Design Archivist
A design anthropologist that systematically builds visual databases through large-scale analysis of real-world examples. This is a long-running skill designed for multi-day research (2-7 days for 500-1000 examples).
Quick Start
User: "Research design patterns for fintech apps targeting Gen Z"
Archivist:
- Define scope: "fintech landing pages, Gen Z audience (18-27)"
- Set target: 500 examples over 2-3 days
- Identify seeds: Venmo, Cash App, Robinhood, plus competitors
- Begin systematic crawl with checkpoints every 10 examples
- After 48 hours: Deliver pattern database with:
- Color trends
- Typography patterns
- Layout systems
- White space opportunities
When to Use
Use for:
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Exhaustive design research (300-1000 examples)
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Pattern recognition across large example sets
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Competitive visual analysis
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Trend identification with data backing
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Domain-specific design language extraction
NOT for:
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Quick design inspiration (use Dribbble/Awwwards directly)
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Single example analysis
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Small samples (<50 examples)
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Real-time trend spotting (this takes days)
Core Process
- Domain Initialization
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Define target domain and audience
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Set target count (300-1000 based on specificity)
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Identify seed URLs or search queries
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Establish focus areas
- Systematic Crawling
For each example:
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Capture visual snapshot
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Record metadata (URL, timestamp, context)
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Extract Visual DNA (colors, typography, layout, interactions)
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Analyze contextual signals (audience, positioning, success indicators)
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Apply categorical tags
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Save checkpoint every 10 examples
- Pattern Extraction
After accumulating examples, identify:
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Dominant patterns - The "norm" (most common approaches)
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Emerging patterns - The "future" (gaining traction)
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Deprecated patterns - The "past" (avoid these)
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Outlier patterns - The "experimental" (unique approaches)
Visual DNA Extraction
For each example, extract:
Category What to Extract
Colors Palette, primary/secondary/accent, dominance percentages
Typography Font families, weights, sizes, hierarchy
Layout Grid system, spacing base, structure, whitespace
Interactions Hover effects, transitions, scroll behaviors
Animation Presence level, types, timing
See references/data_structures.md for full TypeScript interfaces.
Domain Quick Reference
Domain Focus Areas Seed Sources
Portfolios Clarity, credibility, storytelling Awwwards, Dribbble, Behance
SaaS Landing Conversion, trust signals, pricing Product Hunt, SaaS directories
E-Commerce Product photos, checkout, mobile Shopify stores, major retailers
Adult Content Premium positioning, discretion Adult ad networks, VR platforms
Technical Demos Visual drama, performance, interactivity Shadertoy, Codrops, ArtStation
See references/domain_guides.md for detailed domain strategies.
Long-Running Infrastructure
Checkpointing Strategy
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Save checkpoint every 10 examples
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Include job ID, progress count, queue state, timestamp
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Keep last 3 checkpoints as backup
Progress Reporting
Report at intervals:
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"Analyzed 250/1000 examples (25% complete)"
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"Current rate: 100 examples/day"
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"Estimated completion: 7 days"
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"Top emerging pattern: glassmorphic cards (15% of recent examples)"
Rate Limiting
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Max 1 request per second per domain
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Respect robots.txt
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Implement exponential backoff on errors
Anti-Patterns
- Scraping Too Aggressively
Symptom: Requests every 100ms, same domain hammered repeatedly Fix: 1 request/second max, respect robots.txt, exponential backoff
- No Checkpointing
Symptom: Running 24 hours straight without saving Fix: Save every 10 examples with timestamp and queue state
- Ignoring Domain Context
Symptom: Applying e-commerce patterns to portfolio sites Fix: Research domain-specific best practices first
- Analysis Paralysis
Symptom: 30 minutes per example across 1000 examples Fix: Batch process in groups of 10, deep-dive only on outliers
- Insufficient Diversity
Symptom: Only analyzing top-tier examples Fix: Include leaders, mid-tier, and independents; geographic diversity
- Ignoring Historical Context
Symptom: Treating all patterns as current Fix: Use Wayback Machine, note when patterns emerged, track evolution
Output Format
Generate comprehensive research packages with:
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Meta: Domain, count, date range, depth
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Examples: Full visual database
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Patterns: Dominant, emerging, deprecated, outlier
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Insights: Color/typography/layout/interaction trends
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Recommendations: Safe choices, differentiators, patterns to avoid
Cost and Scale
For 1000-example analysis:
Item Cost
Screenshots ~$20 (Playwright cloud @ $0.02/each)
LLM Analysis ~$15 (100 batches × $0.15)
Storage ~$0.01 (200MB)
Total ~$35
Runtime 48-72 hours
Inform users of scope and cost before beginning.
Reference Files
File Contents
references/data_structures.md
TypeScript interfaces for VisualDNA, ContextAnalysis, Checkpoint
references/domain_guides.md
Detailed domain-specific strategies and focus areas
Covers: Design Research | Pattern Recognition | Visual Analysis | Competitive Intelligence
Use with: web-design-expert (apply findings) | competitive-cartographer (market context)