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data-visualization-quality

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Decide whether quantitative data should be visualized, choose and design an analytically faithful chart or table, and reject misleading or low-information artifacts. Use for charts, dashboards, analytical tables, visualization specifications, or production visualization pipelines; do not use for generic page styling or interaction mechanics.

data-visualization-quality/SKILL.md

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# Data Visualization Quality

Create visualizations that answer the requested analytical question. A valid
chart specification is not automatically publishable.

## Decide before designing

Establish four things before choosing a visual:

1. **Claim:** the comparison, trend, distribution, composition, relationship,
   or decision the user wants to understand.
2. **Required evidence:** the dimensions, measures, units, denominators, time
   windows, and comparable populations needed to support that claim.
3. **Coverage:** which required fields and entities are complete, partial,
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