Cursor Rules Template: Real Estate Buyer Matching with Property Inquiries

Overview

Direct answer: This Cursor Rules Template provides a copyable .cursorrules configuration for building real estate buyer matching tools that rely on property inquiries on a Next.js + PostgreSQL stack. It enforces consistent AI behavior, security, and stack-specific conventions so you can paste directly into your project root.

Cursor AI is leveraged to interpret inquiries, rank listings, and surface matches while respecting privacy and security constraints. The template aligns AI prompts with the data models typical for real estate apps.

When to Use These Cursor Rules

When you need consistent AI prompts for transforming property inquiries into buyer intent signals.
When building a buyer matching workflow that integrates with Next.js API routes and a PostgreSQL database.
When you want strict authentication, secrets handling, and audit-friendly AI prompts.
When you need a ready-to-paste configuration to accelerate initial setup and reduce prompt drift.

Copyable .cursorrules Configuration

frameworkRole: 'Frontend + API layers in Next.js, using PostgreSQL for inquiries and matches.'
frameworkContext: 'You are Cursor AI helping implement a real estate buyer matching flow.'
codeStyle: 'TypeScript, ESLint, Prettier, explicit types, no implicit any.'
architectureAndDirectoryRules:
  - root: '.'
  - apps/web: 'Next.js app with pages and app router'
  - db: 'PostgreSQL schema and migrations'
  - services: 'domain services for inquiries and matching'
  - tests: 'unit, integration, and e2e test suites'
authenticationAndSecurity:
  - auth: 'JWT in HttpOnly cookies; CSRF protection; token rotation'
  - secrets: 'Environment variables; secrets not logged'
databaseAndOrmPatterns:
  - database: 'PostgreSQL'
  - queries: 'Parameterized queries; avoid N+1; use prepared statements'
  - migrations: 'SQL-based migrations under db/migrations'
testingAndLintingWorkflows:
  - unitTests: 'Jest + React Testing Library; server tests with supertest'
  - e2e: 'Cypress or Playwright'
  - lint: 'ESLint + Prettier; pre-commit hooks with lint-staged'
prohibitedActionsAndAntiPatterns:
  - 'Do not embed API keys in prompts or code'
  - 'Do not call external AI services from client-side code without rate limiting'
  - 'Do not bypass authentication or authorization checks'
  - 'Do not log PII or sensitive data in plaintext'

Recommended Project Structure

cursor-real-estate-buyer-matcher/
  apps/
    web/
      pages/
      components/
      hooks/
      styles/
  db/
    migrations/
    seeds/
  services/
    inquiries/
    matching/
  tests/
  package.json
  tsconfig.json
  .eslintrc.json

Core Engineering Principles

Explicit, typed interfaces for data models and prompts.
Separation of concerns between UI, API, and data access.
Deterministic AI prompts with defined success criteria.
Data privacy and minimal data exposure by design.
Testability: unit, integration, and end-to-end tests included.
Observability: structured logging and metrics for AI prompts and matches.
Security: secure authentication, secret management, and input validation.

Code Construction Rules

Use Next.js App Router for UI and API routes; keep server and client code separate.
Define TypeScript types for inquiries, buyers, and matches; reuse across layers.
Queries should be parameterized; validate inputs robustly in API handlers.
Prompts must derive from a stable schema: user intents, constraints, and business rules.
Avoid coupling AI prompts to UI logic; decouple via service layer.
All data access should go through a small, testable data-access layer.
Do not rely on unsafe external calls in rendering paths; cache risk-prone results when needed.

Security and Production Rules

Use HttpOnly cookies for auth tokens; enable CSRF protection for state-changing requests.
Store secrets in environment variables; never commit to repo.
Validate and sanitize all user input; implement server-side validation for inquiries.
Limit AI prompt request sizes; monitor and rate-limit API usage.
Enable logging with redaction for PII; implement secure error handling.

Testing Checklist

Unit tests for inquiry parsing and ranking logic.
Integration tests for database access and API endpoints.
End-to-end tests that simulate real property inquiries leading to matches.
Linting and type-checking as part of CI; run on pre-commit hooks.
Security tests: input validation, auth flows, and data exposure checks.

Common Mistakes to Avoid

Hardcoding business rules into UI; keep logic in a service layer.
Over-fetching data in API responses; implement precise projections.
Ignoring data privacy when logging AI prompts and inquiries.
Neglecting migrations and schema drift during development.

FAQ

What is a Cursor Rules Template?

A Cursor Rules Template is a copyable, stack-specific configuration for Cursor AI that standardizes how AI interprets inputs and enforces rules. This template targets a real estate buyer matching workflow using property inquiries on a Next.js + PostgreSQL stack, and includes a ready-to-paste .cursorrules block.

Which stack does this template apply to?

The template is designed for a Next.js frontend with API routes and a PostgreSQL database, focusing on real estate buyer matching driven by property inquiries and client preferences.

How do I integrate the copyable .cursorrules block?

Place the copied block at the project root as .cursorrules. It then guides Cursor AI to behave consistently across prompts, aligning with the defined architecture, security, and testing constraints for the stack.

What should I customize for my project?

Adapt the project structure, database schema references, and authentication flows to your environment. Keep the AI prompts aligned with your data models, and update the migrations, endpoints, and tests accordingly.

How do I test prompts and ranking logic?

Use unit tests for parsing inquiries, integration tests for DB queries, and end-to-end tests that simulate real property inquiries leading to matches. Ensure prompts produce deterministic, testable outputs.

Target User

Use Cases