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)