pytest .cursorrules Cursor AI Prompt File Examples

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FastAPI .cursorrules prompt file guide

Code Style and Structure:

Follow PEP 8 guidelines for Python code style
Use meaningful variable and function names (snake_case for variables and functions, PascalCase for classes)
Keep functions small and focused on a single task
Use docstrings for modules, classes, and functions
Organize code into logical modules and packages
Use virtual environments for project isolation

Python-specific Conventions:

Prefer list comprehensions and generator expressions over map() and filter()
Use context managers (with statements) for resource management
Utilize decorators for cross-cutting concerns (e.g., logging, timing)
Leverage type hints for improved code readability and tooling support
Use f-strings for string formatting

Framework and API Development:

Use FastAPI as the primary framework for building high-performance APIs
Leverage FastAPI's automatic OpenAPI (Swagger) documentation
Utilize dependency injection for clean, testable code
Implement RESTful API design principles
Use Pydantic for data validation, serialization, and settings management
Define request and response models using Pydantic
Utilize Pydantic's BaseSettings for configuration management

Database and Data Modeling:

Use SQLModel, which combines SQLAlchemy core with Pydantic models
Leverage SQLModel's ability to use the same models for database tables and API schemas
Utilize SQLModel's async support for high-performance database operations
Implement database migrations using Alembic
Use connection pooling for efficient database connections
Implement proper indexing for frequently queried fields

Authentication and Security:

Implement JWT for stateless authentication
Use FastAPI's built-in security utilities
Use bcrypt for password hashing
Apply CORS policies using FastAPI's CORS middleware
Implement rate limiting for API endpoints (consider using FastAPI's dependencies for this)

Error Handling and Logging:

Use FastAPI's exception handlers for consistent error responses
Implement custom exception classes for application-specific errors
Use a logging framework (e.g., Python's built-in logging module)
Structure log messages for easy parsing and analysis

Frontend Development:

For server-side rendering, consider using FastAPI with Jinja2 templates
Implement responsive design using CSS frameworks (e.g., Bootstrap)
For single-page applications, use a JavaScript framework (e.g., React, Vue.js) that consumes your FastAPI backend

Testing:

Write unit tests using pytest
Implement integration tests for API endpoints using FastAPI's TestClient
Use factories (e.g., Factory Boy) for test data generation
Aim for high test coverage, especially for critical paths
Utilize Pydantic's parse_obj_as for easy test data creation

Asynchronous Processing:

Leverage FastAPI's asynchronous capabilities for concurrent processing
For background tasks, use FastAPI's background tasks feature or Celery for more complex scenarios
Implement message queues (e.g., RabbitMQ, Redis) for task management if needed

Deployment and DevOps:

Use Docker for containerization
Create optimized Dockerfiles for your FastAPI applications
Implement CI/CD pipelines (e.g., GitLab CI, GitHub Actions)
Use environment variables for configuration management
Utilize Pydantic's BaseSettings to load and validate environment variables
Implement health check endpoints for monitoring

Performance Optimization:

Leverage FastAPI's asynchronous capabilities for I/O-bound operations
Use caching mechanisms (e.g., Redis) for frequently accessed data
Implement database query optimization techniques with SQLModel
Profile code to identify and resolve bottlenecks

API Documentation:

Utilize FastAPI's automatic interactive API documentation (Swagger UI and ReDoc)
Keep API documentation up-to-date by maintaining accurate Pydantic models and FastAPI path operation functions

Version Control:

Use Git for version control
Implement feature branching and pull request workflows
Write meaningful commit messages

Dependencies Management

Use pip and requirements.txt for managing project dependencies
Consider using Poetry for more advanced dependency management

Remember to keep your code DRY (Don't Repeat Yourself), follow SOLID principles, and prioritize readability and maintainability in your Python full-stack development projects. Leverage the power of FastAPI, SQLModel, and Pydantic to create robust, type-safe, and high-performance applications.

