Data visualization

Skill Infrasity-Labs/dev-gtm-claude-skills/product-designers/data-visualization

Open-source Claude skills for GEO, AI discoverability, and developer GTM workflows. Built for developer-focused companies that want their documentation to be found, parsed, and cited by AI systems.

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npx -y skills add Infrasity-Labs/dev-gtm-claude-skills --skill data-visualization

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Design clear, accessible data visualizations with appropriate chart selection and styling.

SKILL.md

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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