Employee Engagement Dashboards Cursor Rules Template

Overview

Cursor rules configuration for building employee engagement dashboards from survey data. Covers a stack of React frontend, Node.js Express backend, and PostgreSQL database.

When to Use These Cursor Rules

Implement dashboards that analyze survey responses
Standardize project structure and conventions across teams
Enforce security, testing, and deployment patterns
Facilitate reproducible analytics pipelines

Copyable .cursorrules Configuration

framework: node-express-react-postgres;
context: You are Cursor AI acting as a senior full-stack engineer building dashboards from survey data;
CodeStyle: language: typescript;
lint: eslint;
formatter: prettier;
tsConfig: recommended;
naming: camelCase;
comments: JSDoc;
Architecture: dirs: apps/dashboard;
services/api;
libs/ui;
data/migrations;
rules: backend API under services/api uses REST + typed responses;
frontend under apps/dashboard uses React + TypeScript;
shared UI components under libs/ui;
SecurityAndAuth: - auth: jwt;
- secrets: env;
- inputValidation: server-side mandatory;
- rateLimiting: per-ip;
- cors: restricted;
Database: type: PostgreSQL;
access: node-postgres;
schemaNotes: surveys, responses, users, dashboards;
migrations: sql-based;
DataAccessPatterns: - prepared statements;
- avoid ORM;
- materialized views for heavy analytics;
TestingAndCI: - unitTests: jest;
- integrationTests: supertest;
- e2eTests: playwright;
- linting: eslint + prettier;
- ci: lint && test;
ProhibitedActions: - do not embed secrets;
- do not bypass authentication;
- do not build SQL with string interpolation;
- do not trust client validation;
- do not ship untested analytics queries;

Recommended Project Structure

apps/dashboard
services/api
libs/ui
data/migrations
README.md

Core Engineering Principles

Secure by default and auditable
Typed contracts across API boundaries
Explicit data access over implicit ORM behavior
Separation of concerns between frontend, API, and data layer
Repeatable, test-driven dashboards and analytics

Code Construction Rules

Use TypeScript for frontend and backend with strict type checks
Validate and sanitize all survey inputs on server side
Query analytics data with parameterized SQL; avoid string concat
Paginate and cache heavy dashboards to prevent N+1 queries
Keep API routes small, idempotent, and well-documented

Security and Production Rules

JWT-based authentication; store tokens securely
Environment-based secrets; never commit secrets
Enable CORS only for trusted origins
Rate limiting and input validation to prevent abuse
Use encrypted connections and monitor for anomalies

Testing Checklist

Unit tests for data access helpers and utilities
Integration tests for API endpoints with mock data
E2E tests for dashboard user flows
CI checks for lint, tests, and migrations in a test env

Common Mistakes to Avoid

Relying on client-side validation alone
Hard-coding secrets or credentials
Using dynamic SQL without parameterization
Skipping tests for analytics queries
Ignoring role-based access control in dashboards

FAQ

What is a Cursor Rules Template for dashboards?

A Cursor Rules Template provides a copyable configuration block and engineering guidelines that tell Cursor AI how to assist building a specific software stack. This template covers employee engagement dashboards built from survey data using React, Node.js, and PostgreSQL, including security, testing, and deployment patterns.

Which stack does this template cover?

This template targets a React frontend with a Node.js Express backend and PostgreSQL data store. It includes guidelines for API design, data access patterns, and dashboard data modeling suitable for survey-based engagement analytics.

How do I use the copyable .cursorrules block?

Copy the entire code block under Copyable .cursorrules Configuration and paste it at the project root as .cursorrules. Cursor AI will read the rules to enforce stack-specific conventions, security, and testing requirements when guiding implementation.

Can I adapt this template to different databases?

Yes. The template is designed for PostgreSQL but the rules allow adapting to other SQL databases. Ensure parameterized queries, proper indexing, and migrate strategies align with the chosen database while preserving analytics capabilities.

How to extend for new metrics?

Extend the data model and SQL queries in backend services to calculate new metrics. Update the Cursor rules to reflect new analytics paths, add tests, and adjust schema.org keywords to improve discoverability.

Target User

Use Cases