Data visualization

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

Design clear, accessible data visualizations with appropriate chart selection and styling.From its SKILL.md

Install

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

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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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 1 of the 12 instructions most data analysis skills give in 377 tokens

Counted across 230 of the 242 authors here whose files we hold, read 2026-09-06

Propose a regression test for each fixed bugin 16 of 230, across 12 files
Name tests after the bug they preventin 14 of 230, across 10 files
Test the API response shape, not the implementationin 14 of 230, across 10 files
Run the test suite before any code reviewin 14 of 230, across 10 files
Force sandbox mode in the test setupin 14 of 230, across 10 files
Write regression tests only for bugs already foundin 14 of 230, across 10 files
Assert sandbox and production paths return the same shapein 14 of 230, across 10 files
Clear stale state when setting an errorin 13 of 230, across 9 files
Keep the whole test suite under one secondin 10 of 230, across 6 files
Run the build type check before code reviewin 10 of 230, across 6 files
Use vectorized operations instead of row iterationin 9 of 230, across 6 files
Start bar chart Y-axes at zerohere, and in 8 of 230, across 7 files

Said here and by no other author read

Choose the simplest chart that communicates the insight
Label directly on the chart when possible
Use consistent color encoding across views
Provide text alternatives for charts
Provide context with benchmarks, targets, or trends
Test with real data, not idealized samples

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.