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# Extraction Best Practices
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Guidelines for capturing and analyzing design references effectively.
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## Capture Quality Guidelines
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### Screenshot Requirements
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- High-resolution (minimum 1920px wide for desktop)
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- Accurate color reproduction (disable browser extensions that alter colors)
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- Actual viewport size, not full-page scrolls
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- Device-specific resolutions (desktop 1920x1080, mobile 390x844)
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- Multiple states: default, hover, active, responsive breakpoints
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### Multiple Examples
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- Analyze 3-5 screens minimum for pattern recognition
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- Include different page types (home, product, about, contact)
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- Single screenshots miss patterns
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- Capture from same site to identify consistency
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## Analysis Best Practices
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### 1. Demand Specifics
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❌ Accept: "Uses blue and gray colors"
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✓ Demand: "Primary: #1E40AF, Secondary: #6B7280, Accent: #F59E0B"
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❌ Accept: "Modern sans-serif font"
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✓ Demand: "Inter, weight 600, 48px for h1, tracking -0.02em"
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### 2. Document Rationale
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Understand *why* design decisions work, not just *what* they are:
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- Why does this color palette create trust?
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- Why does this spacing scale improve readability?
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- Why does this typography hierarchy guide user attention?
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### 3. Create Actionable Guidelines
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Output should be directly implementable in code:
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```css
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/* Immediately usable CSS from extraction */
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:root {
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--font-display: 'Bebas Neue', sans-serif;
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--font-body: 'Inter', sans-serif;
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--color-primary-600: #1E40AF;
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--color-accent-500: #F59E0B;
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--spacing-xs: 4px;
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--spacing-sm: 8px;
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--spacing-md: 16px;
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--spacing-lg: 24px;
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--radius-sm: 4px;
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--radius-md: 8px;
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--shadow-sm: 0 1px 2px rgba(0,0,0,0.05);
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--shadow-md: 0 4px 6px rgba(0,0,0,0.1);
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}
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```
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### 4. Cross-Reference
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- Validate font predictions against Google Fonts library
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- Use font identification tools (WhatFont, Font Ninja) for accuracy
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- Manually verify extracted colors with eyedropper tools
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### 5. Iterate Analysis
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First pass may miss subtleties:
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- Run initial comprehensive analysis
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- Review output, identify gaps
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- Run focused follow-up queries for specific elements
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## Advanced Techniques
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### Design System Mining
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Extract complete design system from single brand (10+ screens):
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```bash
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python scripts/gemini_batch_process.py \
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--files docs/inspiration/brand/*.png \
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--task analyze \
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--prompt "Extract complete, production-ready design system:
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- All color tokens (20+ colors)
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- All typography specs (sizes, weights, line-heights)
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- All spacing tokens
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- All component variants
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- All animation timings
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Output as CSS variables ready for implementation." \
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--output docs/design-guidelines/brand-design-system.md \
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--model gemini-2.5-flash
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```
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### Trend Analysis
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Analyze multiple top designs to identify current trends:
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```bash
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python scripts/gemini_batch_process.py \
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--files docs/inspiration/awwwards-*.png \
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--task analyze \
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--prompt "Trend analysis across 10 award-winning designs:
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1. Dominant aesthetic movements
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2. Common color strategies
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3. Typography trends
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4. Layout innovations
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5. Animation patterns
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Identify what's trending in 2024 web design." \
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--output docs/design-guidelines/trend-analysis.md \
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--model gemini-2.5-flash
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```
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### Historical Evolution
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Track design evolution of single brand over time:
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```bash
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python scripts/gemini_batch_process.py \
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--files docs/inspiration/brand-2020.png docs/inspiration/brand-2024.png \
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--task analyze \
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--prompt "Compare 2020 vs 2024 design evolution:
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1. What changed and why
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2. What remained consistent (brand identity)
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3. How trends influenced changes
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4. Lessons for our design evolution" \
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--output docs/design-guidelines/evolution-analysis.md \
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--model gemini-2.5-flash
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```
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## Common Pitfalls
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### ❌ Surface-Level Analysis
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"Uses blue colors and sans-serif fonts"
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**Fix**: Demand specifics—hex codes, font names, size values
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### ❌ Missing Context
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Extracting design without understanding target audience or purpose
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**Fix**: Research brand context before analysis
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### ❌ Blind Copying
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Extracting and applying design 1:1 to your project
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**Fix**: Extract principles, adapt to your unique context
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### ❌ Single Source
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Learning from one example only
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**Fix**: Analyze 3-5 examples to identify patterns vs. anomalies
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