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design-archivist设计档案员

Agent Skill

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

总安装

408

周安装

17

GitHub Stars

98

下载量

136
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:design-archivist(设计档案员)
来源仓库:https://github.com/curiositech/some_claude_skills
仓库路径:skills/design-archivist
安装命令:
npx skills add https://github.com/curiositech/some_claude_skills --skill design-archivist
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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

简介

design-archivist 通过大规模真实案例分析,系统性构建视觉数据库的设计人类学家。

  • 专为多日研究设计(每 10 个样本设检查点),可整理 500-1000 个设计范例。
  • 支持按主题、受众、平台等维度分类整理,输出可检索的设计模式库。
  • 使用时需明确定义研究范围和目标受众,定期进行数据质量校验。
  • 不适合快速原型设计或单一项目 UI 优化等短期任务。

SKILL.md

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:

  1. Capture visual snapshot
  2. Record metadata (URL, timestamp, context)
  3. Extract Visual DNA (colors, typography, layout, interactions)
  4. Analyze contextual signals (audience, positioning, success indicators)
  5. Apply categorical tags
  6. 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:

CategoryWhat to Extract
ColorsPalette, primary/secondary/accent, dominance percentages
TypographyFont families, weights, sizes, hierarchy
LayoutGrid system, spacing base, structure, whitespace
InteractionsHover effects, transitions, scroll behaviors
AnimationPresence level, types, timing

See references/data_structures.md for full TypeScript interfaces.

Domain Quick Reference

DomainFocus AreasSeed Sources
PortfoliosClarity, credibility, storytellingAwwwards, Dribbble, Behance
SaaS LandingConversion, trust signals, pricingProduct Hunt, SaaS directories
E-CommerceProduct photos, checkout, mobileShopify stores, major retailers
Adult ContentPremium positioning, discretionAdult ad networks, VR platforms
Technical DemosVisual drama, performance, interactivityShadertoy, 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:

ItemCost
Screenshots~$20 (Playwright cloud @ $0.02/each)
LLM Analysis~$15 (100 batches × $0.15)
Storage~$0.01 (200MB)
Total~$35
Runtime48-72 hours

Inform users of scope and cost before beginning.

Reference Files

FileContents
references/data_structures.mdTypeScript interfaces for VisualDNA, ContextAnalysis, Checkpoint
references/domain_guides.mdDetailed 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)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.6%
按下载量换算51

Claude

29.29%
按下载量换算40

Cursor

20.95%
按下载量换算28

Gemini CLI

9.81%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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