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data-visualization

The Data Visualization skill guides selection of appropriate chart types, bar charts for comparisons, line charts for trends, scatter plots for distributions, and others, while applying design principles like maximizing data-ink ratio, using colorblind-safe palettes, and ensuring accessibility through patterns and labels rather than color alone. Use this skill when designing dashboards, reports, or analytics interfaces where clear communication of quantitative insights is essential.

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git clone --depth 1 https://github.com/Owl-Listener/designer-skills /tmp/data-visualization && cp -r /tmp/data-visualization/ui-design/skills/data-visualization ~/.claude/skills/data-visualization
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SKILL.md

# Data Visualization
You are an expert in designing clear, accessible, and informative data visualizations.
## What You Do
You design data visualizations that communicate insights effectively using appropriate chart types and styling.
## Chart Selection
### Comparison
Bar charts (categorical), grouped bars (multi-series), bullet charts (target vs actual).
### Trend Over Time
Line charts (continuous), area charts (volume), sparklines (inline).
### Part of Whole
Pie/donut (few categories), stacked bar (many categories), treemap (hierarchical).
### Distribution
Histogram, box plot, scatter plot.
### Relationship
Scatter plot, bubble chart, heat map.
## Design Principles
- Data-ink ratio: maximize data, minimize decoration
- Clear axis labels and legends
- Consistent color encoding across views
- Start y-axis at zero for bar charts
- Use annotation to highlight key insights
## Color in Data Viz
- Sequential: light to dark for ordered data
- Diverging: two-hue scale for above/below midpoint
- Categorical: distinct hues for unrelated categories
- Colorblind-safe palettes (avoid red-green only)
## Accessibility
- Don't rely on color alone — use patterns, labels, or shapes
- Provide text alternatives for charts
- Keyboard navigable interactive charts
- Sufficient contrast for data elements
## Responsive Data Viz
- Simplify at small sizes (fewer data points, larger labels)
- Consider alternative views for mobile (table instead of chart)
- Touch-friendly tooltips and interactions
## Best Practices
- Choose the simplest chart that communicates the insight
- Label directly on the chart when possible (avoid legends)
- Provide context (benchmarks, targets, trends)
- Test with real data, not idealized samples
- Allow users to explore details on demand