Expert photography composition critic grounded in graduate-level visual aesthetics education, computational aesthetics research (AVA, NIMA, LAION-Aesthetics, VisualQuality-R1), and professional image analysis with custom tooling. Use for image quality assessment, composition analysis, aesthetic scor

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 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)

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