Claude Cursor Skill

visualization

Choose between RViz2, Foxglove, Lichtblick, and Rerun to inspect robot behavior.

LLM Mart · 0 points · 0 views 0 listing impressions 0 install-command copies
Virus-scanned Reviewed automatically before listing.

Full trust report

Download robium-ai-robium-skills_visualization-498ea4e.zip · 1 KB
Part of robium-ai/robium — 44 skills

Install

skills CLI npx skills add https://github.com/robium-ai/robium/tree/main/skills/visualization
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install robium-ai-robium@llmmart
Git git clone https://github.com/robium-ai/robium.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole robium-ai/robium collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Visualization

Choose the view from the evidence you need and where it must be viewed. The viewer is part of the debugging loop, not a decorative dashboard.

Choose the path

  • RViz2: local Linux desktop, live ROS 2 graph, and frame- or message-specific debugging.
  • Foxglove or Lichtblick: remote/headless ROS systems, browser access, shareable layouts, and MCAP playback.
  • Rerun: ML rollouts, perception, arbitrary tensors, and custom non-ROS pipelines.
  • Mixed systems may use two views: a ROS viewer for robot state and Rerun for policy inputs and outputs. Choose per evidence boundary, not per project.

Use the matching rviz2, foxglove, or rerun skill once the path is clear. Use environments only if the unresolved question is where the viewer or bridge can run.

Decide live versus recorded

  • Use live viewing to interact and form a hypothesis.
  • Record before a run when it must be replayed after a crash, compared across versions, or shared with someone who is not present.
  • Scope recordings to the evidence needed, but include enough context to explain timing and transforms.
  • Save the layout or blueprint that makes the recording intelligible.

Always expose useful evidence

  • Frames are connected and current.
  • Sensor values and rates are plausible, not merely nonzero.
  • Commands can be compared with robot response.
  • For navigation, show raw sensors alongside costmaps, plan, footprint, and pose.
  • For a learned policy, align observations, actions, and resulting trajectory on one timeline.

Done

  • The selected tool works in the actual local, remote, or headless context.
  • The view distinguishes inputs, decisions, and outcomes.
  • A recording exists when later comparison or handoff matters.
  • Tool-specific setup is delegated to current upstream documentation and the matching skill.
Files (robium)
  • SKILL.md 1.9 KB
    ---
    name: visualization
    description: Choose between RViz2, Foxglove, Lichtblick, and Rerun to inspect robot behavior.
    ---
    
    # Visualization
    
    Choose the view from the evidence you need and where it must be viewed. The
    viewer is part of the debugging loop, not a decorative dashboard.
    
    ## Choose the path
    
    - **RViz2:** local Linux desktop, live ROS 2 graph, and frame- or
      message-specific debugging.
    - **Foxglove or Lichtblick:** remote/headless ROS systems, browser access,
      shareable layouts, and MCAP playback.
    - **Rerun:** ML rollouts, perception, arbitrary tensors, and custom non-ROS
      pipelines.
    - Mixed systems may use two views: a ROS viewer for robot state and Rerun for
      policy inputs and outputs. Choose per evidence boundary, not per project.
    
    Use the matching `rviz2`, `foxglove`, or `rerun` skill once the path is clear.
    Use `environments` only if the unresolved question is where the viewer or
    bridge can run.
    
    ## Decide live versus recorded
    
    - Use live viewing to interact and form a hypothesis.
    - Record before a run when it must be replayed after a crash, compared across
      versions, or shared with someone who is not present.
    - Scope recordings to the evidence needed, but include enough context to
      explain timing and transforms.
    - Save the layout or blueprint that makes the recording intelligible.
    
    ## Always expose useful evidence
    
    - Frames are connected and current.
    - Sensor values and rates are plausible, not merely nonzero.
    - Commands can be compared with robot response.
    - For navigation, show raw sensors alongside costmaps, plan, footprint, and
      pose.
    - For a learned policy, align observations, actions, and resulting trajectory
      on one timeline.
    
    ## Done
    
    - The selected tool works in the actual local, remote, or headless context.
    - The view distinguishes inputs, decisions, and outcomes.
    - A recording exists when later comparison or handoff matters.
    - Tool-specific setup is delegated to current upstream documentation and the
      matching skill.
    

Comments (0)

Sign in to join the conversation.

No comments yet.

Reviews (0)

No reviews yet.

Related