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FastAPI .cursorrules prompt file guide

Rohit Nair

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Optimize Python .cursorrules PEP 8 async await Dataclasses .cursorrules prompt file

Python:

Use Python 3.8+ features
Follow PEP 8 style guide with a line length of 120 characters
Use type hints consistently
Prefer functional programming patterns where appropriate
Use async/await for asynchronous operations
Structure code into modular, reusable functions

Python Specifics:

Use f-strings for string formatting
Do not use f-strings for log messages (use 'Message with %s', expensive_func1())')
Use list/dict/set comprehensions where they improve readability
Utilize dataclasses or Pydantic models for data structures
Use pytest for testing

Naming Conventions:

Use snake_case for function and variable names
Use PascalCase for class names
Use UPPER_CASE for constants

Error Handling:

Use specific exception types
Implement proper logging
Return meaningful error messages and appropriate HTTP status codes in API responses

General:

Write clear, concise comments and docstrings
Keep functions and methods small and focused
Follow the DRY (Don't Repeat Yourself) principle
Implement proper error logging and monitoring
Use environment variables for configuration
Implement proper security measures (e.g., input validation, CORS, authentication)

Version Control:

Write meaningful commit messages
Use feature branches and pull requests for new features or significant changes
Keep commits small and focused

Documentation:

Maintain up-to-date README files
Document API endpoints and their usage
Keep inline documentation current with code changes

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Optimize Python .cursorrules PEP 8 async await Dataclasses .cursorrules prompt file

markusedgar

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TypeScript Python FastAPI React Best Practices .cursorrules prompt file

Frontend (TypeScript/React):

Use functional components with TypeScript interfaces
Prefer named exports for components
Use declarative programming patterns; avoid classes where possible
Structure files: exported component, subcomponents, helpers, static content, types
Use descriptive variable names (e.g., isLoading, hasError)
Implement responsive design with MUI (Material-UI)
Use dynamic imports for non-critical components
Optimize images: use appropriate formats, include size data, implement lazy loading
Minimize use of useEffect; favor React Server Components where applicable
Wrap client components in Suspense with fallback for better loading experience

TypeScript Usage:

Use TypeScript for all frontend code
Prefer interfaces over types for better extensibility
Avoid enums; use string literal unions or const objects instead
Enable strict mode in tsconfig.json

Naming Conventions:

Use PascalCase for component files (e.g., SubscriptionList.tsx)
Use camelCase for non-component files (e.g., urlUtils.ts)
Use lowercase with dashes for directories (e.g., components/add-subscription)

Syntax and Formatting:

Use arrow functions for component definitions and callbacks
Avoid unnecessary curly braces in conditionals; use concise syntax for simple statements
Use optional chaining (?.) and nullish coalescing (??) operators
Prefer template literals over string concatenation

Error Handling and Validation:

Handle errors and edge cases early in the function
Use try/catch blocks for async operations
Implement proper error logging and user-friendly messages
Use appropriate form validation (consider using a library like Formik or react-hook-form)

State Management:

Use React hooks (useState, useContext) for local and shared state
Consider using a state management library for complex state (e.g., Redux, Recoil)

Backend (Python):

Use Python 3.8+ features
Follow PEP 8 style guide with a line length of 120 characters
Use type hints consistently
Prefer functional programming patterns where appropriate
Use async/await for asynchronous operations
Structure code into modular, reusable functions

Python Specifics:

Use f-strings for string formatting
Do not use f-strings for log messages (use 'Message with %s', expensive_func1())')
Use list/dict/set comprehensions where they improve readability
Utilize dataclasses or Pydantic models for data structures
Use pytest for testing

Naming Conventions:

Use snake_case for function and variable names
Use PascalCase for class names
Use UPPER_CASE for constants

Error Handling:

Use specific exception types
Implement proper logging
Return meaningful error messages and appropriate HTTP status codes in API responses

API Design:

Follow RESTful principles
Use FastAPI for API development
Implement proper request validation and error handling
Use Pydantic models for request/response schemas

Database:

Use SQLModel for ORM (https://sqlmodel.tiangolo.com/)
Use SQLAlchemy for database operations when needed but prefer to use SQLModel
Implement database migrations for schema changes
Use async database operations where possible

General:

Write clear, concise comments and docstrings
Keep functions and methods small and focused
Follow the DRY (Don't Repeat Yourself) principle
Implement proper error logging and monitoring
Use environment variables for configuration
Implement proper security measures (e.g., input validation, CORS, authentication)

Version Control:

Write meaningful commit messages
Use feature branches and pull requests for new features or significant changes
Keep commits small and focused

Testing:

Write unit tests for both frontend and backend code
Aim for high test coverage, especially for critical paths
Use mocking for external dependencies in tests

Performance:

Optimize database queries
Implement caching where appropriate
Use pagination for large datasets

Accessibility:

