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robium-airobium-ai/robium/skills/rerun/SKILL.md

rerun

Skill93 starsChanged 3 months agoMITView on GitHub

Visualize robot and machine-learning data with Rerun.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use a spawned viewer for short local exploration with a real desktop.

    line 13
  2. Distinctive:

    Use an explicit sequence or time timeline for episodes and rollouts.

    line 25
  3. Distinctive:

    Check the current Rerun documentation and examples before choosing archetypes or calling APIs.

    line 29
  4. Distinctive:

    When the boundary is live ROS topics, use RViz2 or Foxglove.

    line 41

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Rerun

Choose the data sink first. Where and when the viewer must run determines how the application should log.

Choose the viewing shape

  • Use a spawned viewer for short local exploration with a real desktop.
  • Stream to a separate viewer or serve one when the producer is remote or headless.
  • Save an .rrd when the run must be inspectable later, reproducible in CI, or shared without keeping the producer alive.
  • Embed a viewer only when it is part of the actual application experience; otherwise keep visualization out of the service boundary.

Make the recording understandable

  • Organize entity paths around the robot, sensors, policy, and world so related data shares a useful hierarchy.
  • Use an explicit sequence or time timeline for episodes and rollouts. Log static geometry once and changing state at the corresponding step.
  • Align transforms, images, actions, predictions, and outcomes on the same time basis before interpreting what the viewer shows.
  • Check the current Rerun documentation and examples before choosing archetypes or calling APIs. Names and signatures change between releases.

Go deeper only when needed

  • For local, remote, file, and browser modes plus their common failures, read OPERATING-MODES.md.
  • For the Robium-tested Gradio streaming pattern and dependency conflict, read GRADIO.md.
  • Let LeRobot's dataset visualizer produce the recording when inspecting a LeRobot episode; use LeRobot guidance for its current command.
  • When the boundary is live ROS topics, use RViz2 or Foxglove. Use the visualization skill only when the tool itself has not been chosen.

Done

  • The chosen sink works in the target environment, related data is aligned and navigable, and a saved or replayable artifact exists when the observation matters beyond the live session.

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