Cursor Rules Template: Cash Flow SaaS with Bank Imports, Invoices, and AI Predictions

Overview

The Cursor rules configuration is a formal, copyable set of guardrails and conventions used by Cursor AI to guide the development of a cash flow forecasting SaaS for small businesses. This page targets the Node.js + PostgreSQL stack, including bank data imports, invoice processing, and AI-based forecasting. It provides a concrete, copyable .cursorrules configuration you can paste into your project root to ensure consistent architecture, security, and repeatable CI/CD across environments.

When to Use These Cursor Rules

• Starting a greenfield SaaS that ingests bank transactions and invoices for forecast accuracy.
• Enforcing a multi-tenant data model with strict data isolation per merchant.
• Setting clear AI forecasting guardrails, evaluation metrics, and testing workflows.
• Onboarding new engineers to the exact project structure and coding standards.
• Auditing architecture decisions and validating production-readiness for banking data pipelines.

Copyable .cursorrules Configuration

// Cursor Rules Template for CashFlow SaaS
Framework: Node.js with TypeScript, Express, PostgreSQL
Role & Context: You are Cursor AI assistant guiding the build of a multi-tenant cash flow forecasting SaaS for small businesses. The system ingests bank imports and invoices, and uses AI predictions to forecast cash flow.
Code Style & Standards: TypeScript strict mode; ESLint + Prettier; tsconfig strict; no implicit any; descriptive types; strict null checks
Architecture & Directory Rules:
  api/src/controllers
  api/src/routes
  api/src/models
  api/src/services
  api/src/db
  api/src/migrations
  api/src/config
  ai/predictors
  ingestion/bank
  ingestion/invoices
  ui/components
  ui/pages
  tests
  infra
Authentication & Security:
  - JWT-based API authentication for all endpoints
  - Per-merchant tenancy isolation in data layer
  - Secrets management via environment variables; do not log secrets
Database & ORM patterns:
  - PostgreSQL with pg (node-postgres)
  - Use parameterized queries; avoid string concatenation
  - Migrations folder for schema changes; seed data in CI only
  - No heavy ORM; prefer raw SQL with a light query builder if needed
Testing & Linting Workflows:
  - Jest for unit tests; Supertest for integration tests
  - ESLint + Prettier; type-check in CI
  - GitHub Actions: lint, test, build, and deploy pipelines
Prohibited Actions & Anti-patterns:
  - Do not embed secrets in code or commit history
  - Do not bypass authentication or data isolation
  - Do not perform destructive migrations in CI without review
  - Do not rely on client-side code for server-only logic
  - Do not bypass data validation on ingest paths

Recommended Project Structure

cashflow-saas/
├── api/
│   └── src/
│       ├── controllers/
│       ├── routes/
│       ├── models/
│       ├── services/
│       ├── db/
│       │   ├── migrations/
│       │   └── seeds/
│       ├── config/
│       └── index.ts
├── client/
│   └── app/
│       ├── components/
│       └── pages/
├── ai/
│   ├── predictors/
│   └── pipelines/
├── ingestion/
│   ├── bank/
│   └── invoices/
├── tests/
├── infra/
│   ├── docker/
│   └── k8s/
└── docs/

Core Engineering Principles

• Data-first design with strong validation and schema adherence
• Tenant isolation by design and least-privilege access
• Guardrails for AI usage and deterministic forecasting results
• Observability: tracing, metrics, centralized logging
• Automation: CI/CD, infrastructure as code, repeatable releases
• Security by default: secure defaults, secrets rotation, minimal surface

Code Construction Rules

• Use TypeScript with strict mode; define clear interfaces for bank imports and invoices
• Ingest bank transactions and invoices via safe parsers with schema validation
• AI prediction pipeline should be isolated, reproducible, and auditable
• All database writes use parameterized queries; avoid raw string interpolation
• Make modules small and composable; export small, testable units
• Testing: unit tests for parsers, integration tests for API endpoints, end-to-end tests for core flows
• Do not rely on client-side logic to perform server-side computations

Security and Production Rules

• Secrets stored in environment variables; never commit keys
• JWT or OAuth2 for API authentication; implement token expiry and rotation
• Least-privilege database roles and row-level security where applicable
• Enable encryption at rest for the database and encryption in transit (TLS)
• Rate limiting and anomaly detection on ingestion endpoints
• Feature flags to safely enable AI features in production

Testing Checklist

• Unit tests for parsers and utilities in ingestion and AI modules
• Integration tests for authentication, API routes, and data ingestion
• End-to-end tests for bank import → forecast → alert workflow
• CI checks: TypeScript compile, lint, test, and a dry run of migrations
• Load and performance tests for the forecasting pipeline under realistic data volumes

Common Mistakes to Avoid

• Skipping data validation on bank/imported invoices leading to forecast false positives
• Assuming single-tenant data can scale without isolation controls
• Hardcoding AI thresholds; prefer configurable, auditable parameters
• Overcomplicating the data model with unneeded abstractions
• Neglecting entity versioning for data evolution in migrations

Related Cursor rules templates

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

• Cursor Rules Template: Rental Marketplace with Property Listings, Applications, Tenant Screening, Payments and Messaging
• Cursor Rules Template: Sales Pipeline SaaS with Lead Scoring, Email Tracking, and AI Dashboards
• Cursor Rules Template: Express ATS with Cursor AI
• Cursor Rules Template: SaaS Revenue Dashboard (MRR, ARR, Churn & Cohorts)

FAQ

What is the purpose of this Cursor Rules Template?

This template provides concrete guardrails and implementation constraints for building a cash flow forecasting SaaS with bank imports, invoices, and AI predictions. It helps ensure a repeatable architecture, secure data handling, and testable AI-driven forecasting on the Node.js + PostgreSQL stack.

Which stack does this Cursor Rules Template cover?

The template targets Node.js with TypeScript, Express, and PostgreSQL. It includes modules for bank data ingestion, invoice processing, AI-driven forecasting, and multi-tenant data isolation suitable for a SaaS.

How do I apply the .cursorrules block?

Copy the entire code block from the Copyable section and paste it at the project root as .cursorrules. Cursor AI will use these rules to guide code generation, scaffolding, and guardrails during implementation and reviews.

What are common anti-patterns to avoid?

Avoid embedding secrets, bypassing authentication, performing production data operations in tests, and relying on client-side logic for server-side tasks. Keep AI-generated code auditable and testable with clear boundaries between ingestion, processing, and forecasting.

How can I extend this template for multi-tenant scenarios?

Extend the data model with explicit tenant boundaries, per-merchant accounts, and row-level security. Use tenancy-aware services and ensure API routing enforces tenant context for every data operation.

Where can I find related Cursor Rules templates?

See the Cursor Rules Templates collection for stack-specific templates and consider exploring Node.js + PostgreSQL or other backend stacks to maintain consistency across projects.