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Design System Builder

CommunityPopular
zebbern
design-system-builder

Extract design systems from reference UI images and generate implementation-ready UI design prompts. Use when users provide UI screenshots/mockups and want to create consistent designs, generate design systems, or build MVP UIs matching reference aesthetics.

Overview

Publisherzebbern
Repositoryclaude-code-guide
Skill namedesign-system-builder
Stars
4.6K
Forks
464
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zebbern on GitHub. Read the source before you install it.

Installation

Install the Design System Builder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zebbern/claude-code-guide.git /tmp/claude-code-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-guide/skills/design-system-builder .claude/skills/design-system-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design System Builder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Design System Builder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Design System Builder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

UI Designer

Overview

This skill enables systematic extraction of design systems from reference UI images through a multi-step workflow: analyze visual patterns → generate design system documentation → create PRD → produce implementation-ready UI prompts.

When to Use

  • User provides UI screenshots, mockups, or design references
  • Need to extract color palettes, typography, spacing from existing designs
  • Want to generate design system documentation from visual examples
  • Building MVP UI that should match reference aesthetics
  • Creating multiple UI variations following consistent design principles

Workflow

Step 1: Gather Inputs

Request from user:

  • Reference images directory: Path to folder containing UI screenshots/mockups
  • Project idea file: Document describing the product concept and goals
  • Existing PRD (optional): If PRD already exists, skip Step 3

Step 2: Extract Design System from Images

Use Task tool with general-purpose subagent, providing:

Prompt template from assets/design-system.md:

  • Analyze color palettes (primary, secondary, accent, functional colors)
  • Extract typography (font families, sizes, weights, line heights)
  • Identify component styles (buttons, cards, inputs, icons)
  • Document spacing system
  • Note animations/transitions patterns
  • Include dark mode variants if present

Attach reference images to the subagent context.

Output: Complete design system markdown following the template format

Save to: documents/designs/{image_dir_name}_design_system.md

Step 3: Generate MVP PRD (if not provided)

Use Task tool with general-purpose subagent, providing:

Prompt template from assets/app-overview-generator.md:

  • Replace {项目背景} with content from project idea file
  • The template guides through: elevator pitch, problem statement, target audience, USP, features list, UX/UI considerations

Interact with user to refine and clarify product requirements

Output: Structured PRD markdown

Save as variable for Step 4 (optionally save to documents/prd/)

Step 4: Compose Final UI Implementation Prompt

Combine design system and PRD using assets/vibe-design-template.md:

Substitutions:

  • {项目设计指南} → Design system from Step 2
  • {项目MVP PRD} → PRD from Step 3 or provided PRD file

Result: Complete, implementation-ready prompt containing:

  • Design aesthetics principles
  • Project-specific color/typography guidelines
  • App overview and feature requirements
  • Implementation tasks (multiple UI variations, component structure)

Save to: documents/ux-design/{idea_file_name}_design_prompt_{timestamp}.md

Step 5: Verify React Environment

Check for existing React project:

bash
find . -name "package.json" -exec grep -l "react" {} \;

If none found, inform user:

bash
npx create-react-app my-app
cd my-app
npm install -D tailwindcss postcss autoprefixer
npx tailwindcss init -p
npm install lucide-react

Step 6: Implement UI

Use the final composed prompt from Step 4 to implement UI in React project.

The prompt instructs to:

  • Create multiple design variations (3 for mobile, 2 for web)
  • Organize as separate components: [solution-name]/pages/[page-name].jsx
  • Aggregate all variations in showcase page

Template Assets

assets/design-system.md

Template for extracting visual design patterns. Includes sections for:

  • Color palette (primary, secondary, accent, functional, backgrounds)
  • Typography (font families, weights, text styles)
  • Component styles (buttons, cards, inputs, icons)
  • Spacing system (4dp-48dp scale)
  • Animations (durations, easing curves)
  • Dark mode variants

Use this template when analyzing reference images to ensure comprehensive design system coverage.

assets/app-overview-generator.md

Template for collaborative PRD generation. Guides through:

  • Elevator pitch
  • Problem statement and target audience
  • Unique selling proposition
  • Platform targets
  • Feature list with user stories
  • UX/UI considerations per screen

Designed for interactive refinement with user to clarify requirements.

assets/vibe-design-template.md

Final implementation prompt template combining design system and PRD. Includes:

  • Aesthetic principles (minimalism, whitespace, color theory, typography hierarchy)
  • Practical requirements (Tailwind CSS, Lucide icons, responsive design)
  • Task specifications (multiple variations, component organization)

This template produces prompts ready for UI implementation without further modification.

Best Practices

Image Analysis

  • Read all images before starting analysis
  • Look for patterns across multiple screens
  • Note both explicit styles (colors, fonts) and implicit principles (spacing, hierarchy)
  • Capture dark mode if present in references

Design System Extraction

  • Be systematic: cover all template sections
  • Use specific values (hex codes, px sizes) not generic descriptions
  • Document the "why" for design choices when inferable
  • Include variants (hover states, disabled states)

PRD Generation

  • Engage user interactively to clarify ambiguities
  • Suggest features based on problem understanding
  • Ensure MVP scope is realistic
  • Document UX considerations per screen/interaction

Output Organization

  • Save design system with descriptive filename (based on image dir name)
  • Save final prompt with timestamp for version tracking
  • Keep all outputs in documents/ directory for easy reference
  • Preserve intermediate outputs for iteration

Example Usage

User provides:

  • reference-images/saas-dashboard/ (5 screenshots)
  • ideas/project-management-app.md (project concept)

Execute workflow:

  1. Read 5 images from reference-images/saas-dashboard/
  2. Use Task tool → design-system.md template → analyze images
  3. Save to documents/designs/saas-dashboard_design_system.md
  4. Use Task tool → app-overview-generator.md with project concept
  5. Refine PRD through user interaction
  6. Combine design system + PRD using vibe-design-template.md
  7. Save to documents/ux-design/project-management-app_design_prompt_20251025_153000.md
  8. Check React environment, inform user if setup needed
  9. Implement UI using final prompt

Notes

  • This is a high freedom workflow—adapt steps based on context
  • Templates provide structure but encourage thoughtful analysis over rote filling
  • User interaction during PRD generation is critical for quality
  • Final prompt quality directly impacts UI implementation success
  • Preserve all intermediate outputs for iteration and refinement

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Design System Builder AI skill do?

Extract design systems from reference UI images and generate implementation-ready UI design prompts. Use when users provide UI screenshots/mockups and want to create consistent designs, generate design systems, or build MVP UIs matching reference aesthetics.

Why use Design System Builder on TypingMind?

Because you install it once and use it with any model. Design System Builder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Design System Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zebbern/claude-code-guide/tree/main/skills/design-system-builder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Design System Builder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Design System Builder?

As many as you like. As long as a model supports skills, you can use Design System Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Design System Builder AI skill free?

Yes. It is published on GitHub by zebbern under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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