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.
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-visualizationSKILL.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
Design an A/B experiment — hypothesis, variants, primary metric, and sample size. Use when a change can be measured quantitatively at scale. For observing behaviour qualitatively, use `test-scenario`.
Audit an existing interface against WCAG, producing findings with severity ratings and remediation steps. Use when you have a design or build to assess now. Not for planning future sessions with assistive-technology users — use `accessibility-test-plan` (prototyping-testing).
Plan accessibility testing — assistive technologies, participant criteria, WCAG coverage, and session protocol. Use when scheduling testing with real AT users. Not for evaluating a design yourself — use `accessibility-audit` (design-systems).
Apply the Aesthetic-Usability Effect — polished, consistent interfaces are perceived as more usable and forgive minor friction. Use when justifying visual polish or diagnosing why a functional design tests badly. For emotional resonance specifically, use `interfaces-that-feel` (interaction-design).
Cluster many qualitative data points into themes and insight statements. Use when synthesising across multiple sessions or sources. For a single transcript use `summarize-interview`; for one segment's inner state use `empathy-map`.
Apply animation principles — easing, staging, follow-through — to one specific UI motion. Use when tuning how an animation feels. For product-wide duration and easing tokens use `motion-system` (design-systems); for a full interaction spec use `micro-interaction-spec`.
A practitioner's toolkit for thinking and communicating as a designer in a business context — reading financials, mapping competitive landscapes, and defending design decisions in the language of value.
Analyse open or closed card sort results into a proposed grouping and label set. Use after running a sort study. For turning that evidence into a full structure, use `information-architecture` (ux-strategy).