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visualization-builder

Design
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Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.

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How to use this skill

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Source SKILL.md: https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/04-data-storytelling-visualization/visualization-builder/SKILL.md

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Visualization Builder

When to use

  • Choosing the right chart type for a specific analytical message
  • A chart exists but is cluttered, misleading, or failing to make the point
  • Building a chart for an executive presentation that must work without verbal explanation
  • Producing consistent, branded visualisations across a report or dashboard
  • Creating accessible charts that work for colorblind viewers or screen readers

Process

  1. Identify the message type — classify the chart's purpose: comparison (bar), trend over time (line), composition / part-of-whole (stacked bar, pie only for 2–3 categories), distribution (histogram, box plot), or relationship (scatter). The message type determines the chart type. See references/chart_selection_guide.md.
  2. Select and load the data — confirm the data is at the right grain for the chart. Aggregations (e.g., groupby month) should happen before plotting, not inside the chart library.
  3. Build the base chart — use scripts/chart_builder.py with pre-set professional styling (whitegrid, sans-serif, accessible color palette). Set axes, ticks, and scale deliberately — default settings are often wrong.
  4. Apply visual hierarchy — make the most important data element visually dominant (bolder line, darker bar, distinct color). De-emphasise secondary series. Remove every element that doesn't contribute to the message (gridlines at 0.2 alpha, no top/right spines). See references/visual_design_principles.md.
  5. Annotate for the reader — add a descriptive title that states the finding ("Mobile churn is 2× desktop"), not the variable names ("Churn by device type"). Annotate key data points, thresholds, and reference lines directly on the chart. Add a data source and date.
  6. Export and validate — export at 300 DPI for print or 150 DPI for web. View the chart at the intended display size. Check: is the key message legible in under 5 seconds? Does it work in greyscale? Complete assets/viz_spec_template.md if the chart is part of a larger deliverable.

Inputs the skill needs

  • The data to be visualised (at the correct aggregation grain)
  • The single key message the chart must communicate
  • The audience (technical or executive) and the display context (presentation slide, report, dashboard, email)
  • Brand colors or style guidelines if applicable
  • Any accessibility requirements (colorblind palette, alt text)

Output

  • scripts/chart_builder.py — creates professional matplotlib/seaborn charts with pre-set styling, annotation helpers, and export settings
  • references/chart_selection_guide.md — which chart type for which message; common chart mistakes and how to fix them
  • references/visual_design_principles.md — color, typography, hierarchy, annotation, and accessibility principles
  • assets/viz_spec_template.md — spec template for a chart: message, data source, chart type, annotations, export requirements