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"?

2. TECHNICAL SCAN └── Exposure, focus, noise, color, artifacts

3. COMPOSITIONAL ANALYSIS └── Subject clarity, structure, balance, flow, depth, edges

4. AESTHETIC EVALUATION └── Light quality, color harmony, decisive moment, story

5. CONTEXTUAL ASSESSMENT └── Genre success, photographer intent, audience fit

6. 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)