robotics-subject-expert
Domain knowledge for Physical AI, ROS 2, and Humanoid Robotics.
Best use case
robotics-subject-expert is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Domain knowledge for Physical AI, ROS 2, and Humanoid Robotics.
Domain knowledge for Physical AI, ROS 2, and Humanoid Robotics.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "robotics-subject-expert" skill to help with this workflow task. Context: Domain knowledge for Physical AI, ROS 2, and Humanoid Robotics.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/robotics-subject-expert/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How robotics-subject-expert Compares
| Feature / Agent | robotics-subject-expert | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Domain knowledge for Physical AI, ROS 2, and Humanoid Robotics.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Robotics & Physical AI Domain Knowledge ## Core Curriculum The project covers **Embodied Intelligence**: the intersection of AI brain and Robot body. ### Key Technologies - **ROS 2 (Robot Operating System)**: The middleware backbone. Version: **Humble Hawksbill**. - **Isaac Sim**: NVIDIA's photorealistic simulation environment for training robots. - **Gazebo**: Open-source 3D robotics simulator. - **Python/C++**: Primary languages for robotics control. ### Concepts - **Inverse Kinematics (IK)**: Calculating joint angles to reach a point. - **SLAM**: Simultaneous Localization and Mapping. - **Reinforcement Learning (RL)**: Training robots via trial and error (Sim-to-Real). - **Computer Vision**: Object detection, segmentation for robot perception. ## Textbook Structure - **Modules**: Organized by topic (e.g., `module1-ros2`). - **Labs**: Hands-on exercises (`labs/week1-exercise1`). - **Capstone**: Final integrated project (`capstone/project-overview`). ## Role As the **Robotics Agent**, you should explain these concepts clearly, provide code examples in ROS 2 (Python/C++), and help troubleshoot simulation issues.
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