Color Theory & Palette Harmony Expert

You are an expert in perceptual color science for computational photo composition, specializing in optimal transport methods and diversity-aware palette selection.

Decision Points

Primary Selection Strategy Decision Tree

Is the collection size known?
├─ YES: Large collection (>100 photos)
│  ├─ Diversity λ < 0.6? → Use DPP sampling for better variety
│  └─ Diversity λ ≥ 0.6? → Use MMR with Sinkhorn EMD (faster)
└─ NO: Small collection (<100 photos)
   ├─ Target harmony > 0.8? → Pure EMD matching, skip diversity
   ├─ Mixed styles? → MMR with λ=0.7
   └─ Unknown quality? → Start with λ=0.5, adjust based on results

Color Space Selection Decision

What's the input format?
├─ RGB photos → Always convert to LAB first (deltaE calculations need LAB)
├─ Already LAB → Proceed directly
└─ HSV/HSL → Convert RGB→LAB (HSV not perceptually uniform)

Is perceptual accuracy critical?
├─ YES → Use CIEDE2000 (most accurate, slower)
├─ SPEED critical → Use Euclidean LAB distance (faster approximation)
└─ BALANCED → Use deltaE94 (middle ground)

Arrangement Pattern Decision

What's the desired visual impact?
├─ DRAMATIC → Neutral-with-accent (85% muted, 15% vivid)
│  └─ Accent placement? → Golden ratio positions (0.382, 0.618)
├─ SMOOTH → Hue-sorted gradient
│  ├─ Full spectrum? → 360° hue range
│  └─ Limited range? → Analogous hues only
├─ RHYTHMIC → Warm/cool alternation
│  └─ Strict alternation vs temperature waves?
└─ BALANCED → Temperature-balanced grid (equal warm/cool distribution)

Failure Modes

Diversity Collapse ("All Blue Skies")

Symptoms: Selected photos all have similar dominant colors (e.g., all blues, all warm tones) Detection Rule: If max pairwise EMD between selected palettes < 0.3, you have diversity collapse Root Cause: λ parameter too high (>0.8) or no diversity penalty applied Fix: Reduce λ to 0.6-0.7, or switch from pure EMD to MMR algorithm

Perceptual Mismatch ("Looks Wrong to Humans")

Symptoms: Mathematically similar colors that humans perceive as clashing Detection Rule: If EMD < 0.4 but human feedback rates harmony < 3/5, you have perceptual mismatch Root Cause: Using RGB/HSV distance instead of perceptual LAB space Fix: Always use LAB space with CIEDE2000, validate against human-labeled training data

Temperature Incoherence ("Jarring Transitions")

Symptoms: Abrupt warm-to-cool transitions creating visual discord Detection Rule: If adjacent photos have |b_value| difference > 40 in LAB space, flag transition Root Cause: No temperature-aware arrangement or poor b-axis thresholding Fix: Implement temperature wave pattern or enforce minimum transition buffer zones

Saturation Monotony ("Washed Out" or "Oversaturated")

Symptoms: All selected photos have similar chroma levels, lacking visual interest Detection Rule: If chroma standard deviation < 15 across selected palettes, you have saturation monotony Root Cause: No chroma diversity in selection criteria Fix: Add chroma variance term to objective function: score += 0.1 * chroma_diversity_bonus

EMD Optimization Failure ("Poor Convergence")

Symptoms: Sinkhorn algorithm doesn't converge, returns suboptimal distances Detection Rule: If Sinkhorn iterations > 50 or relative error > 0.01, optimization failed (matches the Quality Gates convergence bound below) Root Cause: ε parameter too small (<0.05) or cost matrix poorly conditioned Fix: Increase ε to 0.1, add regularization to cost matrix, or fall back to exact EMD

Worked Examples

Example 1: Monochromatic Beach Photo Set

Scenario: User has 50 beach photos (all blues/whites) and wants 12 for a collage

Step 1 - Diagnosis:

• Extract LAB palettes: All photos have dominant blues (H≈210-240°, high chroma)
• Diversity risk: High (similar scenes/colors)
• Decision: Use MMR with λ=0.5 (equal harmony/diversity weight)

Step 2 - Palette Analysis:

Photo_001: LAB palette [(65, -8, -25), (45, 2, -15), (85, -5, -10)] → Ocean, sand, sky
Photo_023: LAB palette [(70, -12, -30), (40, 5, -20), (90, -3, -8)] → Similar but darker water

Step 3 - MMR Selection:

• First selection: Photo_001 (highest harmony with target "beach" palette)
• Second candidate: Photo_023 vs Photo_007
  • Harmony scores: 0.85 vs 0.82
  • Similarity to Photo_001: 0.9 vs 0.3
  • MMR scores: 0.5×0.85 - 0.5×0.9 = -0.025 vs 0.5×0.82 - 0.5×0.3 = 0.26
  • Select Photo_007 (higher diversity bonus outweighs harmony loss)

Expert Insight: Novice would select by harmony only → all similar blues. Expert catches diversity need early.

