Data Visualization Principles Skill

🔴 AI FIRST Quality Principle

Apply the AI FIRST principle: never accept first-pass quality. Minimum 2 iterations. Read all output, improve every section. No shortcuts.

Purpose

Establishes principles for creating effective, accessible, and honest data visualizations for political intelligence data.

Chart Selection Guide

Data Type	Recommended Chart
Comparison	Bar chart, grouped bar
Trend over time	Line chart, area chart
Part-to-whole	Pie/donut, stacked bar
Distribution	Histogram, box plot
Correlation	Scatter plot
Relationships	Network diagram, force graph
Geographic	Choropleth map
Hierarchical	Treemap, sunburst

Design Principles

1. Data-ink ratio — Maximize data, minimize decoration
2. Clarity — Clear labels, legends, and titles
3. Honesty — No misleading scales or truncated axes
4. Accessibility — WCAG 2.1 AA compliant colors and patterns
5. Responsiveness — Adapt to screen sizes
6. Interactivity — Tooltips, zoom, filter where appropriate

Color Guidelines

• Use colorblind-safe palettes
• Maintain 4.5:1 contrast ratio for text
• Use patterns/textures as secondary encoding
• Follow cyberpunk theme variables
• Limit to 7±2 colors per chart

Political Data Considerations

• Show confidence intervals for predictions
• Include data source attribution
• Indicate data freshness/staleness
• Support party-specific color coding
• Handle missing data transparently

Performance

• Canvas for large datasets (>1000 points)
• SVG for interactive/accessible charts
• Lazy load charts below the fold
• Optimize re-renders on data updates

Related Policies

• Secure Development Policy