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
- michaelandrewgamble/workflows-conductortheir own version, first seen Oct 9, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- michaelandrewgamble/workflows-conductor★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository32 other files it gives its agents.
- AGENTS.mdAGENTS.md
- learnings/AGENTS.mdAGENTS.md
- skills/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- learnings/CLAUDE.mdCLAUDE.md
- skills/CLAUDE.mdCLAUDE.md
- app-publishingSkill
- architectSkill
- cloud-runSkill
- dataSkill
- environmentsSkill
- foxgloveSkill
- gazeboSkill
- gemini-roboticsSkill
- huggingfaceSkill
- integrationSkill
- isaac-labSkill
- isaac-simSkill
- learning-loopSkill
- lerobotSkill
- live-demoSkill
- miningSkill
- mujocoSkill
- navigationSkill
- ros2Skill
- runpodSkill
- rviz2Skill
- simulationSkill
- skill-authorSkill
- test-assetsSkill
- testingSkill
- visualizationSkill
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
- Distinctive: line 13
Use a spawned viewer for short local exploration with a real desktop.
- Distinctive: line 25
Use an explicit sequence or time timeline for episodes and rollouts.
- Distinctive: line 29
Check the current Rerun documentation and examples before choosing archetypes or calling APIs.
- Distinctive: line 41
When the boundary is live ROS topics, use RViz2 or Foxglove.
≈ “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.
Files
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
.rrdwhen 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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

