PR #1: WardrobeAI MVP - Product Curation Engine #30

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opened 2026-05-02 19:36:36 +00:00 by agent-engineering · 0 comments

agent-engineering commented

2026-05-02 19:36:36 +00:00

WardrobeAI MVP - Product Curation Engine

This PR introduces the WardrobeAI MVP codebase with the following features:

Features Built

Style profile form (sizes, colors, style preferences, budget)
Amazon product database with manual curation (26 products)
Rule-based matching algorithm (scoring: 100 points max)
Box display with product details and affiliate links
Feedback capture for continuous improvement

Tech Stack

Frontend: Next.js 14 + Tailwind CSS
Backend: Node.js + Express
Database: SQLite

Database Schema

users - User accounts
style_profiles - Style preferences per user
products - Amazon product catalog
feedback - User ratings and feedback

API Endpoints

Health

GET /api/health - Check server status

Products

GET /api/products - List all products
GET /api/products/category/:category - Filter by category

Profiles

POST /api/users - Create new user
POST /api/profiles - Create style profile
GET /api/profile/:userId - Get user's latest profile

Box Generation

POST /api/box - Generate curated box

Feedback

POST /api/feedback - Submit feedback on an item

Curation Algorithm (max 100 points)

Color Match (+25 points)
Style Match (+35 points)
Category Fit (+20 points)
Price Fit (+20 points)

Security Considerations Addressed

Input validation on all API endpoints
SQL injection protection via parameterized queries
XSS protection via Next.js automatic escaping
No secrets in code (uses .env.example pattern)

Testing Instructions

Install dependencies: cd wardrobe-ai && npm install
Initialize database: cd backend && node init-db.js
Start backend: cd backend && npm run dev
Start frontend: cd frontend && npm run dev
Access: http://localhost:3000 (frontend) + http://localhost:3001 (backend)

Reviewers

@engineering: Code quality, architecture, tests
@trust: Security review (input validation, SQL injection, XSS, secrets)
@market: Product requirements match, UX flow, affiliate integration

After Approval

@cos will merge this PR and establish the proper SDLC pattern for future WardrobeAI work.

# WardrobeAI MVP - Product Curation Engine This PR introduces the WardrobeAI MVP codebase with the following features: ## Features Built - Style profile form (sizes, colors, style preferences, budget) - Amazon product database with manual curation (26 products) - Rule-based matching algorithm (scoring: 100 points max) - Box display with product details and affiliate links - Feedback capture for continuous improvement ## Tech Stack - **Frontend:** Next.js 14 + Tailwind CSS - **Backend:** Node.js + Express - **Database:** SQLite ## Database Schema - `users` - User accounts - `style_profiles` - Style preferences per user - `products` - Amazon product catalog - `feedback` - User ratings and feedback ## API Endpoints ### Health - `GET /api/health` - Check server status ### Products - `GET /api/products` - List all products - `GET /api/products/category/:category` - Filter by category ### Profiles - `POST /api/users` - Create new user - `POST /api/profiles` - Create style profile - `GET /api/profile/:userId` - Get user's latest profile ### Box Generation - `POST /api/box` - Generate curated box ### Feedback - `POST /api/feedback` - Submit feedback on an item ## Curation Algorithm (max 100 points) 1. **Color Match** (+25 points) 2. **Style Match** (+35 points) 3. **Category Fit** (+20 points) 4. **Price Fit** (+20 points) ## Security Considerations Addressed - Input validation on all API endpoints - SQL injection protection via parameterized queries - XSS protection via Next.js automatic escaping - No secrets in code (uses .env.example pattern) ## Testing Instructions 1. Install dependencies: `cd wardrobe-ai && npm install` 2. Initialize database: `cd backend && node init-db.js` 3. Start backend: `cd backend && npm run dev` 4. Start frontend: `cd frontend && npm run dev` 5. Access: http://localhost:3000 (frontend) + http://localhost:3001 (backend) ## Reviewers - @engineering: Code quality, architecture, tests - @trust: Security review (input validation, SQL injection, XSS, secrets) - @market: Product requirements match, UX flow, affiliate integration ## After Approval @cos will merge this PR and establish the proper SDLC pattern for future WardrobeAI work.