From vizro-e2e-flow
Guides Vizro chart selection by data question, applies Plotly Express conventions, enforces color rules, builds KPI cards, and advises on custom functions.
How this skill is triggered — by the user, by Claude, or both
Slash command
/vizro-e2e-flow:selecting-vizro-chartsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
| Data question | Chart |
| Data question | Chart |
|---|---|
| Compare categories | Bar (horizontal preferred) |
| Trend over time | Line (12+ points) |
| Part-to-whole (simple) | Pie/donut (2–5 slices only) |
| Part-to-whole (complex) | Stacked bar |
| Distribution | Histogram or box |
| Correlation | Scatter |
Never use: 3D charts, pie with 6+ slices, dual Y-axis, bar charts not starting at zero.
app.py or custom chart functions.marker_color, hex codes, color_discrete_map, or color_discrete_sequence. This applies even for categories with apparent semantic meaning. Only override when the user explicitly asks.from vizro.themes import palettes, colors for cell styling.@capture("graph"))Use when: aggregation/sorting needed, update_layout()/update_traces() calls, reference lines, parameter-driven logic, dual-axis, multi-trace go.Figure(), shared legend control.
Standard px charts expressible via YAML args do not need custom functions. Full list: https://plotly.com/python-api-reference/plotly.express.html
kpi_card / kpi_card_reference from vizro.figures in Figure model._target_: kpi_card → title:), not on the component.Load chart-best-practices.md when you need: extended chart type decision tree, Plotly Express formatting conventions (100% stacked bar, axis/legend cleanup), palette/color names and use cases, accessibility rules, or detailed @capture("graph") guidance.
npx claudepluginhub mckinsey/vizro --plugin vizro-e2e-flowFull-stack data visualization intelligence: chart selection, data transformation, library choice, performance optimization, and accessibility. Activated by chart-related requests.
Designs clear, accessible data visualizations with chart selection for comparisons/trends/distributions, styling principles, color palettes, responsiveness, and best practices.
Guides data visualization design principles including chart selection, color encoding, annotation strategies, accessibility, and Tufte's data-ink ratio.