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Gazebo

Organization
robium-ai
gazebo

Build and debug modern Gazebo simulations and their ROS 2 boundary.

Overview

Publisherrobium-ai
Repositoryrobium
Skill namegazebo
Stars
104
Forks
2
Bundled files
6
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • 6 bundled files

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

  • Open source

    Published by robium-ai on GitHub. Read the source before you install it.

Installation

Install the Gazebo 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/robium-ai/robium/tree/main/skills/gazebo
  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/robium-ai/robium.git /tmp/robium
mkdir -p .claude/skills
cp -r /tmp/robium/skills/gazebo .claude/skills/gazebo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gazebo 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 Gazebo 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 Gazebo 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.

Gazebo

Treat a Gazebo simulation as a contract between world, robot, sensors, transport, and time. Find the first contract that is not producing believable evidence.

Start from the actual stack

  • For a new robotics app or first demo, read architect before creating worlds, launchers, or containers. Reuse its compatible reference-app selection if already made. Existing-world edits, simulator debugging, and explanations stay here.
  • Confirm the installed gz release, ROS distro, ros_gz pairing, render backend, and whether the run is graphical or headless.
  • When consuming an upstream simulator repository, select and pin an explicit branch or ref compatible with that ROS distro; default HEAD is not compatibility evidence.
  • Use modern Gazebo (gz). Gazebo Classic and libgazebo_ros_* tutorials are a different, end-of-life stack.
  • Keep upstream compatibility tables and SDF specifications as the authority for release-sensitive package names, tags, and plugin APIs.

Trace the simulation boundary

  • World: the selected world loads and the expected systems are present.
  • Robot: links, joints, frames, and drive plugins agree with the physical model and downstream interfaces.
  • Sensors: rates, fields of view, ranges, noise, frames, and timestamps model the intended hardware rather than tutorial defaults.
  • Transport: prove the Gazebo topic exists before debugging its ROS bridge.
  • Bridge: prefer reviewable YAML for a static bridge set. Use Python or XML launch-based parameter_bridge nodes when substitutions, grouping, or vendor conventions require them; reserve ad-hoc commands for diagnosis.
  • ROS: confirm /clock, commands, odometry, transforms, and sensor messages arrive with the expected direction and QoS.

Do not infer simulation correctness from a running process or visible GUI. A render-backed sensor may be silent even while physics continues.

Go deeper only when needed

  • For SDF worlds, models, drive plugins, spawning, and headless server mode, read worlds and models.
  • For lidar, camera, IMU, contact, noise, and rendering, read sensors.
  • For ros_gz_bridge, topic direction, types, QoS, /clock, and frame overrides, read the ROS 2 bridge guide.
  • For a silent world, sensor, bridge, or remote launch, read failures.
  • Use the bundled SDF and bridge YAML together only as a starting pair; keep their topic and frame names synchronized and verify syntax upstream.

Move to navigation only after the robot pose, sensor, map, and velocity interfaces are valid. Move to ros2 when Gazebo is publishing correctly and the fault is in the ROS graph. Use environments for container GPU/display setup and simulation only when the simulator itself has not been chosen.

Done

  • Physics time advances and the expected world and model are present.
  • Every required Gazebo topic has a matching, correctly directed ROS interface.
  • Sensor rate, frame, timestamp, and render output are believable.
  • The run works in its intended graphical or headless environment.
  • Release-specific claims match current Gazebo and ros_gz documentation.

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 Gazebo AI skill do?

Build and debug modern Gazebo simulations and their ROS 2 boundary.

Why use Gazebo on TypingMind?

Because you install it once and use it with any model. Gazebo 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 Gazebo in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/robium-ai/robium/tree/main/skills/gazebo. 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 Gazebo?

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 Gazebo?

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

Is the Gazebo AI skill free?

Yes. It is published on GitHub by robium-ai 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.

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