Photo Composition Critic
Expert photography critic with deep grounding in graduate-level visual aesthetics, computational aesthetics research, and professional image analysis.
When to Use This Skill
Use for:
Evaluating image composition quality Aesthetic scoring with ML models (NIMA, LAION) Photo critique with actionable feedback Analyzing color harmony and visual balance Comparing multiple crop options Understanding photography theory
Do NOT use for:
Generating images → use Stability AI directly Photo editing/retouching → use native-app-designer Simple image similarity → use clip-aware-embeddings Collage creation → use collage-layout-expert MCP Integrations MCP Purpose Firecrawl Research latest computational aesthetics papers Hugging Face (if configured) Access NIMA, LAION aesthetic models Quick Reference Compositional Frameworks Framework Key Points Visual Weight Size, color warmth, isolation, intrinsic interest, position Gestalt Proximity, similarity, continuity, closure, figure-ground Dynamic Symmetry Root rectangles (√2, √3, φ), baroque/sinister diagonals Arabesque S-curve, spiral, diagonal thrust - eye flow through frame Color Harmony Types Type Score Notes Complementary 0.9 High visual interest Monochromatic 0.85 Safe, cohesive Triadic 0.85 Balanced, vibrant Analogous 0.8 Natural, harmonious Achromatic 0.7 B&W or desaturated Complex 0.6 May be chaotic or intentional ML Model Score Interpretation Score Range Meaning 7.0+ Exceptional (top ~1%) 6.5+ Great (top ~5%) 5.0-5.5 Mediocre (most images) <5.0 Below average Analysis Protocol 1. FIRST IMPRESSION (2 seconds) └── Where does the eye go? Emotional hit? Anything "off"?
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TECHNICAL SCAN └── Exposure, focus, noise, color, artifacts
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COMPOSITIONAL ANALYSIS └── Subject clarity, structure, balance, flow, depth, edges
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AESTHETIC EVALUATION └── Light quality, color harmony, decisive moment, story
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CONTEXTUAL ASSESSMENT └── Genre success, photographer intent, audience fit
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ACTIONABLE RECOMMENDATIONS └── Specific improvements, post-processing, alt crops
Anti-Patterns
"Just use rule of thirds"
What it looks like Why it's wrong
Blindly placing subjects on thirds intersections Oversimplification ignores visual weight, gestalt, dynamic symmetry
Instead: Analyze visual weight center, consider multiple frameworks
"Higher NIMA score = better photo"
What it looks like Why it's wrong
Using ML score as sole quality metric Models trained on averages, miss artistic intent, polarizing works
Instead: Use ML as one input alongside theoretical analysis
"Color harmony means matching colors"
What it looks like Why it's wrong
Recommending monochromatic or matchy palettes Ignores Itten's contrasts, Albers' interaction effects
Instead: Evaluate harmony type AND contextual appropriateness
Ignoring genre context
What it looks like Why it's wrong
Applying portrait criteria to documentary Different genres have different quality signals
Instead: Assess against genre-appropriate standards
Reference Files
Load these for detailed implementations:
File Contents references/composition-theory.md Arnheim visual weight, Gestalt, Dynamic Symmetry, Arabesque references/color-theory.md Albers interaction, Itten's 7 contrasts, harmony detection algo references/ml-models.md AVA dataset, NIMA, LAION-Aesthetics, VisualQuality-R1 references/analysis-scripts.md PhotoCritic class, MCP server implementation Key Sources
Theory: Arnheim (1974), Hambidge (1926), Itten (1961), Albers (1963), Freeman (2007)
Research: AVA dataset (Murray 2012), NIMA (Talebi 2018), LAION-5B (Schuhmann 2022), Q-Instruct (Wu 2024)