Mike-Moore100

Mike-Moore100 / UX-Design-System-for-AI

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A rule-based UX and design system for AI-built apps, turning proven principles into enforceable constraints for better, simpler products.

18
2
69% credibility
Found Apr 30, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A set of rules, templates, and prompts to guide AI tools in building simple, user-friendly app interfaces based on proven design principles.

How It Works

1
🔍 Discover the UX guide

You find this simple set of rules that helps AI create easy-to-use app screens without confusion.

2
📋 Share rules with your AI helper

You copy the friendly rules into your AI chat and tell it to follow them strictly like a recipe.

3
👤 Describe your user and goal

You explain who will use the screen and the one main thing they want to do right now.

4
✨ AI builds your screen

Your AI creates a clear screen design that's fast and obvious, guiding the user straight to success.

5
✅ AI checks and improves

It scores the design for clarity and tweaks anything to make it even simpler and better.

🎉 Enjoy your perfect screen

You now have an intuitive, no-fuss app screen that users love and understand instantly.

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AI-Generated Review

What is UX-Design-System-for-AI?

This rule-based design system for AI-built apps enforces proven UX principles like Hick's Law and Gestalt into strict constraints, ensuring AI outputs clean, intuitive interfaces. Paste its Markdown prompts into tools like Claude or Cursor, define user goals and actions, and let AI generate, score, and refine screens until they hit 85+ usability. It solves AI's tendency to spit out cluttered, decision-heavy UIs by prioritizing one primary action per screen for faster, low-cognitive-load apps.

Why is it gaining traction?

Unlike vague style guides, it bakes rules into AI prompts with built-in scoring loops, forcing iterative improvements without dev intervention—perfect for rule-based chatbot design or generative apps. Developers hook on the workflow: input task/user intent, get polished React/HTML output ready for production, cutting manual UX tweaks. In a world of rule-based AI GitHub experiments, its focus on enforceable constraints stands out for consistent, predictable results.

Who should use this?

Solo founders or indie hackers prototyping AI-driven web apps and dashboards via prompts. Frontend devs building rule-based recommendation systems or chatbots needing quick, familiar patterns without overthinking flows. Teams enforcing structural rule-based design in rapid AI iterations, like onboarding or feature screens.

Verdict

Grab it if you're deep in AI app building—its detailed prompts deliver real UX wins despite 18 stars and 0.7% credibility score signaling early-stage maturity. Docs are solid for immediate use, but expect to tweak for complex rule-based system design; test on a small project first.

(178 words)

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