Project Rules

This rule provides comprehensive guidelines for using the Guzzle HTTP client in PHP projects, covering code organization, common patterns, performance, security, testing, and tooling.

This rule outlines best practices for Hardhat development, covering code organization, security, testing, and performance. It aims to provide a comprehensive guide for developers working with the Hardhat Ethereum development environment.

Comprehensive best practices and coding standards for developing, deploying, and maintaining applications on the Heroku platform. This rule emphasizes the Twelve-Factor App methodology and provides detailed guidance for optimizing application architecture, performance, security, and maintainability on Heroku.

This rule provides comprehensive best practices for htmx development, covering code organization, security, performance, testing, and common pitfalls. It aims to guide developers in building robust, maintainable, and secure htmx applications.

This rule provides comprehensive best practices for using the httpx library, covering code organization, performance, security, testing, and common pitfalls. Adhering to these guidelines will improve code quality, maintainability, and security when working with httpx.

This rule provides guidelines for best practices when working with the Hugging Face Transformers library, covering code organization, performance, testing, security, and common pitfalls. It emphasizes community standards and maintainability.

Comprehensive guide covering best practices for the Hypothesis Python library, including coding standards, testing, performance, and security. Provides actionable guidance for developers to write maintainable, robust, and efficient property-based tests.

This rule file provides best practices for using the Insomnia API Client, including project organization, environment management, testing, and collaboration to improve API development workflows.

This rule provides comprehensive best practices for Ionic Framework development, covering code organization, performance, security, testing, and more. Following these guidelines will result in more maintainable, performant, and secure Ionic applications.

This rule provides comprehensive guidelines for using isort in Python projects, covering code organization, common patterns, performance, security, testing, tooling, and common pitfalls. It aims to standardize import sorting and improve code quality.

Enforces best practices for Java development, covering code style, performance, security, and testing. Provides guidelines for writing clean, maintainable, and efficient Java code.

This rule provides best practices and coding standards for the JAX library, emphasizing functional programming, JIT compilation, automatic differentiation, and immutable data structures. It also covers performance considerations, common pitfalls, and tooling recommendations.

Comprehensive best practices for Jenkins, covering code organization, security, performance, testing, and common pitfalls. Provides guidelines for writing robust, maintainable, and secure Jenkins pipelines and configurations.

This rule provides guidelines for writing clean, maintainable, and effective tests using Jest. It covers code organization, performance, common pitfalls, and best practices for testing JavaScript and TypeScript projects.

Enforces Jetpack Compose best practices for code organization, performance, and maintainability. This rule provides guidelines for writing efficient and idiomatic Compose code.

This rule file provides guidelines for jQuery development, covering code organization, performance, security, and testing. It helps developers write maintainable, efficient, and secure jQuery code.

Comprehensive guidelines and best practices for writing effective, maintainable, and performant JUnit tests in Java projects. This rule file covers code organization, patterns, performance, security, testing strategies, common pitfalls, and tooling.

This rule enforces Keras library best practices, focusing on code clarity, modularity, performance optimization, and security considerations. It provides actionable guidance for developers to improve the quality and maintainability of Keras-based machine learning projects.

This rule file outlines best practices for Kivy UI development, including code organization, performance, security, and testing. Adhering to these guidelines ensures maintainable, efficient, and secure Kivy applications.

This rule provides comprehensive best practices for developing and maintaining Kubernetes applications and infrastructure, covering coding standards, security, performance, testing, and deployment.

Comprehensive best practices and coding standards for developing applications using LangChain.js. Focuses on code organization, performance, security, testing, and common pitfalls to ensure robust and maintainable AI-driven solutions.

This rule provides best practices for developing LangChain applications, covering code organization, performance, security, testing, and common pitfalls. It aims to improve code quality, maintainability, and overall project success.

This rule file provides comprehensive best practices for developing with LangGraph, covering code organization, performance, security, testing, and common pitfalls. It offers actionable guidance for developers to build robust and maintainable LangGraph applications.

This rule outlines comprehensive best practices for Laravel development, covering coding standards, security, performance, and testing to ensure maintainable, efficient, and secure applications. It provides guidelines for code organization, common patterns, performance considerations, security best practices, testing approaches, common pitfalls, and tooling.

This rule file provides comprehensive best practices for LightGBM, covering code organization, performance, security, testing, and common pitfalls to avoid. Adhering to these guidelines will improve the efficiency, reliability, and maintainability of your LightGBM projects.

This rule outlines best practices and coding standards for developing with LlamaIndex, covering code organization, performance, security, testing, and common pitfalls. It aims to ensure maintainable, efficient, and secure LlamaIndex applications.

This rule provides comprehensive guidelines for developing AI applications with LlamaIndex-JS, covering code organization, performance, security, and testing best practices. It aims to ensure robust, efficient, and secure LLM-powered applications.

This rule enforces LLVM's coding standards and promotes best practices for writing efficient, maintainable, and robust code within the LLVM ecosystem. It covers style, language usage, optimization strategies, and more.

Comprehensive guide to best practices when developing with Material-UI/MUI, covering code organization, performance, security, testing, and common pitfalls. It focuses on creating maintainable, scalable, and performant React applications using MUI components.

This rule provides guidelines and best practices for developing robust, maintainable, and performant data visualizations using Matplotlib in Python. It covers aspects from code organization to testing and security considerations.