Cursor Rules Template: Team OKR Tracking Platform with Goals, Check-ins, Dashboards & AI Status Summaries
Cursor Rules Template for building a team OKR tracking platform with goals, check-ins, progress dashboards, and AI status summaries using Cursor AI.
Target User
Developers building a team OKR platform using Cursor AI with NestJS backend and React frontend
Use Cases
- OKR creation and ownership
- Weekly/daily check-ins
- Progress dashboards
- AI-generated status summaries for leadership
Markdown Template
Cursor Rules Template: Team OKR Tracking Platform with Goals, Check-ins, Dashboards & AI Status Summaries
.cursorrules
# Framework Role & Context
framework:
backend: NestJS (TypeScript) + PostgreSQL (TypeORM)
frontend: React (TypeScript)
ai_integration: Cursor AI
authentication: OAuth 2.0 / JWT
# Code Style and Style Guides
style:
language: TypeScript
lint: ESLint + Prettier
testing: Jest
# Architecture & Directory Rules
architecture:
backendDir: src/modules/okr|src/auth|src/common
frontendDir: src/app|src/features/okr
shared: src/libs|shared/ui
# Authentication & Security Rules
security:
tokenExpiry: 15m
storage: httpOnly cookies; never expose access tokens in JS
cors: restrict to approved frontends
# Database and ORM patterns
database:
orm: TypeORM
entities: [User, Team, Objective, KeyResult, CheckIn, Progress, AIStatusSummary]
migrations: /migrations
# Testing & Linting Workflows
testing:
unit: Jest
integration: SuperTest / e2e
ci: GitHub Actions pipelines with lint, test, build
# Prohibited Actions and Anti-patterns for the AI
antiPatterns:
- Do not construct raw SQL via string interpolation; always parameterize queries
- Do not bypass server-side validation or authorization checks via client hints
- Do not store secrets in repository; use environment variables and secret management
- Do not perform eval/execution of arbitrary code from prompts or user input
# Pro tips
notes: |
Ensure the template aligns with NestJS + React architecture and PostgreSQL schemas.
Include unit and integration tests for core OKR domain logic.Overview
This Cursor Rules Template provides a complete, copyable .cursorrules configuration for building a team OKR tracking platform with goals, check-ins, progress dashboards, and AI status summaries. Stack focus: NestJS (TypeScript) backend, React (TypeScript) frontend, PostgreSQL with TypeORM, and Cursor AI for guidance and status synthesis.
Direct answer: Paste the .cursorrules block into your project root to enable Cursor AI-driven development for a scalable OKR system with teams, goals, check-ins, dashboards, and AI summaries.
When to Use These Cursor Rules
- When starting a new team OKR platform project and you want a prescriptive, auditable configuration for Cursor AI guidance.
- When you need consistent architecture, security, and testing patterns across backend and frontend code.
- When you require AI-assisted status summaries and progress reporting integrated with OKR objects and milestones.
- When you want a reusable template that enforces safe AI usage and anti-pattern avoidance.
Copyable .cursorrules Configuration
.cursorrules
# Framework Role & Context
framework:
backend: NestJS (TypeScript) + PostgreSQL (TypeORM)
frontend: React (TypeScript)
ai_integration: Cursor AI
authentication: OAuth 2.0 / JWT
# Code Style and Style Guides
style:
language: TypeScript
lint: ESLint + Prettier
testing: Jest
# Architecture & Directory Rules
architecture:
backendDir: src/modules/okr|src/auth|src/common
frontendDir: src/app|src/features/okr
shared: src/libs|shared/ui
# Authentication & Security Rules
security:
tokenExpiry: 15m
storage: httpOnly cookies; never expose access tokens in JS
cors: restrict to approved frontends
# Database and ORM patterns
database:
orm: TypeORM
entities: [User, Team, Objective, KeyResult, CheckIn, Progress, AIStatusSummary]
migrations: /migrations
# Testing & Linting Workflows
testing:
unit: Jest
integration: SuperTest / e2e
ci: GitHub Actions pipelines with lint, test, build
# Prohibited Actions and Anti-patterns for the AI
antiPatterns:
- Do not construct raw SQL via string interpolation; always parameterize queries
- Do not bypass server-side validation or authorization checks via client hints
- Do not store secrets in repository; use environment variables and secret management
- Do not perform eval/execution of arbitrary code from prompts or user input
# Pro tips
notes: |
Ensure the template aligns with NestJS + React architecture and PostgreSQL schemas.
