photo-composition-critic

---
name: photo-composition-critic
description: Expert photography composition critic grounded in graduate-level visual aesthetics education, computational aesthetics research (AVA, NIMA, LAION-Aesthetics, VisualQuality-R1), and professional
license: MIT
---

# 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)