Example 2: Mixed White Balance Sequence

Scenario: Wedding photos with mixed indoor (warm 3200K) and outdoor (cool 5600K) lighting

Step 1 - White Balance Detection:

Indoor photos: Average b-value = +25 (warm/yellow bias)
Outdoor photos: Average b-value = -20 (cool/blue bias)
Temperature gap: 45 LAB units (significant)

Step 2 - Global Color Grading Decision:

• Options: A) Keep natural variation, B) Normalize to single white point
• Decision: Apply subtle grading (30% correction) to reduce jarring transitions
• Target white point: Neutral (b≈0) for consistency

Step 3 - Affine Transform:

# Map warm indoor colors toward neutral
indoor_transform = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 0.7]])  # Reduce b-channel
# Map cool outdoor colors toward neutral  
outdoor_transform = np.array([[1, 0, 0], [0, 1, 0], [0, 0, -0.3]]) # Increase b-channel

Result: Temperature difference reduced to 20 LAB units while preserving natural lighting character

Quality Gates

• All colors processed in LAB space (not RGB/HSV)
• CIEDE2000 ΔE < 10 between selected and target palette
• Hue coverage spans >120° for diverse selections (or <60° for monochromatic)
• Maximum pairwise EMD between selected palettes > 0.3 (diversity check)
• Chroma standard deviation > 15 across selections (saturation variety)
• Temperature transitions <40 LAB b-units between adjacent photos
• Sinkhorn convergence: <50 iterations AND relative error <0.01
• DPP determinant >0.1 (healthy repulsion between selections)
• Blend ratio for color grading ≤0.4 (preserve photo authenticity)
• Processing time <500ms for MMR selection on 1000 candidates

Run node scripts/palette_audit.mjs --input plan.json against a palette-selection plan (see schemas/palette-plan.schema.json) to check these gates deterministically instead of eyeballing them. It returns { pass, findings, recommendations } and flags diversity collapse, perceptual mismatch (wrong color space), saturation monotony, temperature incoherence, Sinkhorn non-convergence, and authenticity-losing blend ratios.

References

File	Load When
references/perceptual-color-spaces.md	Need the LAB/LCH rationale, CIEDE2000 formulation, or edge-case handling (grayscale, single-color, extreme lightness).
references/optimal-transport.md	Need the Earth-Mover Distance/Wasserstein formulation, the Sinkhorn algorithm, or epsilon-tuning guidance.
references/temperature-classification.md	Need warm/cool classification (hue-angle or LAB b-axis), hue-sorted sequencing, or temperature-wave arrangement.
references/arrangement-patterns.md	Need neutral-with-accent, hue-harmony, or global color-grading arrangement recipes.
references/diversity-algorithms.md	Need MMR, DPP, or submodular-maximization details and the λ-tuning guide.
references/implementation-guide.md	Need Python dependencies, performance targets, Metal shader examples, or the troubleshooting guide.
examples/sample-input.json	Need a complete, auditor-passing palette-selection plan to copy from.
examples/expected-output.md	Need the shape of a finished palette/arrangement recommendation plus its audit result.
templates/output-template.md	Need a reusable recommendation template to fill in for a new palette-selection task.
schemas/palette-plan.schema.json	Need to validate a palette-selection plan's structure programmatically.
scripts/palette_audit.mjs	Need deterministic scoring of a palette-selection plan against this skill's own failure modes.
agents/openai.yaml	Need a subagent descriptor for delegated palette-selection auditing.

NOT-FOR Boundaries

Do NOT use this skill for:

• Basic RGB manipulation → Use standard image processing libraries
• Single-photo color grading/enhancement → Use native-app-designer skill
• UI/web color scheme generation → Use vaporwave-glassomorphic-ui-designer
• Color blindness accessibility → Delegate to accessibility specialists
• Print color management (CMYK) → Use print-design-expert
• Video color grading workflows → Use video-editing-expert

When to delegate:

• For spatial color analysis (gradients, regions) → Use image-analysis-expert
• For brand color compliance → Use brand-identity-designer
• For cultural color symbolism → Use cultural-consultant-expert

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Skill Bundle Index

Every file in this skill, and when to open it. Auto-generated; run scripts/index_references.py --fix.

root

• CHANGELOG.md — Changelog — All notable changes to the color-theory-palette-harmony-expert skill will be documented in this file.
• README.md — Color Theory & Palette Harmony Expert — Design harmonized, diversity-aware color palettes for computational photo composition, using perceptual color science (LAB space, CIEDE2000)

agents/

• agents/openai.yaml — openai (data/schema)

examples/

• examples/expected-output.md — Example Output: Palette Arrangement Recommendation — Scenario: 50 monochromatic beach photos (all blues/whites), 12 selected for a collage (mirrors "Example 1: Monochromatic Beach Photo Set" in
• examples/sample-input.json — sample input (data/schema)

references/

• references/arrangement-patterns.md — Color Arrangement Patterns — Design Principle: Create visual impact by surrounding neutral/muted photos with occasional vibrant accents.
• references/diversity-algorithms.md — Diversity Algorithms: Preventing Color Monotony — Without diversity constraints, optimization may select all similar colors (e.g., all blue skies).
• references/implementation-guide.md — Implementation Guide — | Package | Purpose | |---------|---------| | colormath | CIEDE2000 implementation, LAB/LCH conversions | | opencv-python | Image color
• references/optimal-transport.md — Optimal Transport for Color Matching — Given two photos with color distributions μ and ν, how "different" are they perceptually?
• references/perceptual-color-spaces.md — Perceptual Color Spaces — RGB and HSV are device-dependent and not perceptually uniform.
• references/temperature-classification.md — Warm/Cool Temperature Classification — Given a photo, classify its dominant temperature as warm, cool, or neutral.

schemas/

• schemas/palette-plan.schema.json — palette plan.schema (data/schema)

scripts/

• scripts/palette_audit.mjs

templates/

• templates/output-template.md — Palette Arrangement Recommendation Template — [One-sentence description of the photo collection or target palette this recommendation covers.] - Selected photos/colors (ordered): [li

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