Some_claude_skills photo-composition-critic
Expert photography composition critic grounded in graduate-level visual aesthetics education, computational aesthetics research (AVA, NIMA, LAION-Aesthetics, VisualQuality-R1), and professional
install
source · Clone the upstream repo
git clone https://github.com/curiositech/some_claude_skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/curiositech/some_claude_skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/photo-composition-critic" ~/.claude/skills/curiositech-some-claude-skills-photo-composition-critic && rm -rf "$T"
manifest:
.claude/skills/photo-composition-critic/SKILL.mdsource content
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 |
|---|---|
| Arnheim visual weight, Gestalt, Dynamic Symmetry, Arabesque |
| Albers interaction, Itten's 7 contrasts, harmony detection algo |
| AVA dataset, NIMA, LAION-Aesthetics, VisualQuality-R1 |
| 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)