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.
Domain knowledge for Physical AI, ROS 2, and Humanoid Robotics.
Teams using robotics-subject-expert should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
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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