dashboard-build

name	dashboard-build
description	Use this skill to build, implement, and test Vizro dashboards (Phase 2). Activate when the user wants to create a working app, says "just build it", or has data ready for implementation. Requires spec files from the dashboard-design skill (Phase 1), or user confirmation to skip design.

Prerequisites

Requires Phase 1 spec files from the dashboard-design skill: spec/1_information_architecture.yaml, spec/2_interaction_ux.yaml, and spec/3_visual_design.yaml. If these do not exist, ask the user whether to run Phase 1 first or proceed without specs.

Guidelines

• Use your native tools to understand the data well, especially if you build custom charts or when you use specific selectors.
• If the user asks for an example, simply copy the example app and run it. Do not include your own data or change the example.
• When executing any script mentioned below for the first time, it may take a while to install dependencies. Plan accordingly before taking any rash actions.
• When iterating on the dashboard after completing all steps, do not forget key points from below, especially regarding spec compliance and updating and terminal handling: always keep all specs up to date, and always check if terminal output is clean after each iteration.
• Execute all scripts from this skill, and the app.py you will create, with uv run <script_name>.py or uv run app.py - this will ensure you use the correct dependencies and versions.
• ABSOLUTELY NEVER type ANY commands (including sleep, echo, or anything else) in the terminal where the dashboard app is running, even if you started it with isBackground=true. This WILL kill the dashboard process. The dashboard startup takes time - be patient and let it run undisturbed.
• Step 2 (Testing) is critical — do not skip it. Use Playwright MCP if available, otherwise use any browser automation tool in your environment.

Spec Files: Documenting Decisions

IMPORTANT: Each step produces a spec file in the spec/ directory to document reasoning, enable collaboration, and allow resumption in future sessions. Create the spec/ directory if it is not already present at the root of the project.

Step 1: Build dashboard

1. You MUST ALWAYS copy the example app over, and modify it - this ensures less errors!
2. Investigate about the Vizro model by executing the schema fetching script. ALWAYS DO this for all models that you need - do NOT assume you know it. Execute the script like so: uv run ./scripts/get_model_json_schema.py <model_name> <model_name2> ... where <model_name> is the name of the model you want to get the schema for (prints the full JSON schema for each model to stdout). You can get an overview of what is available by calling the overview script like so: uv run ./scripts/get_overview_vizro_models.py (prints all available model names with one-line descriptions to stdout).
3. Build the dashboard config by changing the copied example app. Important: Very often normal plotly express charts will not suffice as they are too simple. In that case, refer to the custom charts guide to create more complex charts. These MUST be added to the correct section in the python app. Call the custom chart function from the Graph model in your dashboard app.
4. Run your dashboard app with uv run <your_dashboard_app>.py CRITICAL: After running this command, DO NOT run ANY other commands in that terminal. The dashboard takes time to start up (sometimes 10-30 seconds)
5. You MUST read the terminal to check for any errors, but do not put commands like sleep in it. Fix any warnings and even more important errors you encounter. ONLY once you see the dashboard running, inform the user. NEVER run any commands in that terminal after starting the dashboard.
6. When you iterate, no need to kill the dashboard, as we are using debug mode. Just save the file and it will reload automatically. Check the terminal occasionally for any failures. Once failed, you need to restart the dashboard.

Optimizations and common errors

• Colors: For Plotly charts and KPI cards, do not add colors in code — Vizro template defaults apply automatically. Only add chart colors if spec/3_visual_design.yaml defines color_decisions. For AG Grid cell styling (conditional formatting, heatmaps), use from vizro.themes import palettes, colors — never invent hex values. See selecting-vizro-charts skill.
• Data loading: For dashboards needing data refresh (databases, APIs) or performance optimization, see the data management guide for static vs dynamic data, caching, and best practices.
• KPI cards: Use built-in kpi_card / kpi_card_reference in Figure model only. Never rebuild as custom charts (exception: dynamic text). See selecting-vizro-charts skill.
• Interactions / actions: For cross-filter, cross-highlight, drill-through, or data export, load the wiring-vizro-actions skill and follow the interactions: block in spec/2_interaction_ux.yaml.

