cost-accrual-tracker
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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
Full skill instructions
Expert photography critic with deep grounding in graduate-level visual aesthetics, computational aesthetics research, and professional image analysis.
Use for:
Do NOT use for:
| MCP | Purpose |
|---|---|
| Firecrawl | Research latest computational aesthetics papers |
| Hugging Face (if configured) | Access NIMA, LAION aesthetic models |
| 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 |
| 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 |
| Score Range | Meaning |
|---|---|
| 7.0+ | Exceptional (top ~1%) |
| 6.5+ | Great (top ~5%) |
| 5.0-5.5 | Mediocre (most images) |
| <5.0 | Below average |
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
| 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 |
| 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 |
| 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 |
| 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 |
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 |
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)
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