1.0.0
Teach your coding agent robotics — 22 versioned, battle-tested skills for ROS 2, Gazebo, Nav2, LeRobot, Isaac Sim, MuJoCo and more. npx robium-ai install
npx -y skills add robium-ai/robium-plugin --skill 1.0.0Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
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Choose and apply robotics visualization: selection guidance for rviz2 vs Foxglove vs Rerun, plus best practices — what to visualize at each dev stage, live vs recorded, local vs remote. Use when: 'visualize', 'see what the robot sees', 'debug visually', 'plot the trajectory', 'dashboard for the robot', choosing a viz tool, or recording data for later inspection. Umbrella skill — after selecting, load the matching tool skill: rviz2 (ROS-native debugging), foxglove (remote/web + MCAP recording), rerun (ML/data-centric logging). Not for: tool-specific how-to (the per-tool skills).
SKILL.md
8.2 KB, ~1.9k tokens by cl100k_base, as published. Nobody here has run it
visualization
The visualization-selection umbrella for robium. "I can't see what the robot is doing" is one of the fastest ways a debugging session stalls, and picking the wrong tool for the context (a local-display tool on a headless server, a ROS-native tool for a non-ROS ML pipeline) wastes more time than the debugging itself. This skill decides which of rviz2, Foxglove, or Rerun fits a given context, and states the cross-cutting practice of what to visualize at each build stage. It does not teach any one tool's UI, panels, or API — that is each tool's own skill.
When to use this skill
- A viz tool hasn't been chosen yet for the current context (local desktop, headless remote server, or an ML/data pipeline outside ROS).
- The trigger phrases in the description: 'visualize', 'see what the robot sees', 'debug visually', 'plot the trajectory', 'dashboard for the robot'.
- Deciding whether to look at data live or record it for later inspection, before wiring up either path.
- Cross-references — go straight to the tool skill, skipping this one, once
the tool is already chosen and the question is tool-specific:
- RViz2 panel configuration, displays, or plugins on a local Linux desktop →
rviz2. - Foxglove layouts, the Foxglove Bridge, or MCAP recording/playback →
foxglove. - Rerun logging API, blueprints, or timeline views for ML/perception data →
rerun. - Whether headless/remote is the deployment target at all (as opposed to
which viz tool to use once it is) →
environments. - The whole-stack decision this feeds into →
architect(routes here).
- RViz2 panel configuration, displays, or plugins on a local Linux desktop →
Key directives
- Delegation posture: route + embed the decision logic. The selection (which tool, for which context) and the always-visualize checklist live here; every tool's actual usage lives in its own skill. Never re-teach rviz2/Foxglove/Rerun mechanics in this skill — link to the tool skill.
- Context picks the tool, not preference. Local ROS desktop debugging, remote/headless or cross-team sharing, and ML-centric rollout inspection each have a different right answer — see the selection table in Decision guidance. Don't default to whichever tool was used last on an unrelated project.
- Decide live vs recorded up front. Live viewing is enough for interactive
debugging in the moment; anything that needs to be compared across runs,
shared with someone not present, or replayed after a crash needs a recording
path (rosbag2/MCAP or a Rerun
.rrdrecording) planned before the run happens, not reconstructed after the fact from logs. - Visualize something concrete at every dev stage, not just "no errors." A stack that builds and runs with no crashes can still be silently wrong — see the always-visualize checklist below. Absence of an exception is not evidence of correct behavior.
- Remote/headless defaults to a web-based tool. Per
environments' general headless-first guidance, don't reach for X11/Wayland display forwarding as the default remote-viz path — see Decision guidance.
Quick start
1. Answer two questions: (a) is this ROS 2 data or ML/custom pipeline data, and (b) is the viewer local-with-a-display or remote/headless/shared? These two answers select the tool — see the table in Decision guidance.
2. Load the matching tool skill (rviz2, foxglove, or rerun) for the
actual setup and usage.
3. Decide live vs recorded for this session (see Key directives) before starting the run, and pick the recording mechanism the chosen tool skill documents if recording is needed.
4. Before calling any stage "working," walk the always-visualize checklist below — don't rely on "it ran without errors."
Decision guidance
Selection table by context:
| Context | Recommended | Why |
|---|---|---|
| ROS 2, local Linux desktop, interactive debugging | rviz2 | ROS-native, zero extra infra, richest ROS message-type support out of the box. |
| Remote/headless server, cross-platform team, or need to share a view/recording | foxglove | Web-based — works over SSH/remote with no display; MCAP recording is a first-class, shareable artifact. |
| ML rollouts, custom (non-ROS) data pipelines, perception/policy debugging with arbitrary tensors/embeddings | rerun | Built for data-centric, timeline-based logging outside the ROS message-type world; the tool lerobot itself wraps for episode visualization. |
| ROS 2 data, but the viewer is remote or the team is mixed-platform | foxglove | Same ROS data, but rviz2 doesn't work headless — Foxglove Bridge exposes ROS topics to the web client instead. |
When a project spans both worlds (a ROS 2 robot driving a learned policy),
it's normal to use more than one: rviz2/foxglove for the ROS-side state
(TF, costmaps, sensor topics) and rerun for the policy's own inputs/outputs —
pick per-concern, not one tool for the whole project.
Always-visualize checklist (walk this at every dev stage before declaring it working):
- TF tree — every expected frame is present and connected (no gaps
between
map/odom/base_link/sensor frames); a broken TF chain silently breaks anything that depends on it (Nav2, a perception node) without a visible error elsewhere. - Sensor rates — each sensor topic is publishing at its expected rate, not just "publishing at all"; a lidar or camera silently dropping to a fraction of its configured rate degrades downstream behavior without an obvious error.
- Costmaps / policy actions — for a navigation stack, the costmap layers look sane for the environment (obstacles where they should be, no phantom inflation); for a learned policy, the actual actions/trajectory overlaid on the scene look purposeful, not just "the eval script exited 0."
Platform gotchas
- rviz2 needs a local display. It is a standard Qt/OpenGL desktop app with
no headless or web-remote mode — on a remote/headless server, don't try to
X11-forward it as the default; use
foxgloveinstead (see the selection table). - macOS has no native ROS 2, so
rviz2(which links against ROS 2) runs inside Docker on a Mac dev machine like the rest of the ROS 2 stack (seeenvironments' andros2's macOS gotcha) —foxglove's web client andrerun's viewer both run natively on macOS without that constraint.
Customization
- Switching tools mid-project: it's common to debug locally with
rviz2and switch tofoxgloveonly when demoing or handing off to a remote teammate — this doesn't require re-instrumenting the robot, since both consume the same ROS 2 topics; only the viewer changes. - Non-ROS projects: the selection table's ROS-specific rows don't apply;
rerunis the default for any custom data pipeline regardless of local vs remote, since its viewer already works both ways.
References
- Upstream: RViz2 documentation,
Foxglove documentation,
Rerun documentation,
MCAP format (the recording format
foxgloveand ROS 2 tooling both use). - Sibling skills:
rviz2(ROS-native local viz mechanics),foxglove(remote/web viz and MCAP recording mechanics),rerun(ML/data-centric logging mechanics),environments(headless/remote deployment decision this skill assumes is already made),lerobot(wrapsrerunfor episode visualization),architect(routes here).