蓝牙音频设备配网与面板）时使用此 Skill。
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.