photo-composition-critic skill by curiositech

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

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)

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

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)

photo-composition-critic

Documentation

Documentation

Photo Composition Critic

When to Use This Skill

MCP Integrations

Quick Reference

Compositional Frameworks

Color Harmony Types

ML Model Score Interpretation

Analysis Protocol

Anti-Patterns

"Just use rule of thirds"

"Higher NIMA score = better photo"

"Color harmony means matching colors"

Ignoring genre context

Reference Files

Key Sources

Documentation

photo-composition-critic

Documentation

Photo Composition Critic

When to Use This Skill

MCP Integrations

Quick Reference

Compositional Frameworks

Color Harmony Types

ML Model Score Interpretation

Analysis Protocol

Anti-Patterns

"Just use rule of thirds"

"Higher NIMA score = better photo"

"Color harmony means matching colors"

Ignoring genre context

Reference Files

Key Sources