Ensure proper keyboard navigation
Use semantic HTML elements
Provide alternative text for images

Documentation:

Maintain up-to-date README files
Document API endpoints and their usage
Keep inline documentation current with code changes

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TypeScript Python FastAPI React Best Practices .cursorrules prompt file

Bryan Thode

NEW

Flask-RESTful JWT RESTX .cursorrules prompt file

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

Key Principles

Write concise, technical responses with accurate Python examples.
Use functional, declarative programming; avoid classes where possible except for Flask views.
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., blueprints/user_routes.py).
Favor named exports for routes and utility functions.
Use the Receive an Object, Return an Object (RORO) pattern where applicable.
Python/Flask
Use def for function definitions.
Use type hints for all function signatures where possible.
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
Flask
Flask-RESTful (for RESTful API development)
Flask-JWT-Extended (for JWT authentication)
Flask-Specific Guidelines
Use Flask application factories for better modularity and testing.
Organize routes using Flask Blueprints for better code organization.
Use Flask-RESTful for building RESTful APIs with class-based views.
Implement custom error handlers for different types of exceptions.
Use Flask's before_request, after_request, and teardown_request decorators for request lifecycle management.
Utilize Flask extensions for common functionalities (e.g., Flask-SQLAlchemy, Flask-Migrate).
Use Flask's config object for managing different configurations (development, testing, production).
Implement proper logging using Flask's app.logger.
Performance Optimization
Use Flask-Caching for caching frequently accessed data.
Use background tasks for time-consuming operations (e.g., Celery with Flask).
Key Conventions

Use Flask's application context and request context appropriately.
Prioritize API performance metrics (response time, latency, throughput).
Structure the application:
- Use blueprints for modularizing the application.
- Implement a clear separation of concerns (routes, business logic, data access).
- Use environment variables for configuration management.

Testing

Write unit tests using pytest.
Use Flask's test client for integration testing.
Implement test fixtures for database and application setup.
API Documentation
Use Flask-RESTX or Flasgger for Swagger/OpenAPI documentation.
Ensure all endpoints are properly documented with request/response schemas.
Deployment
Implement proper logging and monitoring in production.
Use environment variables for sensitive information and configuration.
Refer to Flask documentation for detailed information on Views, Blueprints, and Extensions for best practices.

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Flask-RESTful JWT RESTX .cursorrules prompt file

David V

NEW

Python .cursorrules prompt file best practices

You are an AI assistant specialized in Python development. Your approach emphasizes:

Clear project structure with separate directories for source code, tests, docs, and config.
Modular design with distinct files for models, services, controllers, and utilities.
Configuration management using environment variables.
Robust error handling and logging, including context capture.
Comprehensive testing with pytest.
Detailed documentation using docstrings and README files.
Dependency management via https://github.com/astral-sh/rye and virtual environments.
Code style consistency using Ruff.
CI/CD implementation with GitHub Actions or GitLab CI.
AI-friendly coding practices:
- Descriptive variable and function names
- Type hints
- Detailed comments for complex logic
- Rich error context for debugging

You provide code snippets and explanations tailored to these principles, optimizing for clarity and AI-assisted development.

Follow the following rules:

For any python file, be sure to ALWAYS add typing annotations to each function or class. Be sure to include return types when necessary. Add descriptive docstrings to all python functions and classes as well. Please use pep257 convention. Update existing docstrings if need be.

Make sure you keep any comments that exist in a file.

When writing tests, make sure that you ONLY use pytest or pytest plugins, do NOT use the unittest module. All tests should have typing annotations as well. All tests should be in ./tests. Be sure to create all necessary files and folders. If you are creating files inside of ./tests or ./src/goob_ai, be sure to make a init.py file if one does not exist.

All tests should be fully annotated and should contain docstrings. Be sure to import the following if TYPE_CHECKING:
from _pytest.capture import CaptureFixture
from _pytest.fixtures import FixtureRequest
from _pytest.logging import LogCaptureFixture
from _pytest.monkeypatch import MonkeyPatch
from pytest_mock.plugin import MockerFixture

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Python .cursorrules prompt file best practices

Malcolm Jones (bossjones/Tony Dark)

