| license | Apache-2.0 |
| name | design-archivist |
| description | Long-running design anthropologist that builds comprehensive visual databases from 500-1000 real-world examples, extracting color palettes, typography patterns, layout systems, and interaction design across any domain (portfolios, e-commerce, SaaS, adult content, technical showcases). This skill should be used when users need exhaustive design research, pattern recognition across large example sets, or systematic visual analysis for competitive positioning. |
| allowed-tools | Read,Write,WebSearch,WebFetch |
| category | Design & Creative |
| tags | ["design-history","archival","preservation","research","documentation"] |
| pairs-with | [{"skill":"web-design-expert","reason":"Apply researched patterns to designs"},{"skill":"competitive-cartographer","reason":"Design-focused competitive analysis"}] |
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:
1. Define scope: "fintech landing pages, Gen Z audience (18-27)"
2. Set target: 500 examples over 2-3 days
3. Identify seeds: Venmo, Cash App, Robinhood, plus competitors
4. Begin systematic crawl with checkpoints every 10 examples
5. After 48 hours: Deliver pattern database with:
- Color trends
- Typography patterns
- Layout systems
- White space opportunities
When to Use
Use for:
- Exhaustive design research (300-1000 examples)
- Pattern recognition across large example sets
- Competitive visual analysis
- Trend identification with data backing
- Domain-specific design language extraction
NOT for:
- Quick design inspiration (use Dribbble/Awwwards directly)
- Single example analysis
- Small samples (<50 examples)
- Real-time trend spotting (this takes days)
Core Process
1. Domain Initialization
- Define target domain and audience
- Set target count (300-1000 based on specificity)
- Identify seed URLs or search queries
- Establish focus areas
2. Systematic Crawling
For each example:
- Capture visual snapshot
- Record metadata (URL, timestamp, context)
- Extract Visual DNA (colors, typography, layout, interactions)
- Analyze contextual signals (audience, positioning, success indicators)
- Apply categorical tags
- Save checkpoint every 10 examples
3. Pattern Extraction
After accumulating examples, identify:
- Dominant patterns - The "norm" (most common approaches)
- Emerging patterns - The "future" (gaining traction)
- Deprecated patterns - The "past" (avoid these)
- Outlier patterns - The "experimental" (unique approaches)
Visual DNA Extraction
For each example, extract:
