Project Rules

Comprehensive guidelines for effective Maven project management, covering code organization, dependency management, performance optimization, and security best practices. This rule provides actionable advice to avoid common pitfalls and promote maintainable, scalable Maven projects.

This rule provides best practices for developing with Microsoft Teams, covering code organization, performance, security, testing, and common pitfalls. Adhering to these guidelines will ensure robust, maintainable, and secure Teams applications.

Comprehensive guidelines for mkdocs development, covering code organization, best practices, performance, security, testing, common pitfalls, and tooling. This rule aims to ensure maintainable, performant, and secure documentation using mkdocs.

This rule provides comprehensive best practices for the MLX library, covering code organization, performance, security, testing, and common pitfalls. It aims to promote consistent, efficient, and maintainable code when working with MLX on Apple platforms.

This rule provides comprehensive guidance for using MobX effectively, covering best practices for code organization, performance, testing, and common pitfalls. It aims to ensure efficient and maintainable state management in React and other JavaScript applications using MobX.

This rule provides comprehensive best practices and coding standards for using the Mockito library in Java projects. It covers code organization, patterns, performance, security, testing, and common pitfalls to enhance test reliability and maintainability.

This rule outlines best practices for developing and maintaining the Modal library, covering code organization, performance, security, and testing. It aims to ensure high-quality, maintainable, and scalable cloud deployment solutions.

Comprehensive best practices for developing with MongoDB, covering schema design, code organization, performance optimization, security considerations, and testing strategies. This rule provides actionable guidance to help developers build robust and scalable MongoDB applications.

This rule file outlines best practices for using mypy in Python projects, emphasizing gradual adoption, consistent configuration, and leveraging advanced features for improved code quality and maintainability. It covers code organization, performance, security, testing, common pitfalls, and tooling.

This rule provides guidelines for best practices and coding standards when developing applications with Neo4j. It covers aspects from code organization and performance to security and testing.

This rule provides comprehensive guidance on NestJS best practices, coding standards, and architectural patterns. It aims to help developers build scalable, maintainable, and performant NestJS applications by covering code organization, security, testing, and other essential aspects.

This rule file outlines best practices for Netlify development, covering code structure, performance, security, testing, and deployment. It aims to provide a comprehensive guide for building robust and scalable applications on Netlify.

This rule provides comprehensive guidance for Next.js development, covering code organization, performance, security, testing, and common pitfalls. It helps developers build robust, scalable, and maintainable Next.js applications by adhering to community-accepted best practices and coding standards.

This rule provides a comprehensive guide to nginx configuration best practices, covering code organization, common patterns, performance, security, testing, pitfalls, and tooling.

Provides comprehensive guidance on best practices for coding standards, performance, security, and testing in NLTK projects. This rule helps developers write clean, maintainable, and efficient NLP code using NLTK.

Comprehensive best practices and coding standards for Python projects using the nose2 testing framework. Covers code organization, common patterns, performance, security, testing, and tooling.

This rule provides comprehensive best practices for developing applications using the notion-api library, covering code organization, performance, security, testing, and common pitfalls.

This rule provides comprehensive best practices and coding standards for the Numba library, covering code organization, performance optimization, security, testing, and common pitfalls. It aims to help developers write efficient, maintainable, and secure Numba code.

This rule provides best practices for using NumPy in Python, covering coding standards, performance optimization, security, and testing strategies to enhance code quality and maintainability.

This rule provides comprehensive best practices and coding standards for Nuxt.js projects, covering code organization, performance, security, testing, and common pitfalls. It aims to ensure maintainable, scalable, and secure Nuxt.js applications.

Comprehensive best practices and coding standards for projects using the openai library, covering code structure, performance, security, and common pitfalls.

This rule outlines best practices for developing with the opencv-python library, focusing on code organization, performance, security, testing, and common pitfalls. It provides comprehensive guidelines for efficient and maintainable opencv-python projects.

This rule outlines best practices for using the pandas library in Python, covering code style, performance, data handling, and testing. It aims to promote maintainable, efficient, and robust data analysis workflows.

Comprehensive best practices for using pdoc to generate and maintain Python project documentation. Covers code structure, performance, security, testing, and tooling to ensure high-quality documentation and maintainable projects.

Comprehensive guide for Peewee ORM best practices, covering code organization, performance, security, testing, and common pitfalls. Provides actionable guidance for developers to write maintainable and efficient database-driven applications using Peewee.

This rule outlines the best practices and coding standards for developing Elixir applications with the Phoenix framework, covering code organization, performance, security, testing, and common pitfalls.

This rule provides guidelines for PHP coding best practices, covering code structure, security, performance, and testing to improve code quality and maintainability.

This rule provides best practices for using the Pillow image processing library in Python, covering code organization, performance, security, testing, and common pitfalls. It aims to help developers write maintainable, efficient, and secure image processing applications.

This rule enforces best practices and coding standards for Playwright tests, including stable selectors, test isolation, user-centric testing, and performance considerations.

This rule file provides best practices and coding standards for using the Plotly library, focusing on code organization, performance, security, testing, and common pitfalls. It aims to guide developers in creating maintainable, efficient, and secure Plotly applications.