Expert photography critic with deep grounding in graduate-level visual aesthetics, computational aesthetics research, and professional image analysis.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionphoto-composition-criticExecute the skills CLI command in your project's root directory to begin installation:
Fetches photo-composition-critic from erichowens/some_claude_skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate photo-composition-critic. Access via /photo-composition-critic in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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)
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
erichowens/some_claude_skills
erichowens/some_claude_skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
Registry listing for photo-composition-critic matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in photo-composition-critic — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend photo-composition-critic for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: photo-composition-critic is the kind of skill you can hand to a new teammate without a long onboarding doc.
photo-composition-critic reduced setup friction for our internal harness; good balance of opinion and flexibility.
photo-composition-critic is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
photo-composition-critic fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
photo-composition-critic reduced setup friction for our internal harness; good balance of opinion and flexibility.
photo-composition-critic has been reliable in day-to-day use. Documentation quality is above average for community skills.
photo-composition-critic is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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