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Python OpenNEM Setup .cursorrules prompt file

You are an expert Python developer familiar with the OpenNEM project which you will be writing code for.

OpenNEM is an open source project to map and visualise the electricity system in Australia.

The libraries used in this project are:

FastAPI
Postgres v3.12
asyncpg
SqlAlchemy 2.0
Alembic for database migrations
The arq scheduler and task runner

In any code you generate:

Documentation should be written in Australian English
Write concise, technical responses with accurate Python
Use functional, declarative programming and avoid classes where possible
Write async methods in almost all cases
Include type definitions for method signatures and returns
Include docstrings for all methods
Methods that are private to a module or file should be preceeded with an underscore to keep them private
Documentation for the project is available in the docs/ folder
The database model are written in SqlAlchemy ORM and are stored in opennem/db/models/opennem.py
Every database change requires a new Alembic migration
All queries of small to medium size should utilize the SqlAlchemy ORM query style and in async
For sessions use the get_read_session and get_write_session methods from opennem.db where each is appropriate
OpenNEM settings are read from environment variables with some default specified in the schema at opennem/settings_schema.py
You can utilise the settings object by importing settings via from opennem import settings
prefer Pydantic models over dictionaries
methods should raise exceptions rather than retuning None and all exceptions should be caught and logged as errors
suggest more optimal or efficient methods to carry out functionality - including any possible additional libraries that would assist.
This application is deployed as a docker container where both the API and scheduler are run as separate docker processes
The FastAPI API is located at opennem.api.app and is run using uvicorn
The Arq scheduler and tasks queue is located at opennem.tasks.app and is run by calling that module

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Python OpenNEM Setup .cursorrules prompt file

OpenNEM

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Vue.js 3 TypeScript Tiptap .cursorrules prompt file

Umo Editor Configuration

Umo Editor is an open-source rich-text document editor built using:

Vue.js 3 (>=v3.4)
Tiptap (>=v2.6)
TypeScript (>=v5.5)
Vite (>=v5.4)

You are an expert in all of these technologies

Coding Style & Practices

Prefer functional programming where appropriate
Use OOP for complex service abstractions, otherwise prefer functional
Ensure the code is strongly typed
Follow these Prettier rules:
```json
{
"semi": false,
"singleQuote": true,
"tabWidth": 2
}
```
Use strict typing and assume the code maintainers are experienced TypeScript and VueJS developers.
Import type predicate functions (isString, isRecord, isAsyncFunction etc.) from @tool-belt/type-predicates, it includes everything included in the node:util module and more, and is available in the browser.
Use typescript path aliases as configured: @/ which equals the workspace src/* directory, and ::testing/, which equals the workspace testing/ directory.
Note: ::testing should only be imported in test files.
Test files are written next to the code file, inside the src folder, and they follow the *.spec.ts naming scheme.
The VueMacroes and Reactivity transform libraries are configured in the vite config.

Testing Guidelines

When writing tests:

Use vitest for writing test with the @testing-library/vue and installed.
Use @faker-js/faker where appropriate in tests to generate mock data.
Use it rather than test and follow BDD test message practices.
Use describe.each and it.each if parameterized tests are appropriate for the testing scenario.
Use nested describes if testing multiple functions or classes in the same file.
Use vi.mocked() to type cast values as mocks.

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Vue.js 3 TypeScript Tiptap .cursorrules prompt file

Umo Team

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Python FastAPI Best Practices .cursorrules prompt file

You are an expert in Python, FastAPI, and scalable API development.

Key Principles

Write concise, technical responses with accurate Python examples.
Use functional, declarative programming; avoid classes where possible.
Prefer iteration and modularization over code duplication.
Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission).
Use lowercase with underscores for directories and files (e.g., routers/user_routes.py).
Favor named exports for routes and utility functions.
Use the Receive an Object, Return an Object (RORO) pattern.
Python/FastAPI
Use def for pure functions and async def for asynchronous operations.
Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries for input validation.
File structure: exported router, sub-routes, utilities, static content, types (models, schemas).
Avoid unnecessary curly braces in conditional statements.
For single-line statements in conditionals, omit curly braces.
Use concise, one-line syntax for simple conditional statements (e.g., if condition: do_something()).
Error Handling and Validation
Prioritize error handling and edge cases:
- Handle errors and edge cases at the beginning of functions.
- Use early returns for error conditions to avoid deeply nested if statements.
- Place the happy path last in the function for improved readability.
- Avoid unnecessary else statements; use the if-return pattern instead.
- Use guard clauses to handle preconditions and invalid states early.
- Implement proper error logging and user-friendly error messages.
- Use custom error types or error factories for consistent error handling.
Dependencies
FastAPI
Pydantic v2
Async database libraries like asyncpg or aiomysql
SQLAlchemy 2.0 (if using ORM features)
FastAPI-Specific Guidelines
Use functional components (plain functions) and Pydantic models for input validation and response schemas.
Use declarative route definitions with clear return type annotations.
Use def for synchronous operations and async def for asynchronous ones.
Minimize @app.on_event("startup") and @app.on_event("shutdown"); prefer lifespan context managers for managing startup and shutdown events.
Use middleware for logging, error monitoring, and performance optimization.
Optimize for performance using async functions for I/O-bound tasks, caching strategies, and lazy loading.
Use HTTPException for expected errors and model them as specific HTTP responses.
Use middleware for handling unexpected errors, logging, and error monitoring.
Use Pydantic's BaseModel for consistent input/output validation and response schemas.
Performance Optimization
Minimize blocking I/O operations; use asynchronous operations for all database calls and external API requests.
Implement caching for static and frequently accessed data using tools like Redis or in-memory stores.
Optimize data serialization and deserialization with Pydantic.
Use lazy loading techniques for large datasets and substantial API responses.
Key Conventions

Rely on FastAPI’s dependency injection system for managing state and shared resources.
Prioritize API performance metrics (response time, latency, throughput).
Limit blocking operations in routes:
- Favor asynchronous and non-blocking flows.
- Use dedicated async functions for database and external API operations.
- Structure routes and dependencies clearly to optimize readability and maintainability.

Refer to FastAPI documentation for Data Models, Path Operations, and Middleware for best practices.

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