NEW

Python GitHub Setup .cursorrules prompt file

{
"general": {
"coding_style": {
"language": "Python",
"use_strict": true,
"indentation": "4 spaces",
"max_line_length": 120,
"comments": {
"style": "# for single-line, ''' for multi-line",
"require_comments": true
}
},
"naming_conventions": {
"variables": "snake_case",
"functions": "snake_case",
"classes": "PascalCase",
"interfaces": "PascalCase",
"files": "snake_case"
},
"error_handling": {
"prefer_try_catch": true,
"log_errors": true},
"testing": {
"require_tests": true,
"test_coverage": "80%",
"test_types": ["unit", "integration"]
},
"documentation": {
"require_docs": true,
"doc_tool": "docstrings",
"style_guide": "Google Python Style Guide"
},
"security": {
"require_https": true,
"sanitize_inputs": true,
"validate_inputs": true,
"use_env_vars": true
},
"configuration_management": {
"config_files": [".env"],
"env_management": "python-dotenv",
"secrets_management": "environment variables"
},
"code_review": {
"require_reviews": true,
"review_tool": "GitHub Pull Requests",
"review_criteria": ["functionality", "code quality", "security"]
},
"version_control": {
"system": "Git",
"branching_strategy": "GitHub Flow",
"commit_message_format": "Conventional Commits"
},
"logging": {
  "logging_tool": "Python logging module",
  "log_levels": ["debug", "info", "warn", "error"],
  "log_retention_policy": "7 days"
  },
  "monitoring": {
  "monitoring_tool": "Not specified",
  "metrics": ["file processing time", "classification accuracy", "error rate"]
  },
  "dependency_management": {
  "package_manager": "pip",
  "versioning_strategy": "Semantic Versioning"
  },
  "accessibility": {
  "standards": ["Not applicable"],
  "testing_tools": ["Not applicable"]
  },
  "internationalization": {
  "i18n_tool": "Not applicable",
  "supported_languages": ["English"],
  "default_language": "English"
  },
  "ci_cd": {
  "ci_tool": "GitHub Actions",
  "cd_tool": "Not specified",
  "pipeline_configuration": ".github/workflows/main.yml"
  },
  "code_formatting": {
  "formatter": "Black",
  "linting_tool": "Pylint",
  "rules": ["PEP 8", "project-specific rules"]
  },
  "architecture": {
    "patterns": ["Modular design"],
    "principles": ["Single Responsibility", "DRY"]
    }
    },
    "project_specific": {
    "use_framework": "None",
    "styling": "Not applicable",
    "testing_framework": "pytest",
    "build_tool": "setuptools",
    "deployment": {
    "environment": "Local machine",
    "automation": "Not specified",
    "strategy": "Manual deployment"
    },
    "performance": {
    "benchmarking_tool": "Not specified",
    "performance_goals": {
    "response_time": "< 5 seconds per file",
    "throughput": "Not specified",
    "error_rate": "< 1%"
    }
    }
    },
    "context": {
      "codebase_overview": "Python-based file organization tool using AI for content analysis and classification",
      "libraries": ["watchdog", "spacy", "PyPDF2", "python-docx", "pandas", "beautifulsoup4", "transformers", "scikit-learn", "joblib", "python-dotenv", "torch", "pytest", "shutil", "logging", "pytest-mock"],
      "coding_practices": {
      "modularity": true,
      "DRY_principle": true,
      "performance_optimization": true
      }
      },
      "behavior": {
      "verbosity": {
      "level": 2,
      "range": [0, 3]
      },
      "handle_incomplete_tasks": "Provide partial solution and explain limitations",
      "ask_for_clarification": true,
      "communication_tone": "Professional and concise"
      }
      }

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Python GitHub Setup .cursorrules prompt file

Jesse Naldo

NEW

PyTorch Scikit-learn .cursorrules Prompt File

You are an expert in developing machine learning models for chemistry applications using Python, with a focus on scikit-learn and PyTorch.

Key Principles:

- Write clear, technical responses with precise examples for scikit-learn, PyTorch, and chemistry-related ML tasks.
- Prioritize code readability, reproducibility, and scalability.
- Follow best practices for machine learning in scientific applications.
- Implement efficient data processing pipelines for chemical data.
- Ensure proper model evaluation and validation techniques specific to chemistry problems.

Machine Learning Framework Usage:

- Use scikit-learn for traditional machine learning algorithms and preprocessing.
- Leverage PyTorch for deep learning models and when GPU acceleration is needed.
- Utilize appropriate libraries for chemical data handling (e.g., RDKit, OpenBabel).

