Project Rules

This rule provides comprehensive best practices and coding standards for ROS (Robot Operating System) development, covering code organization, common patterns, performance, security, testing, and tooling. It aims to enhance code quality, maintainability, and interoperability in ROS projects.

Comprehensive best practices for Ruby development, covering code organization, common patterns, performance, security, testing, and tooling. This guide offers actionable advice to improve Ruby code quality, maintainability, and efficiency.

This rule provides comprehensive best practices for Rust development, covering code organization, common patterns, performance, security, testing, pitfalls, and tooling. It aims to guide developers in writing idiomatic, efficient, secure, and maintainable Rust code.

This rule file outlines best practices and coding standards for developing Sanic applications, covering code organization, performance, security, testing, and common pitfalls.

This rule provides guidelines for best practices and coding standards when using the scikit-image library for image processing in Python. It covers code organization, performance, security, testing, and common pitfalls.

Enforces best practices and coding standards for scikit-learn projects, promoting maintainability, performance, and security. This rule provides guidelines on code organization, common patterns, performance optimization, testing, and common pitfalls.

This rule outlines coding standards, best practices, and common pitfalls for developing scientific computing applications using the SciPy library. It emphasizes clarity, maintainability, performance, and security for efficient SciPy development.

This rule provides comprehensive best practices for Scrapy development, including code organization, performance, security, testing, and common pitfalls to avoid. It aims to guide developers in building robust, efficient, and maintainable web scraping applications with Scrapy.

This rule provides best practices for coding standards in Seaborn, emphasizing clear, reproducible code, optimal performance, and secure data handling within AI and machine learning data science development.

This rule provides best practices and coding standards for using the Selenium library in Python. It covers code organization, performance, security, testing, common pitfalls, and tooling to ensure maintainable and efficient Selenium projects.

This rule provides comprehensive best practices for integrating and utilizing Sentry in your projects. It covers code organization, performance, security, testing, and common pitfalls when using Sentry for error tracking and performance monitoring.

This rule enforces best practices for using the `net/http` ServeMux in Go, promoting clean, maintainable, and efficient code. It covers routing, handler design, and error handling specifics to help developers leverage ServeMux effectively.

This rule provides guidance on best practices for using setuptools in Python projects, covering code organization, performance, security, testing, and common pitfalls.

This rule provides comprehensive best practices for developing with Shadcn UI, covering code organization, performance, security, and testing.

This rule provides comprehensive best practices for developing with the smolagents library, covering code organization, performance, security, testing, and common pitfalls. It aims to guide developers in building robust, maintainable, and efficient AI agent applications.

This rule provides guidelines and best practices for developing robust, scalable, and secure real-time applications using Socket.IO. It covers code organization, performance optimization, security considerations, testing strategies, and common pitfalls to avoid when working with Socket.IO.

This rule provides best practices and coding standards for Solidity smart contract development, covering code organization, security, performance, and testing.

This comprehensive guide outlines best practices for SolidJS development, covering code organization, performance, security, testing, and common pitfalls. It provides actionable guidelines for building maintainable, efficient, and secure SolidJS applications.

This rule file provides comprehensive best practices and coding standards for developing projects using spaCy, covering code organization, performance, security, testing, and more. It aims to guide developers in building maintainable, efficient, and secure NLP applications with spaCy.

This rule file provides comprehensive guidelines for writing high-quality Sphinx documentation, covering code style, structure, performance, and best practices. It aims to ensure consistency, readability, and maintainability of Sphinx-based projects.

This rule set provides comprehensive best practices and coding standards for developing robust and maintainable Java backend applications using the Spring Boot framework. It focuses on code structure, performance, security, and testing.

This rule provides comprehensive best practices and coding standards for developing robust, maintainable, and performant Spring Boot applications, covering code structure, performance, security, testing, and common pitfalls.

Enforces best practices for SQLAlchemy, covering code organization, performance, security, testing, and common pitfalls to ensure maintainable, efficient, and secure database interactions.

This rule provides comprehensive guidance for SQLite development, covering best practices for schema design, performance optimization, security, testing, and more. It aims to ensure efficient, secure, and maintainable SQLite database applications.

A comprehensive guide to best practices for using the statsmodels library in Python, covering code organization, performance, testing, and common pitfalls. These guidelines promote maintainable, reliable, and efficient statsmodels code.

This rule provides guidelines and best practices for developing maintainable, performant, and secure Streamlit applications. It covers code organization, performance optimization, security considerations, testing strategies, and common pitfalls to avoid.

This rule file outlines best practices for integrating Stripe's payment processing services into web and mobile applications, focusing on security, performance, and maintainability. It provides guidelines on coding standards, error handling, and testing to ensure a robust and reliable Stripe integration.

Comprehensive best practices for developing with Supabase, covering code organization, performance, security, testing, and common pitfalls. This rule provides actionable guidance for developers to build robust and scalable applications using Supabase.

Comprehensive Svelte best practices covering code structure, performance, security, testing, and common pitfalls. This rule provides guidance on writing maintainable, efficient, and secure Svelte applications.

This rule provides comprehensive best practices and coding standards for SvelteKit development, covering code structure, performance, security, testing, and common pitfalls. It aims to improve code quality, maintainability, and overall project health.