🎯

design-archivist

🎯Skill

from erichowens/some_claude_skills

What it does

Systematically builds comprehensive visual design databases by analyzing 500-1000 real-world examples across diverse domains, extracting actionable design patterns and trends.

design-archivist

Installation

Install skill:

npx skills add https://github.com/erichowens/some_claude_skills --skill design-archivist

Last UpdatedJan 23, 2026

View on GitHub Back to Skills

Skill Details

SKILL.md

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.

Overview

# 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:

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:

| 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

Save checkpoint every 10 examples
Include job ID, progress count, queue state, timestamp
Keep last 3 checkpoints as backup

Progress Reporting

Report at intervals:

"Analyzed 250/1000 examples (25% complete)"
"Current rate: 100 examples/day"
"Estimated completion: 7 days"
"Top emerging pattern: glassmorphic cards (15% of recent examples)"

Rate Limiting

Max 1 request per second per domain
Respect robots.txt
Implement exponential backoff on errors

Anti-Patterns

1. Scraping Too Aggressively

Symptom: Requests every 100ms, same domain hammered repeatedly

Fix: 1 request/second max, respect robots.txt, exponential backoff

2. No Checkpointing

Symptom: Running 24 hours straight without saving

Fix: Save every 10 examples with timestamp and queue state

3. Ignoring Domain Context

Symptom: Applying e-commerce patterns to portfolio sites

Fix: Research domain-specific best practices first

4. Analysis Paralysis

Symptom: 30 minutes per example across 1000 examples

Fix: Batch process in groups of 10, deep-dive only on outliers

5. Insufficient Diversity

Symptom: Only analyzing top-tier examples

Fix: Include leaders, mid-tier, and independents; geographic diversity

6. 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:

Meta: Domain, count, date range, depth
Examples: Full visual database
Patterns: Dominant, emerging, deprecated, outlier
Insights: Color/typography/layout/interaction trends
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)

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