Project Rules

This rule provides comprehensive best practices for developing with Elasticsearch, covering code organization, performance, security, testing, and common pitfalls, ensuring efficient and maintainable Elasticsearch applications. These practices apply broadly across languages interacting with Elasticsearch.

Enforces best practices, coding standards, and performance considerations for Electron development. Covers code structure, security, testing, and common pitfalls to ensure robust and maintainable applications.

This rule outlines best practices for developing and maintaining applications within the ELK (Elasticsearch, Logstash, Kibana) stack, including coding standards, configuration, performance, security, and testing.

Comprehensive guide to Emacs Lisp coding standards, best practices, and common pitfalls. Focuses on maintainability, readability, and performance for building robust Emacs extensions.

This rule provides comprehensive best practices for using esbuild, focusing on performance, code organization, and security in build configurations and development workflows.

This rule provides comprehensive guidelines for ESLint, covering code organization, common patterns, performance, security, testing, and tooling, ensuring high-quality, maintainable JavaScript/TypeScript code.

This rule provides comprehensive best practices and coding standards for Expo projects, covering code organization, performance, security, testing, and common pitfalls to ensure maintainable and high-quality applications.

This rule provides comprehensive guidance on best practices for developing robust, maintainable, and performant Express.js applications, covering aspects from code organization and security to testing and deployment.

This rule provides comprehensive best practices for developing applications with Fabric.js, covering code organization, performance optimization, security considerations, and testing strategies. It aims to help developers create efficient, maintainable, and secure Fabric.js-based applications.

Comprehensive guidelines for developing robust, scalable, and maintainable FastAPI applications. Covers code structure, performance, security, testing, and common pitfalls.

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

This rule provides comprehensive best practices for developing robust, maintainable, and scalable applications using the Fiber web framework in Go. It covers code organization, performance, security, testing, and common pitfalls.

This rule provides guidelines for Firebase library usage, covering code organization, performance, security, testing, and common pitfalls. It aims to ensure efficient, secure, and maintainable Firebase projects.

Comprehensive guide for using Flake8 effectively in Python projects, covering code style, error prevention, security, testing, and optimization. It outlines best practices, patterns, and common pitfalls to help developers maintain high code quality and adherence to standards.

A comprehensive guide to best practices for developing RESTful APIs using Flask and Flask-RESTful, covering code organization, performance, security, and testing.

This rule provides comprehensive best practices for developing Flask applications, covering code structure, security, performance, and testing.

Comprehensive guidelines and best practices for Flutter development, covering code organization, performance optimization, security, testing, and tooling.

This rule file provides comprehensive guidelines for using Font Awesome effectively, covering setup, styling, accessibility, performance, and security best practices. It ensures consistent and optimized usage across projects.

Provides guidelines for using gcp-cli, including best practices for scripting, configuration management, security, and performance. Focuses on automation, predictable output, and secure authentication within Google Cloud environments.

This rule provides best practices for developing and managing infrastructure and applications on Google Cloud Platform (GCP), encompassing code organization, security, performance, and deployment strategies.

This rule provides coding standards and best practices for using the gensim library, focusing on NLP, topic modeling, performance, and code organization. It offers actionable guidelines for developers to create effective and maintainable gensim-based applications.

This rule outlines best practices for effective use of Git, including code organization, commit strategies, branching models, and collaborative workflows.

This rule provides comprehensive guidelines for GitHub Actions development, covering best practices, coding standards, performance, security, and testing. It aims to ensure efficient, reliable, secure, and maintainable workflows.

Enforces best practices for GitLab CI/CD configurations, promoting efficient, maintainable, and secure pipelines. This rule covers aspects from code organization to security and testing strategies.

This rule provides a comprehensive set of best practices for developing Go applications, covering code organization, performance, security, testing, and common pitfalls.

Comprehensive coding standards and best practices for Godot Engine development, covering code organization, performance, testing, and security to ensure maintainable, efficient, and secure game projects. These rules are primarily for GDScript but also reference relevant C# practices where applicable.

This rule provides guidelines for Google Maps JavaScript API development, covering code organization, performance, security, testing, and common pitfalls. It promotes best practices to ensure efficient, secure, and maintainable map applications.

Comprehensive rules for Gradle best practices, covering code organization, performance, security, testing, and more. Provides actionable guidance to improve Gradle project maintainability and efficiency.

Comprehensive guide for Grafana development best practices, covering code organization, performance, security, testing, and common pitfalls to ensure robust and maintainable Grafana solutions. Includes guidance for creating efficient dashboards, data sources, and plugins.

This rule provides comprehensive best practices and coding standards for GraphQL development, covering code organization, performance, security, testing, and common pitfalls.