---

name: visualization-workflows description: "Operate Kaolin visualization workflows for Timelapse USD checkpoints, Jupyter visualizers, Dash3D, GLTF inspection, and visualization troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

visualization-workflows

Use this sub-skill when the task is about showing, logging, inspecting, or troubleshooting Kaolin visualization outputs rather than creating the geometry or rendering algorithm itself.

Scope

Use this sub-skill for:

• Writing and validating kaolin.visualize.Timelapse checkpoint log directories.
• Reading Timelapse layout metadata with TimelapseParser.
• Starting or planning kaolin-dash3d / kaolin.experimental.dash3d safely.
• Building Jupyter/IPython visualization cells with quick_viz, IpyTurntableVisualizer, IpyFirstPersonVisualizer, ipycanvas, ipyevents, and ipywidgets.
• Composing GLTF interactive visualization notebooks after geometry loading and rendering functions already exist.
• Diagnosing missing USD/OpenUSD pxr, browser, Jupyter, ipycanvas, ipyevents, Flask, Tornado, or Matplotlib dependencies.

Route elsewhere when the request is primarily about:

• Mesh/point cloud/GLTF/USD data loading or representation conversion: use the geometry/IO sub-skill.
• Tensor operations, sampling, voxel/SPC conversion, metrics, or losses: use the ops/metrics sub-skill.
• Camera math, lighting, rasterization, differentiable rendering, nvdiffrast, or shader behavior: use the rendering/cameras/lighting sub-skill.
• Physics simulation loops or Simplicits output generation: use the physics sub-skill.

First steps for a visualization task

1. Identify the requested surface:
   • Timelapse log writer/parser
   • Dash3D web visualizer
   • Jupyter/IPython viewer
   • GLTF interactive notebook composition
   • dependency/server/browser troubleshooting
2. Check whether the input data already exists. Do not generate new geometry or implement rendering algorithms here; ask the appropriate owner to produce tensors, meshes, point clouds, cameras, or render functions.
3. Load only the smallest relevant bundled reference:
   • API reference for signatures and supported types.
   • Workflows for copyable task patterns.
   • Troubleshooting for dependency and runtime failures.
4. Prefer safe probes and dry-run planning before launching UI/server code. run_main() and kaolin-dash3d start a long-lived server loop.

Safe bundled helper

Use scripts/kaolin_dash3d_help.py when an automation needs to inspect Dash3D command arguments, check a Timelapse-style directory, or produce a launch command without starting an endless server.

Typical safe commands:

python scripts/kaolin_dash3d_help.py --help
python scripts/kaolin_dash3d_help.py --logdir ./viz --inspect-logdir
python scripts/kaolin_dash3d_help.py --check-imports

The helper intentionally does not start Dash3D. Start the real server only when a human or bounded supervisor is ready to stop it:

kaolin-dash3d --logdir=./timelapse-logdir --port=8080 --log_level=20

Verification guidance

Native verification candidates for this sub-skill are Timelapse writer/parser tests and Dash3D argument/help/parser checks. Notebook and browser UI cases are useful but optional and should not be run by default in headless automation.

Hard synthetic cases to keep supported:

• Verify a Timelapse log directory and produce the Dash3D launch command without starting a persistent server.
• Diagnose missing ipycanvas, ipyevents, Jupyter/browser, Matplotlib, Flask/Tornado, or pxr dependencies from symptoms and safe import probes.