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Design · Data Analysis / AGENT SKILL
data-visualization
0
Choose chart types and design accessible data encodings, axes, and labels.
Select chart types and design data encodings — marks, axes, labels, and accessible chart styling. Use when presenting data graphically. Owns chart selection and encoding only; the categorical colour ramp itself belongs to `color-system`.
Understand the trade-offs.
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The full skill.
Original instructions from the publisher’s 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