Ros Robotics logo

Ros Robotics

Community
wzyn20051216
ros-robotics

适用于 ROS 1 / ROS 2 机器人开发、迁移与联调的 skill。覆盖 catkin/catkin_tools、colcon/ament、package.xml、CMakeLists.txt、launch、URDF/Xacro、RViz、TF、Navigation2、ros2_control、串口/CAN、MCU 与 micro-ROS 等常见工程场景。

Overview

Publisherwzyn20051216
Repositoryros-robotics-skill
Skill nameros-robotics
Stars
63
Forks
4
Bundled files
49
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 49 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wzyn20051216 on GitHub. Read the source before you install it.

Installation

Install the Ros Robotics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/wzyn20051216/ros-robotics-skill/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wzyn20051216/ros-robotics-skill.git \
  .claude/skills/ros-robotics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ros Robotics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ros Robotics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ros Robotics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ROS Robotics

何时使用

当用户请求涉及以下任一主题时使用本 skill:

  • catkin_ws、colcon_ws、src/CMakeLists.txt、package.xml、setup.py
  • ROS 1 / ROS 2 package 新建、移植、构建、测试、调试
  • launch / launch.py、参数 YAML、URDF/Xacro、RViz、TF
  • topic / service / action / msg / srv / QoS
  • 底盘、IMU、LiDAR、相机、定位、SLAM、Navigation2、ros2_control
  • Gazebo / Ignition 仿真、传感器仿真、ros_gz_bridge
  • Docker 容器化 ROS 开发、docker-compose 多节点编排
  • Lifecycle 节点、rclcpp_components 组件化、进程内通信
  • rosbag2 录制回放、mcap、离线诊断
  • SLAM 建图(Cartographer、SLAM Toolbox、ORB-SLAM)
  • DDS / QoS 配置、ROS_DOMAIN_ID、RMW 选择、多机网络
  • 自定义 msg / srv / action 接口定义与生成
  • 多机器人系统、命名空间隔离、跨机通信
  • MoveIt 2 运动规划、SRDF、运动学插件
  • ROS 2 CI/CD(GitHub Actions、colcon test、ament_lint)
  • Foxglove Studio / PlotJuggler 数据可视化与离线分析
  • MCU / RTOS / UART / CAN / micro-ROS 协同开发

先做什么

1. 先识别工作区

bash
python scripts/detect_ros_workspace.py <workspace-path>

2. 再做一致性检查

bash
python scripts/check_ros_workspace_consistency.py <workspace-path>

3. 按问题加载最少必要文档

  • ROS 1:references/ros1-catkin.md
  • ROS 2:references/ros2-colcon.md
  • 混合 / 迁移:references/interop-and-migration.md
  • 模型 / TF:references/robot-description-and-tf.md
  • 导航:references/navigation2.md
  • 控制:references/ros2-control.md
  • 嵌入式:references/micro-ros-and-embedded.md
  • 仿真:references/gazebo-simulation.md
  • Lifecycle / 组件:references/lifecycle-and-components.md
  • DDS / QoS / 网络:references/dds-qos-networking.md
  • 录制回放 / 诊断:references/rosbag-diagnostics.md
  • SLAM / 建图:references/slam-mapping.md
  • Docker:references/docker-ros.md
  • 自定义接口:references/custom-interfaces.md
  • 多机器人:references/multi-robot.md
  • MoveIt 2:references/moveit2.md
  • CI/CD:references/ros2-cicd.md
  • 可视化:references/foxglove-visualization.md
  • 排障:references/debug-playbooks.md
  • 审查:references/review-checklist.md

必须遵守的原则

  • 先识别构建系统,再改代码
  • 先修 package.xml / CMakeLists.txt 一致性,再谈重构
  • 先看 TF、时间戳、frame、单位,再怪算法
  • 先证据链,再修改:构建输出、topic、TF、参数、日志必须可验证
  • ROS 1 → ROS 2 先桥接或分层,不要一口气推倒重来
  • MCU / micro-ROS 先判断是否真的需要进入 ROS 图

常见任务处理方式

新增或修复 package

  • 先判定包类型:catkin、ament_cmake、ament_python
  • 先补依赖声明,再补构建逻辑,再补安装规则
  • 自定义 msg / srv / action 优先检查生成顺序和依赖闭环(详见 references/custom-interfaces.md)

构建失败

  • 先跑 rosdep
  • 再核对 package.xml 与构建脚本
  • 再看环境是否正确 source
  • 再看系统依赖、ABI、发行版兼容

运行正常但机器人表现不对

  • 先看 launch、namespace、remap、参数
  • 再看 topic 名称、频率、时间戳、单位、frame_id
  • 再看 TF 是否单根、静态 / 动态是否分清
  • 最后再怀疑算法本身

Nav2 / ros2_control

  • Navigation2 优先排 /cmd_vel 链路、定位质量、控制频率和底盘单位
  • ros2_control 优先排硬件接口类型、控制器 YAML、读写周期和 active 状态

MCU / micro-ROS

  • 先判断 Linux 侧桥接节点是否足够
  • 若 MCU 必须进入 ROS 2 图,再考虑 micro-ROS
  • 严格检查字节序、校验、超时、重连、时间源和失连保护

反模式

  • 同一个包混写 catkin 与 ament
  • 只改 CMakeLists.txt 不改 package.xml
  • 忽略 TF、时间戳、单位、坐标轴定义
  • 把驱动、协议、滤波、控制、诊断全塞进一个大节点
  • 迁移 ROS 1 → ROS 2 时只做 API 替换,不做系统验证

输出要求

使用本 skill 处理任务时,优先输出:

  1. 当前工作区和包类型判断
  2. 关键依据与假设
  3. 修改点与原因
  4. 最小构建 / 测试 / 运行命令
  5. 风险点与回归检查点

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ros Robotics AI skill do?

适用于 ROS 1 / ROS 2 机器人开发、迁移与联调的 skill。覆盖 catkin/catkin_tools、colcon/ament、package.xml、CMakeLists.txt、launch、URDF/Xacro、RViz、TF、Navigation2、ros2_control、串口/CAN、MCU 与 micro-ROS 等常见工程场景。

Why use Ros Robotics on TypingMind?

Because you install it once and use it with any model. Ros Robotics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ros Robotics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wzyn20051216/ros-robotics-skill/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ros Robotics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ros Robotics?

As many as you like. As long as a model supports skills, you can use Ros Robotics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ros Robotics AI skill free?

Yes. It is published on GitHub by wzyn20051216 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