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)
Converted and distributed by TomeVault — claim your Tome and manage your conversions.
1---2name: photo-composition-critic3description: Expert photography composition critic grounded in graduate-level visual aesthetics education, computational aesthetics research (AVA, NIMA, LAION-Aesthetics, VisualQuality-R1), and professional Use when this capability is needed.4---56# Photo Composition Critic78Expert photography critic with deep grounding in graduate-level visual aesthetics, computational aesthetics research, and professional image analysis.910## When to Use This Skill1112**Use for:**13- Evaluating image composition quality14- Aesthetic scoring with ML models (NIMA, LAION)15- Photo critique with actionable feedback16- Analyzing color harmony and visual balance17- Comparing multiple crop options18- Understanding photography theory1920**Do NOT use for:**21- Generating images → use **Stability AI** directly22- Photo editing/retouching → use **native-app-designer**23- Simple image similarity → use **clip-aware-embeddings**24- Collage creation → use **collage-layout-expert**2526## MCP Integrations2728| MCP | Purpose |29|-----|---------|30| **Firecrawl** | Research latest computational aesthetics papers |31| **Hugging Face** (if configured) | Access NIMA, LAION aesthetic models |3233## Quick Reference3435### Compositional Frameworks3637| Framework | Key Points |38|-----------|------------|39| **Visual Weight** | Size, color warmth, isolation, intrinsic interest, position |40| **Gestalt** | Proximity, similarity, continuity, closure, figure-ground |41| **Dynamic Symmetry** | Root rectangles (√2, √3, φ), baroque/sinister diagonals |42| **Arabesque** | S-curve, spiral, diagonal thrust - eye flow through frame |4344### Color Harmony Types4546| Type | Score | Notes |47|------|-------|-------|48| Complementary | 0.9 | High visual interest |49| Monochromatic | 0.85 | Safe, cohesive |50| Triadic | 0.85 | Balanced, vibrant |51| Analogous | 0.8 | Natural, harmonious |52| Achromatic | 0.7 | B&W or desaturated |53| Complex | 0.6 | May be chaotic or intentional |5455### ML Model Score Interpretation5657| Score Range | Meaning |58|-------------|---------|59| 7.0+ | Exceptional (top ~1%) |60| 6.5+ | Great (top ~5%) |61| 5.0-5.5 | Mediocre (most images) |62| <5.0 | Below average |6364## Analysis Protocol6566```671. FIRST IMPRESSION (2 seconds)68 └── Where does the eye go? Emotional hit? Anything "off"?69702. TECHNICAL SCAN71 └── Exposure, focus, noise, color, artifacts72733. COMPOSITIONAL ANALYSIS74 └── Subject clarity, structure, balance, flow, depth, edges75764. AESTHETIC EVALUATION77 └── Light quality, color harmony, decisive moment, story78795. CONTEXTUAL ASSESSMENT80 └── Genre success, photographer intent, audience fit81826. ACTIONABLE RECOMMENDATIONS83 └── Specific improvements, post-processing, alt crops84```8586## Anti-Patterns8788### "Just use rule of thirds"8990| What it looks like | Why it's wrong |91|--------------------|----------------|92| Blindly placing subjects on thirds intersections | Oversimplification ignores visual weight, gestalt, dynamic symmetry |93| **Instead**: Analyze visual weight center, consider multiple frameworks |9495### "Higher NIMA score = better photo"9697| What it looks like | Why it's wrong |98|--------------------|----------------|99| Using ML score as sole quality metric | Models trained on averages, miss artistic intent, polarizing works |100| **Instead**: Use ML as one input alongside theoretical analysis |101102### "Color harmony means matching colors"103104| What it looks like | Why it's wrong |105|--------------------|----------------|106| Recommending monochromatic or matchy palettes | Ignores Itten's contrasts, Albers' interaction effects |107| **Instead**: Evaluate harmony type AND contextual appropriateness |108109### Ignoring genre context110111| What it looks like | Why it's wrong |112|--------------------|----------------|113| Applying portrait criteria to documentary | Different genres have different quality signals |114| **Instead**: Assess against genre-appropriate standards |115116## Reference Files117118Load these for detailed implementations:119120| File | Contents |121|------|----------|122| `references/composition-theory.md` | Arnheim visual weight, Gestalt, Dynamic Symmetry, Arabesque |123| `references/color-theory.md` | Albers interaction, Itten's 7 contrasts, harmony detection algo |124| `references/ml-models.md` | AVA dataset, NIMA, LAION-Aesthetics, VisualQuality-R1 |125| `references/analysis-scripts.md` | PhotoCritic class, MCP server implementation |126127## Key Sources128129**Theory**: Arnheim (1974), Hambidge (1926), Itten (1961), Albers (1963), Freeman (2007)130131**Research**: AVA dataset (Murray 2012), NIMA (Talebi 2018), LAION-5B (Schuhmann 2022), Q-Instruct (Wu 2024)132133---134> Converted and distributed by [TomeVault](https://tomevault.io/claim/curiositech) — claim your Tome and manage your conversions.135<!-- tomevault:4.0:skill_md:2026-04-11 -->
