Cursor Rules Template: BI SaaS with Data Connectors, Charts, Dashboards, SQL Editor and AI Explanations

Overview

Cursor rules configuration for our BI SaaS stack aligns Cursor AI with a modern data analytics platform. This template targets a Node.js (Express) backend, a React/TypeScript frontend, and PostgreSQL, with data connectors, charts, dashboards, a SQL editor, and AI explanations. The direct answer: paste the copyable .cursorrules block below into your project root to enforce consistent engineering and AI-assisted development for this stack.

When to Use These Cursor Rules

• Starting a BI SaaS project with data connectors and dashboard capabilities.
• Standardizing how AI explanations are generated for charts and queries.
• Enforcing architecture boundaries between frontend, backend, and connectors.
• Implementing secure data access, testing, and linting workflows from day one.

Copyable .cursorrules Configuration

// Cursor rules configuration for BI SaaS
framework: 'Node.js (Express) + React (TypeScript) + PostgreSQL'
context: 'BI SaaS with data connectors, charts, dashboards, a SQL editor, and AI explanations using Cursor AI'
codeStyle: 'TypeScript, ESLint, Prettier'
architecture: 'Monorepo: apps/frontend, apps/backend, packages/connectors, packages/ui, libs/db'
authentication: 'JWT with access scopes; OAuth 2.0 for admin'
database: 'PostgreSQL; use pg with prepared statements; avoid string concatenation to prevent SQL injection'
orm: 'Use raw SQL via pg; optional lightweight ORM for simple models'
testingLinting: 'Jest for unit tests; Supertest for API tests; Playwright for E2E; ESLint + Prettier in CI'
antiPatterns: 'Do not interpolate user input into SQL; Do not bypass auth; Do not trust client data; Do not ship credentials; Do not expose raw secrets'

Recommended Project Structure

my-bi-saas/
├── apps/
│   ├── frontend/          # React + TypeScript UI for dashboards, charts
│   └── backend/           # Node + Express API with SQL editor endpoints
├── packages/
│   ├── connectors/        # Data connectors to external sources
│   ├── ui/                # Shared UI components
│   └── db/                # DB schema and migrations
├── tests/
│   └── e2e/               # Playwright tests
└── scripts/               # CI/CD and tooling

Core Engineering Principles

• Explicit data access via parameterized queries and clear data access layers.
• Explicit boundaries between backend, frontend, and connectors.
• Cursor AI is an assistant; validate outputs before presenting to users.
• Security by default: least privilege, auditing, and RBAC at the API layer.
• CI/CD with lint, test, and build gates; deterministic builds.
• Typed contracts across services; avoid runtime type drift.

Code Construction Rules

• Use TypeScript end-to-end (frontend and backend).
• Frontend: React with typed components; UI for charts, dashboards, and SQL editor.
• Backend: Node.js + Express; expose REST endpoints for connectors and SQL editor features.
• PostgreSQL as the primary data store; parameterize all user inputs; avoid dynamic SQL creation.
• Data connectors implemented as services under packages/connectors with clear interfaces.
• Tests: unit tests for data transforms; integration tests for connectors; E2E tests for login, connectors, and SQL editor.
• Linting and formatting enforced via ESLint and Prettier in CI.
• Do not use full-ORMs that obscure SQL; prefer explicit SQL with a thin layer over pg.

Security and Production Rules

• JWTs with short expiry; rotate refresh tokens; validate scopes per endpoint.
• RBAC enforcement at backend for datasets and dashboards.
• TLS in transit; encryption at rest where applicable; secure DB access.
• Redact PII in logs; avoid leaking credentials; use environment-based secrets management.
• Rate limiting and input validation; guard data connectors against misuse.
• Audit trails for data exports and connector activity.

Testing Checklist

• Unit tests for data transformation and business rules.
• API tests for backend endpoints, including authentication and authorization.
• E2E tests for login, data connectors, chart creation, and SQL editor queries.
• Connector mocks for reliable CI; verify error handling and retry logic.
• Static type checks and linting in CI; ensure reproducible builds.

Common Mistakes to Avoid

• Overusing heavy ORMs that hide SQL and reduce visibility into queries.
• Exposing raw data or internal schema to the frontend without proper filtering.
• Skipping tests for data connectors or SQL execution paths.
• Weak authentication for admin connectors or data export endpoints.
• Inconsistent time zone handling in dashboards and SQL results.
• Not parameterizing SQL queries or not validating input on the backend.

Related Cursor rules templates

Explore adjacent Cursor rules templates for similar stacks, workflows, and production constraints.

• KPI Dashboard Builder — Cursor Rules Template for Cursor AI
• Cursor Rules Template — Real Estate Dashboard with Cash Flow, ROI, and Analytics
• Cursor Rules Template: Manufacturing Execution Dashboard (NestJS + PostgreSQL)
• Cursor Rules Template: Customer Success Platform with Health Scores, Renewals, NPS and AI Churn

FAQ

What is the Cursor Rules Template for BI SaaS?

This template defines Cursor AI rules for building a BI SaaS with data connectors, charts, dashboards, a SQL editor, and AI explanations. It provides explicit constraints, architecture boundaries, and recommended practices so developers can paste a copyable .cursorrules block into their project root and start coding with guidance.

Which stack does this template cover?

The template targets a Node.js (Express) backend, a React + TypeScript frontend, and PostgreSQL as the data store. It includes data connectors, a SQL editor component, charting, dashboards, and AI explanation features powered by Cursor AI.

How do I integrate data connectors and the SQL editor?

Connectors live in packages/connectors and expose a consistent interface for querying external sources. The SQL editor runs server-side queries via the backend API using parameterized SQL; all user input is validated and sanitized before execution.

How is security ensured in production?

Security is enforced by RBAC, token-based authentication, scoped access, TLS, encryption at rest, safe logging, and strict input validation. Data connectors are sandboxed, and audit trails capture connector usage and data access events.

How can I customize AI explanations in dashboards?

AI explanations are exposed via a dedicated service that analyzes query results and generates human-readable insights. You can tailor the explanation templates and confidence scoring per dataset, ensuring users receive relevant, explainable AI outputs.