Color Strategy for Data Viz AI Prompt

Design a color strategy for this data visualization or dashboard.

Visualization type: {{viz_type}}
Data encoding needs: {{encoding_needs}} (categorical groups, sequential scale, diverging scale, highlighting)
Brand colors: {{brand_colors}}
Accessibility requirement: {{accessibility}} (must pass WCAG AA color contrast, colorblind-safe)

1. Choose the right color palette type:

   CATEGORICAL (for distinguishing unordered groups):
   - Max 8 colors (human perceptual limit for distinct hues)
   - Colors should be equally distinct — avoid mixing light and dark versions of similar hues
   - Recommended: ColorBrewer Qualitative, Okabe-Ito (colorblind-safe), Tableau 10
   - Primary choice for: bar charts with multiple categories, multi-line charts, map choropleth regions

   SEQUENTIAL (for ordered numeric data, low to high):
   - Single hue: light (low values) → dark (high values)
   - Or perceptually uniform multi-hue: e.g. yellow → green → blue
   - Avoid: rainbow (jet) colormap — perceptually non-uniform, not colorblind-safe
   - Primary choice for: heatmaps, choropleth maps, scatter plots with continuous color encoding

   DIVERGING (for data with a meaningful midpoint):
   - Two hues diverging from a neutral center: e.g. blue — white — red
   - Center (zero or baseline) must be a neutral color (white, light grey)
   - Primary choice for: correlation matrices, market share vs benchmark, profit/loss maps

   HIGHLIGHT (single color to draw attention):
   - Use one accent color for the key data point or series; make everything else grey
   - Most powerful technique for directing viewer attention

2. Colorblind accessibility:
   Approximately 8% of males have red-green color blindness (deuteranopia/protanopia).
   - NEVER rely on red vs green alone to encode meaning (e.g. positive vs negative)
   - Safe alternatives for red/green distinction: use blue vs orange, or add symbols/patterns
   - Test: convert the palette to greyscale — can values still be distinguished?
   - Use: Okabe-Ito palette, Viridis, Cividis — these are designed to be distinguishable by all
   - Tool: Sim Daltonism, Color Oracle, or Coblis for simulating colorblind views

3. Color contrast (WCAG AA):
   - Text on colored background: minimum contrast ratio 4.5:1 (3:1 for large text)
   - Data elements (bars, lines, dots): minimum 3:1 contrast ratio against the background
   - Test using: WebAIM Contrast Checker or Figma plugins

4. Brand color integration:
   - Use the primary brand color for the most important data series only
   - Avoid using brand colors if they are not colorblind-safe (common for red-dominant brands)
   - Build a secondary palette around the brand primary using ColorBrewer rules

5. Color encoding rules:
   - Encode one thing at a time: don't use color AND size AND shape all for different dimensions
   - Be consistent: the same color always means the same thing across the dashboard
   - Don't use color for ordinal data: use sequential shades, not arbitrary colors

Return: palette specification (hex codes), palette type with rationale, colorblind test plan, contrast check, and usage rules.