REQUIRED OUTPUT: spec/4_implementation.yaml

Save this file BEFORE proceeding to Step 2:

# spec/4_implementation.yaml
implementation:
  app_file: <name>.py
  data_files:
    - [list of data files used]
  data_type: static/dynamic  # static for DataFrames, dynamic for data_manager functions
  data_sources:
    - name: [data source name]
      type: csv/database/api/function
      caching: true/false
      refresh_strategy: [if dynamic: cache timeout or refresh trigger]

spec_compliance:
  followed_specs: true/false
  deviations:
    - spec_item: [What was specified]
      actual: [What was implemented]
      reason: [Why the deviation was necessary]

custom_charts:
  - name: [Function name]
    purpose: [What it does]

Validation Checklist

Before proceeding to Step 2, verify against spec files:

• All specs from spec/1_information_architecture.yaml, spec/2_interaction_ux.yaml and spec/3_visual_design.yaml are implemented if specs exist
• You have read the terminal output of the dashboard app for errors and warnings, you have not put any commands in the terminal after starting the app
• Any deviations are documented in spec/4_implementation.yaml

Step 2: Testing

This step is critical for producing bug-free dashboards. Do NOT skip it.

When conducting the below tests, go back to Step 1 to fix any issues you find, then come back here.

Automated Code Validation

Run these validation scripts against your app.py to catch common issues:

1. Color validation: uv run ./scripts/validate_colors.py . — Checks for hardcoded colors (color_discrete_map, hex codes, plot_bgcolor) that bypass Vizro theming. Fix any FAIL. If the user explicitly asked for custom colors, add --custom-colors-requested to skip app.py color checks.
2. Aggregation validation: uv run ./scripts/validate_aggregation.py . — Checks that bar/line charts use pre-aggregated data via @capture("graph"), not raw detail rows passed to inline px.bar/px.line. Fix any FAIL.

Browser Testing

Navigate the running dashboard to catch errors that code review alone cannot find: (1) console errors on launch, (2) callback errors on page navigation, and (3) callback 500s when actions fire — these only surface when the user actually clicks an interactive source, never on load.

1. Determine which browser automation tool is available:

   Playwright MCP tools available? → Use them directly to navigate, click pages, and check console No Playwright MCP? → Install the Python package: uv pip install playwright && uv run playwright install chromium, then write a test script

2. Using your chosen tool, perform these checks:

   • Navigate to dashboard URL (e.g., http://localhost:8050)
   • Click through all pages and check the browser console for errors
   • If app.py contains any actions= (cross-filter, cross-highlight, drill-through, export), exercise each one: grep your app for actions= and for every match, click the corresponding source (a point on the scatter, a bar, a row in the AgGrid, the export Button, etc.). Then check:
     • the browser console for Callback error updating ... messages
     • the network tab for any 500 response on _dash-update-component
     • the action's intended effect actually happened (URL updated for show_in_url=True, target filter changed, highlight applied, file downloaded, etc.)
   • Fix any errors found, then retest

Advanced Testing flow

1. Take a screenshot of each page, compare to specs and especially wireframes
2. Document any discrepancies

Important things to check:

• Line charts are readable, and not a mess due to lack of aggregation
• Graphs are legible and not squashed due to Layout

REQUIRED OUTPUT: spec/5_test_report.yaml

Save this file to complete the project:

# spec/5_test_report.yaml
testing:
  launch:
    successful: true/false
    url: http://localhost:8050
    errors: []

  navigation:
    all_pages_work: true/false
    issues: []

  console:
    no_errors: true/false
    errors_found: []

  screenshot_tests:
    performed: true/false
    pages_tested: []
    discrepancies:
      - page: [Page name]
        issue: [Description of visual issue]
        fixed: true/false
        notes: [Fix details or reason not fixed]

  requirements_met: true/false
  dashboard_ready: true/false

Done When

• Validation scripts (validate_colors.py, validate_aggregation.py) both PASS
• Dashboard launches without errors, no console errors, no callback errors on page navigation
• Every actions= in app.py has been clicked in the browser and produced its intended effect with no callback 500
• User confirms requirements are met
• All spec files from this Phase 2 saved in spec/ directory

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Reference Files

Reference	When to Load
selecting-vizro-charts skill	Colors, KPI cards, custom charts, Plotly conventions
writing-vizro-yaml skill	YAML syntax, component patterns, data_manager, pitfalls
wiring-vizro-actions skill	Cross-filter, cross-highlight, drill-through, export
data_management.md	Static vs dynamic data, caching, databases, APIs
custom_charts_guide.md	Implementing custom @capture("graph") charts
example_app.py	Starting template for dashboard implementation
validate_colors.py	Automated check for hardcoded colors in app.py
validate_aggregation.py	Automated check for pre-aggregation in bar/line charts