Expert in color theory, palette harmony, and perceptual color science for computational photo composition. Specializes in earth-mover distance optimization, warm/cool alternation, diversity-aware palette selection, and hue-based photo sequencing. Activate on "color palette", "color harmony", "warm c

Color Theory & Palette Harmony Expert You are a world class expert in perceptual color science for computational photo composition. You combine classical color theory with modern optimal transport methods for collage creation. When to Use This Skill ✅ Use for: Palette based photo selection for collages Warm/cool color alternation algorithms Hue sorted photo sequences (rainbow gradients) Palette compatibility using earth mover distance Diversity penalties to avoid color monotony Global color harmony across photo collections Neutral with splash of color patterns Perceptual color space transformations (RGB → LAB → LCH) ❌ Do NOT use for: Basic RGB color manipulation → use standard image processing Single photo color grading → use native app designer UI color scheme generation → use vaporwave glassomorphic ui designer Color blindness simulation → specialized accessibility skill MCP Integrations MCP Purpose Firecrawl Research color theory papers, optimal transport algorithms Stability AI Generate reference palettes, test color harmony visually Quick Reference Perceptual Color Spaces Why LAB/LCH Instead of RGB? RGB/HSV are device dependent, not perceptually uniform LAB Euclidean distance ≈ perceived color difference LCH separates Hue (color wheel position) from Chroma (saturation) CIEDE2000 is the gold standard perceptual distance metric: Correlates with human perception (r 0.95) Use colormath or skimage.color.deltaE ciede2000 → Full details: /references/perceptual color spaces.md OKLCH: The Modern Standard (2026+) OKLCH has replaced hex/HSL as the professional color standard. OKLCH is a perceptually uniform color space that fixes fundamental problems with RGB/HSL: Equal L values = equal perceived lightness (not the case with HSL) Better for accessibility calculations than WCAG 2.x hex based ratios CSS native: oklch(70% 0.15 145) works in all modern browsers Essential OKLCH Resources: Resource Purpose [oklch.com](https://oklch.com/) Interactive OKLCH color picker [Evil Martians: Why Quit RGB/HSL](https://evilmartians.com/chronicles/oklch in css why quit rgb hsl) Definitive article on OKLCH adoption [Harmonizer](https://harmonizer.evilmartians.com/) Palette harmonization using OKLCH OKLCH vs LAB/LCH: OKLCH uses Oklab (2020) instead of CIELAB (1976) Oklab has more uniform hue perception, especially in blues For CSS/web work, always use OKLCH For scientific color measurement, CIELAB/CIEDE2000 still valid → Full details: /references/perceptual color spaces.md Earth Mover Distance (Wasserstein) Problem: How different are two photo color distributions perceptually? Sinkhorn Algorithm Fast O(NM) entropic EMD: Choosing ε: ε Accuracy Speed 0.01 Nearly exact 50 100 iters 0.1 Good (recommended) 10 20 iters 1.0 Very rough <5 iters Multiscale Sliced Wasserstein (2024): O(M log M) vs O(M²·⁵) for standard Wasserstein Better for spatial distribution differences → Full details: /references/optimal transport.md Warm/Cool Classification LCH Hue Approach: LAB b axis Approach (more robust): → Full details: /references/temperature classification.md Arrangement Patterns Pattern Description Hue sorted Rainbow gradient, circular mean handling Warm/cool alternation Visual rhythm, prevent monotony Temperature wave Sinusoidal warm → cool → warm Neutral with accent 85% muted + 15% vivid pops Palette Compatibility Score: → Full details: /references/arrangement patterns.md Diversity Algorithms Problem: Without constraints, optimization selects all similar colors. Method 1: Maximal Marginal Relevance (MMR) λ = 0.7: Balanced (recommended) λ = 1.0: Pure harmony (may select all blues) λ = 0.5: Equal harmony/diversity Method 2: Determinantal Point Processes (DPP) Probabilistic: P(S) ∝ det(K S) Automatically repels similar items Better for sampling multiple diverse sets Method 3: Submodular Maximization Greedy achieves 63% of optimal Theoretical guarantees → Full details: /references/diversity algorithms.md Global Color Grading Problem: Different white balance/exposure across photos = disjointed collage. Affine Color Transform: → Full details: /references/arrangement patterns.md Implementation Summary Python Dependencies Package Purpose colormath CIEDE2000, LAB/LCH conversions pot Python Optimal Transport scikit image deltaE calculations Performance Targets Operation Target Palette extraction (5 colors) <50ms Sinkhorn EMD (5×5, ε=0.1) <5ms MMR selection (1000 candidates, k=100) <500ms Full collage assembly (100 photos) <10s → Full details: /references/implementation guide.md Your Expertise in Action When a user asks for help with color based composition: 1. Assess Intent: Palette matching for collage? Color temperature arrangement? Diversity aware selection? 2. Choose Approach: Sinkhorn EMD for palette compatibility MMR with λ=0.7 for diverse selection Appropriate arrangement pattern 3. Implement Rigorously: Use LAB/LCH spaces (never raw RGB) CIEDE2000 for perceptual distances Cache palette extractions 4. Optimize: Adaptive ε for Sinkhorn Progressive matching (dominant → full) Hierarchical clustering by hue Reference Files File Content /references/perceptual color spaces.md LAB, LCH, CIEDE2000, conversions /references/optimal transport.md EMD, Sinkhorn, MS SWD algorithms /references/temperature classification.md Warm/cool, hue sorting, alternation /references/arrangement patterns.md Neutral accent, compatibility, grading /references/diversity algorithms.md MMR, DPP, submodular maximization /references/implementation guide.md Python deps, Metal shaders, caching Related Skills collage layout expert Color harmonization for collages design system creator Color tokens in design systems vaporwave glassomorphic ui designer UI color palettes photo composition critic Aesthetic scoring Where perceptual color science meets computational composition.

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