---

name: visualization description: "Build and troubleshoot Mesa visualizations with SolaraViz, SpaceRenderer, component builders, portrayal styles, user params, and headless stack checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Visualization

Use this sub-skill when the task is to build, adapt, or debug a Mesa browser/notebook visualization rather than to change the model, space, or metric definitions.

Use this for

• SolaraViz / JupyterViz dashboards and component layout.
• SpaceRenderer with Matplotlib or Altair backends.
• make_space_component and make_plot_component component builders.
• AgentPortrayalStyle, PropertyLayerStyle, dynamic portrayals, and property-layer overlays.
• model_params widgets, reset-time model construction, and the optional command console.
• Safe headless import/signature checks with the bundled script.

Route elsewhere

• Model constructors, activation, step(), run_for(), and simulation semantics: ../model-core/SKILL.md.
• Grid/network/continuous space topology and property-layer creation: ../spaces/SKILL.md.
• DataCollector reporter definitions and experiment metric design: ../analysis-experiments/SKILL.md.

Start here

1. Check public signatures, style fields, backend choices, and the model_params reset contract in references/api-reference.md.
2. Use the authoring recipes in references/workflows.md for dashboards, dynamic portrayals, plots, property layers, custom components, and headless checks.
3. Diagnose import, browser, constructor, portrayal, plot, and backend failures with references/troubleshooting.md.
4. Run scripts/check_visualization_stack.py when you need a no-server, no-browser stack probe.

High-signal rules

• SolaraViz re-creates models with **model_parameters.value; every key in model_params must match a keyword accepted by the model constructor, a scenario field handled by the model, or **kwargs.
• Prefer SpaceRenderer for layered space structure, agents, property layers, and backend-specific styling. Use make_space_component for a lighter built-in space component.
• Match post_process to the backend: Matplotlib receives an Axes; Altair receives a Chart.
• Browser or Playwright end-to-end checks are optional. A baseline visualization readiness check should start with import/signature validation and safe component construction patterns.