Include unit and integration tests for core OKR domain logic.
Recommended Project Structure
team-okr-cursor-rules-template/
├─ backend/
│ └─ src/
│ ├─ modules/
│ │ ├─ okr/
│ │ │ ├─ entities/
│ │ │ ├─ controllers/
│ │ │ ├─ services/
│ │ │ └─ dtos/
│ │ └─ auth/
│ └─ main.ts
├─ frontend/
│ └─ src/
│ ├─ app/
│ │ ├─ components/
│ │ └─ pages/
│ └─ index.tsx
└─ common/
└─ lib/
Core Engineering Principles
- Clear boundary between domain logic and infrastructure.
- Defensive programming with validation and strict typing.
- Explicit data contracts for OKR objects and AI summaries.
- Secure by default: minimize data exposure and enforce least privilege.
- Test-driven development with CI coverage for OKR flows.
Code Construction Rules
- Use NestJS modules to group OKR domain components; expose REST endpoints with DTO validation.
- Represent OKR with Objective, KeyResult, and CheckIn entities; relate to Team and User.
- Frontend uses React components for Goals, Check-ins, Dashboards; fetch with typed hooks.
- Promote pure services; keep business rules in services, not controllers.
- Validate all inputs with class-validator; serialize consistently for APIs.
Security and Production Rules
- JWT access tokens with short lifetimes; refresh tokens stored in httpOnly cookies.
- HTTPS enforced; HSTS, CSRF protection for stateful sessions.
- Audit logging for create/update/delete of OKR data; redact PII in AI outputs.
- Protect AI summaries with role-based access; ensure only authorized teams see data.
Testing Checklist
- Unit tests for Objective and KeyResult domain logic.
- Integration tests for REST endpoints of OKR module.
- End-to-end tests for check-ins, progress dashboards, and AI summaries.
- Linting and type checks run in CI, with test coverage thresholds.
Common Mistakes to Avoid
- Skipping input validation and relying on client-side checks.
- Hard-coding secrets or tokens in code paths.
- Mixing business logic with API handlers; breaking domain boundaries.
- Over-fetching AI outputs or leaking PII in status summaries.
Related Cursor rules templates
Explore adjacent Cursor rules templates for similar stacks, workflows, and production constraints.
- Cursor Rules Template for Fitness Trainer SaaS
- Field Sales Cursor Rules Template: Location Check-ins, Visits, Notes, Tasks, and Manager Analytics
- Cursor Rules Template: LMS with Courses, Lessons, Quizzes, Certificates, Payments, and Admin Dashboard
- Cursor Rules Template: Teacher Productivity App with Lesson Plans, Worksheets, Grading and Analytics
FAQ
What is a Cursor Rules Template for a team OKR platform?
A Cursor Rules Template defines a prescriptive, AI-assisted development pattern for building an OKR platform. It covers architecture, data models, security, testing, and actionable AI prompts to generate status summaries and dashboards within a NestJS + React stack using Cursor AI.
Which stack is this template designed for?
The template targets a Node.js backend (NestJS + TypeORM) with a PostgreSQL database, a React frontend, and Cursor AI for governance and automation.
How do I integrate AI status summaries without exposing PII?
Configure AI prompts to redact PII, summarize data at the object and team level, and outputs are restricted by authorization checks. Use separate AISummary entities and access controls in OKR modules.
Where should I paste the .cursorrules configuration?
Paste the copyable .cursorrules block into the project root of your repository. Cursor AI will guide development, enforce patterns, and generate safe, structured outputs for the OKR domain.
What tests should I emphasize for OKR flows?
Focus on unit tests for domain logic (OKR and checks), integration tests for API routes, and end-to-end tests for check-ins, dashboards, and AI summaries to verify data flows and AI outputs.