Data Handling and Preprocessing:

- Implement robust data loading and preprocessing pipelines.
- Use appropriate techniques for handling chemical data (e.g., molecular fingerprints, SMILES strings).
- Implement proper data splitting strategies, considering chemical similarity for test set creation.
- Use data augmentation techniques when appropriate for chemical structures.

Model Development:

- Choose appropriate algorithms based on the specific chemistry problem (e.g., regression, classification, clustering).
- Implement proper hyperparameter tuning using techniques like grid search or Bayesian optimization.
- Use cross-validation techniques suitable for chemical data (e.g., scaffold split for drug discovery tasks).
- Implement ensemble methods when appropriate to improve model robustness.

Deep Learning (PyTorch):

- Design neural network architectures suitable for chemical data (e.g., graph neural networks for molecular property prediction).
- Implement proper batch processing and data loading using PyTorch's DataLoader.
- Utilize PyTorch's autograd for automatic differentiation in custom loss functions.
- Implement learning rate scheduling and early stopping for optimal training.

Model Evaluation and Interpretation:

- Use appropriate metrics for chemistry tasks (e.g., RMSE, R², ROC AUC, enrichment factor).
- Implement techniques for model interpretability (e.g., SHAP values, integrated gradients).
- Conduct thorough error analysis, especially for outliers or misclassified compounds.
- Visualize results using chemistry-specific plotting libraries (e.g., RDKit's drawing utilities).

Reproducibility and Version Control:

- Use version control (Git) for both code and datasets.
- Implement proper logging of experiments, including all hyperparameters and results.
- Use tools like MLflow or Weights & Biases for experiment tracking.
- Ensure reproducibility by setting random seeds and documenting the full experimental setup.

Performance Optimization:

- Utilize efficient data structures for chemical representations.
- Implement proper batching and parallel processing for large datasets.
- Use GPU acceleration when available, especially for PyTorch models.
- Profile code and optimize bottlenecks, particularly in data preprocessing steps.

Testing and Validation:

- Implement unit tests for data processing functions and custom model components.
- Use appropriate statistical tests for model comparison and hypothesis testing.
- Implement validation protocols specific to chemistry (e.g., time-split validation for QSAR models).

Project Structure and Documentation:

- Maintain a clear project structure separating data processing, model definition, training, and evaluation.
- Write comprehensive docstrings for all functions and classes.
- Maintain a detailed README with project overview, setup instructions, and usage examples.
- Use type hints to improve code readability and catch potential errors.

Dependencies:

- NumPy
- pandas
- scikit-learn
- PyTorch
- RDKit (for chemical structure handling)
- matplotlib/seaborn (for visualization)
- pytest (for testing)
- tqdm (for progress bars)
- dask (for parallel processing)
- joblib (for parallel processing)
- loguru (for logging)

Key Conventions:

1. Follow PEP 8 style guide for Python code.
2. Use meaningful and descriptive names for variables, functions, and classes.
3. Write clear comments explaining the rationale behind complex algorithms or chemistry-specific operations.
4. Maintain consistency in chemical data representation throughout the project.

Refer to official documentation for scikit-learn, PyTorch, and chemistry-related libraries for best practices and up-to-date APIs.

Note on Integration with Tauri Frontend:

- Implement a clean API for the ML models to be consumed by the Flask backend.
- Ensure proper serialization of chemical data and model outputs for frontend consumption.
- Consider implementing asynchronous processing for long-running ML tasks.

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PyTorch Scikit-learn .cursorrules Prompt File

Aravindh Marimuthu

NEW

Python Projects Guide .cursorrules prompt file

You are an AI assistant specialized in Python development. Your approach emphasizes:

1. Clear project structure with separate directories for source code, tests, docs, and config.

2. Modular design with distinct files for models, services, controllers, and utilities.

3. Configuration management using environment variables.

4. Robust error handling and logging, including context capture.

5. Comprehensive testing with pytest.

6. Detailed documentation using docstrings and README files.

7. Dependency management via https://github.com/astral-sh/rye and virtual environments.

8. Code style consistency using Ruff.

9. CI/CD implementation with GitHub Actions or GitLab CI.

10. AI-friendly coding practices:
  - Descriptive variable and function names
  - Type hints
  - Detailed comments for complex logic
  - Rich error context for debugging

You provide code snippets and explanations tailored to these principles, optimizing for clarity and AI-assisted development.

stack: