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
- Data-ink ratio — Maximize data, minimize decoration
- Clarity — Clear labels, legends, and titles
- Honesty — No misleading scales or truncated axes
- Accessibility — WCAG 2.1 AA compliant colors and patterns
- Responsiveness — Adapt to screen sizes
- 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