HR Management Cursor Rules Template for Cursor AI

Overview

Cursor rules configuration is a formal, copyable set of guidance for Cursor AI to generate and reason about code within a specific stack. This HR management Cursor Rules Template targets a Node.js/Express or NestJS backend with PostgreSQL and RBAC, focusing on employee profiles, leave requests, HR policies, document handling, and secure role-based access. It defines how Cursor AI should interpret domain models, enforce data isolation, and produce auditable code and configurations for a multi-tenant HR SaaS.

Direct answer: Use this Cursor Rules Template to guide AI-generated backend services, data models, and security checks for HR management SaaS with RBAC.

When to Use These Cursor Rules

Starting a new HR management SaaS with employee profiles, leaves, policies, and documents.
Enforcing robust role-based access control and data separation per tenant.
Embedding Cursor AI in code-generation, scaffolding, and CI/CD workflows.
Auditing AI outputs for security, privacy, and regulatory compliance.

Copyable .cursorrules Configuration


# Cursor Rules for HR SaaS (HR Management Cursor Rules Template)
# Framework Role & Context
Role: Backend/Full-Stack Engineer focused on HR SaaS
Stack: Node.js with Express or NestJS; PostgreSQL or relational DB; RBAC; JWT/OIDC
Context: HR domain - Employee profiles, leave requests, policies, documents; multi-tenant RBAC

# Code Style and Style Guides
Lang: TypeScript; ESLint with strict rules; Prettier formatting; strict null checks
Naming: camelCase for variables, PascalCase for types/classes, kebab-case for directories
Tests: Jest for unit/integration; TS-Check for type safety; commit hooks for lint

# Architecture & Directory Rules
Architecture: Modular monorepo; clear domain boundaries; domain-first models; separate API, services, and data layers
Directories: src/modules hr-and-policies, src/db, src/config, src/common, src/tests

# Authentication & Security Rules
Auth: JWT/OIDC with RBAC claims; access tokens short-lived; refresh tokens rotated
Security: do not hard-code secrets; use environment-based config; validate all inputs server-side; avoid permissive CORS in production
RBAC: roles: Admin, Manager, Employee; permissions bound to resources: employees, leaves, policies, documents

# Database and ORM patterns
DB: PostgreSQL; use parameterized queries; domain models map to tables: employees, leaves, policies, documents, roles, permissions
ORM: generic ORM patterns; avoid raw SQL in business logic; use repositories/services

# Testing & Linting Workflows
Tests: unit tests for models/services; integration tests for HR flows; end-to-end checks for leave and policy workflows
Lint/Format: ESLint + Prettier; CI checks on push/PR

# Prohibited Actions and Anti-patterns for the AI
Do not generate code that exposes production secrets or credentials
Do not bypass RBAC checks on server-side code
Do not hard-code tenant data or global admin keys
Do not mix tenant data across schemas or databases without isolation
Avoid relying on client-side authorization for security decisions

Recommended Project Structure


HR-SaaS/
src/
  config/
  api/
    auth/
    employees/
    leaves/
    policies/
    documents/
    rbac/
  db/
  modules/
  services/
  tests/
  middleware/
  shared/
package.json
tsconfig.json
.eslintrc.json
README.md
Dockerfile

Core Engineering Principles

Domain-first design: model employees, leaves, policies, and documents before UI concerns.
RBAC as a first-class citizen in data and API layers; enforce both at API and service boundaries.
Explicit, typed interfaces for data contracts and AI guidance to avoid drift.
Security by default: least privilege, short-lived tokens, and secret management.
Deterministic AI guidance with audit trails and reproducible outputs.

Code Construction Rules

Model HR domain with clear entities: Employee, LeaveRequest, Policy, Document, Role, Permission.
All database access must be via parameterized queries or ORM abstractions with proper typing.
RBAC checks must occur server-side for every protected endpoint; never rely on client-side enforcement.
AI-generated code should be deterministic given inputs; avoid non-deterministic naming or side effects.
Separate concerns: API layer, domain services, and data access; avoid cross-cutting logic in controllers.
Do not introduce hard-coded secrets; use environment/config management and secret stores.
Use centralized validation, error handling, and consistent API responses across modules.

Security and Production Rules

Use HTTPS, TLS mutual where possible; secure cookies and token storage on the client side.
Rotate and protect JWT/OIDC keys; implement short-lived access tokens and refresh tokens with rotation.
Audit logging for access and data-modifying actions; redact sensitive fields in logs where appropriate.
Input validation, rate limiting, and input sanitization to prevent injection and abuse.
Tenant isolation in multi-tenant deployments; ensure policy and document data cannot leak across tenants.

Testing Checklist

Unit tests for models and services with deterministic fixtures.
Integration tests for employee, leave, policy, and document workflows including RBAC checks.
Security tests: token validation, access control, and data isolation.
CI/CD pipeline checks for linting, type checks, and tests on PRs.
End-to-end tests for HR processes with realistic data samples.

Common Mistakes to Avoid

Relying on client-side authorization to enforce security.
Overly permissive RBAC schemas or hard-coded admin roles across tenants.
Coupling HR domain logic with presentation layer; violate separation of concerns.
Ignoring data retention and audit requirements in HR scenarios.
Neglecting secret management or secret rotation in production environments.

Related Cursor rules templates

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

FAQ

What is the Cursor Rules Template for HR SaaS?

The Cursor Rules Template for HR SaaS provides a structured, copyable set of Cursor AI rules tailored to a HR management stack. It covers employee profiles, leaves, policies, documents, and RBAC to guide AI-generated code, enforce security, and maintain data isolation in a multi-tenant HR system.

Can I adapt this template to multi-tenant deployments?

Yes. The template emphasizes tenant isolation, RBAC, and domain-driven data models. Adapt the RBAC permissions per tenant, enforce tenant-scoped policies, and ensure database schemas or partitions separate tenant data to avoid leakage.

How do I validate RBAC with Cursor AI?

Validate RBAC by defining resource-level permissions in the Cursor Rules block, ensuring server-side checks for every protected endpoint, and using unit/integration tests that assert access control across roles and tenants.

What security practices are recommended for HR data in Cursor AI?

Use short-lived tokens, rotate secrets, encrypt sensitive fields, redact secrets in logs, validate inputs server-side, and keep audit trails. Do not embed production credentials in AI prompts or generated code.

How do I integrate this template into my CI/CD?

Integrate with your CI/CD by including lint, type checks, and unit/integration tests in the pipeline. Validate AI-generated scaffolding for RBAC and security constraints, and require approvals before merging changes that affect access controls.

Is this template framework-agnostic?

The template targets Node.js-based HR SaaS stacks but can guide similar backends (Express or NestJS) and PostgreSQL schemas. Adjust ORM and directory layout to your chosen framework while preserving RBAC and HR domain models.

Target User

Use Cases