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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jlink
by zhinkgitJ-Link 下载与在线调试工具，用于探测设备、烧录固件、读写内存、查看寄存器、复位目标、读取 RTT/SWO 日志， 以及在线调试（暂停/恢复/单步/断点运行/调用栈/变量查看）。 当用户提到 J-Link、JLink、RTT、烧录固件、写内存、读内存、寄存器查看、目标复位、探针连通性检查、 在线调试、单步、断点、调用栈时自动触发，也兼容 /jlink 显式调用。 即使用户只是说"烧录一下"、"看看 RTT 输出"或"调试一下"，只要上下文涉及 J-Link 探针就应触发此 skill。
can
by zhinkgit嵌入式 CAN / CAN-FD 调试工具，用于扫描接口、监控报文、发送测试帧、记录日志、数据库文件解码和总线统计。 当用户提到 CAN、CAN-FD、DBC 解码、总线抓包、USB-CAN 联调、报文发送、总线统计、 PCAN、Vector、slcan、CAN 接口扫描、CAN ID 过滤、ASC 日志、BLF 文件时自动触发， 也兼容 /can 显式调用。即使用户只是说"看看 CAN 报文"、"发一帧试试"或"解码一下 DBC"， 只要上下文明确提到 CAN 总线通信的操作或问题就应触发此 skill。
eide
by zhinkgitEIDE (Embedded IDE) 工程构建工具，用于扫描 .eide/eide.yml 工程、枚举构建 配置 (ConfigName)、执行 build/rebuild/clean 并解析构建日志，返回可供 jlink/openocd 复用的产物路径。当用户提到 EIDE、Embedded IDE、eide.yml、 unify_builder、VS Code EIDE 扩展、Cl.eide 时自动触发，也兼容 /eide 显式调用。 即使用户只是说"用 EIDE 编译一下"或"EIDE 烧录到板子上"，只要上下文涉及 EIDE 嵌入式工程就应触发此 skill。
gcc
by zhinkgitGCC 嵌入式工程构建工具（CMake + arm-none-eabi-gcc），用于扫描 CMake 型嵌入式工程、 列出预设、配置、编译、重建、清理和分析 ELF 大小。当用户提到 GCC、arm-none-eabi、 CMake 嵌入式编译、Ninja 构建、ELF 大小分析、arm-gcc、交叉编译、cmake --build、 cmake --preset 时自动触发，也兼容 /gcc 显式调用。即使用户只是说"编译一下"或 "看看固件多大"，只要上下文涉及 CMake 嵌入式 GCC 工程就应触发此 skill。
keil
by zhinkgitKeil MDK 工程构建工具，用于扫描 .uvprojx/.uvmpw 工程、枚举 Target、执行 build/rebuild/clean 并解析构建日志，返回可供 jlink/openocd 复用的产物路径。 flash 子命令仅作为兼容入口保留。当用户提到 Keil、MDK、uVision、UV4、 Target 枚举、编译、重建、清理、烧录、下载固件、flash 时自动触发，也兼容 /keil 显式调用。 即使用户只是说"编译一下"或"烧录到板子上"，只要上下文涉及嵌入式 Keil 工程就应触发此 skill。
net
by zhinkgit嵌入式网络调试工具，用于发现接口、抓包、分析 pcap/pcapng、做连通性测试、端口扫描和流量统计。 当用户提到 Wireshark、tshark、Npcap、抓包、网络联调、端口扫描、连通性排查、pcap 分析、 网络接口、ping 测试、traceroute、流量统计、Modbus TCP、EtherNet/IP 等网络协议调试时自动触发， 也兼容 /net 显式调用。即使用户只是说"抓个包看看"、"扫一下端口"、"网络通不通"或"分析一下这个 pcap"， 只要上下文中出现具体工具名（tshark、Wireshark、Npcap）、协议名（Modbus TCP、EtherNet/IP、ICMP 等）、 调试动作（抓包、端口扫描、连通性测试、ping、traceroute、流量统计、pcap 分析）或网络接口操作，就应触发此 skill。
openocd
by zhinkgitOpenOCD 下载与调试工具，用于探针探测、固件烧录、Flash 擦除、GDB Server 启动、目标复位控制、 Telnet 在线调试（halt/resume/step/寄存器/内存/断点）、GDB 源码级调试，以及 Semihosting/ITM 输出捕获和底层查询。 当用户提到 OpenOCD、ST-Link、CMSIS-DAP、DAPLink、FTDI、烧录固件、擦除 Flash、GDB Server、 reset、interface/target/board 配置、openocd.cfg、在线调试、单步、断点、寄存器查看、 内存读写、semihosting 时自动触发，也兼容 /openocd 显式调用。 即使用户只是说"烧录一下"、"启动 GDB Server"、"擦除芯片"、"看看寄存器"、"单步调试" 或"抓一下 semihosting"，只要上下文涉及 OpenOCD 支持的开源调试器就应触发此 skill。
probe-rs
by zhinkgitprobe-rs 下载与调试工具，用于探针发现、固件烧录、复位、内存读写、GDB Server 调试和 RTT 日志读取。 当用户提到 probe-rs、cargo-embed、DAP、RTT、CMSIS-DAP、ST-Link、J-Link、烧录、芯片信息、 连接 under reset、probe 选择器、probe-rs gdb、probe-rs attach 时自动触发，也兼容 /probe-rs 显式调用。 即使用户只是说"用 probe-rs 烧进去"、"看看 RTT"或"拉个 backtrace"，只要上下文明确提到 probe-rs 的功能、CLI 命令或相关术语（如 cargo-embed、RTT、probe-rs flash）时就应触发此 skill。
serial
by zhinkgit嵌入式串口调试工具，用于扫描串口、实时监控、发送数据、记录日志和 Hex 查看。 当用户提到串口、COM 口、UART、AT 命令调试、波特率、Hex 串流、串口抓日志、 串口监控、查看 MCU 输出、二进制协议联调时自动触发，也兼容 /serial 显式调用。 即使用户只是说"看看串口输出"、"发个 AT 命令"或"抓一下日志"，只要上下文涉及 串口通信就应触发此 skill。
ssh
by zhinkgitSSH/服务器操作助手。用于远程服务器、user@host、SSH 配置、上传下载、部署、跳板机、隧道、端口转发、服务器命令执行等任务；以 ~/.ssh/config 的 Host 别名为唯一服务器清单，优先密钥认证，通过本 skill 的 Python 脚本封装 OpenSSH 操作。
terminal
by zhinkgit双向交互终端会话工具，用于串口终端、SSH 交互 Shell、本地 Shell、设备控制台、AT/CLI 菜单、需要保持上下文的交互式调试；当用户提到交互终端、串口终端、SSH 终端、打开 shell、发送命令后继续读输出、保持会话、登录后操作、菜单式命令行时触发，也兼容 /terminal 显式调用。
workflow
by zhinkgitembeddedskills 的薄编排层，用于在当前 workspace 中发现工程、选择 build/flash/debug/observe 后端、串联 .embeddedskills/state.json，并聚合底层 skill 的结果。 当用户明确输入以下命令之一："一键构建烧录"、"自动诊断"、"串起 build -> flash -> debug -> observe" 或显式调用 /workflow 时触发。
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.