Project Rules

Enforces consistent code formatting using Black, the uncompromising Python code formatter, promoting readability and reducing diffs. Covers best practices related to Black's configuration, usage, and integrations.

This rule file outlines best practices for using the boto3 library, including code organization, security, performance, testing, and common pitfalls. It aims to ensure efficient, secure, and maintainable AWS integrations with boto3.

Comprehensive coding standards, best practices, and architectural guidelines for Python backend development with the Bottle microframework, designed to improve code quality, maintainability, and security.

This rule provides comprehensive guidance on Bun library coding standards, best practices, and common patterns. It includes performance optimization, security considerations, and testing strategies.

This rule file provides a comprehensive guide to C# best practices, coding standards, and common patterns for writing maintainable, performant, and secure code.

This rule enforces best practices and coding standards for Chakra UI projects, including accessibility, styling, performance, and security. It aims to provide clear, actionable guidance for developers using Chakra UI.

This rule provides best practices for using Cheerio for web scraping and HTML parsing in JavaScript, covering code organization, performance, security, testing, and common pitfalls.

This rule provides comprehensive best practices for configuring and optimizing CircleCI workflows, covering code organization, security, performance, and testing to ensure efficient and reliable CI/CD pipelines.

This rule file outlines comprehensive best practices for developing applications using the Clerk library, focusing on security, performance, code organization, and testing to ensure robust and maintainable authentication implementations.

Comprehensive best practices for developing robust and maintainable command-line interfaces using the Click library in Python. Covers code structure, patterns, performance, security, testing, and common pitfalls.

This rule provides a comprehensive set of best practices and coding standards for developing with the Cloudflare library, specifically focusing on Terraform configurations. It aims to guide developers in creating efficient, secure, and maintainable infrastructure code.

This rule provides guidelines for using CodeMirror effectively, covering code organization, performance, security, testing, and common pitfalls. It aims to ensure robust and maintainable code editor implementations.

This rule provides comprehensive best practices for developing with the CrewAI library, covering code organization, performance, security, testing, and common pitfalls. It serves as a guide for building robust and scalable AI applications using CrewAI.

This rule provides best practices for CSS development, covering code organization, performance, security, testing, and common pitfalls. It aims to ensure maintainable, scalable, and efficient CSS code.

Enforces CUDA coding standards, performance optimizations, and best practices to ensure efficient and maintainable GPU-accelerated code. This rule provides guidance on code organization, memory management, error handling, and more.

This rule provides best practices for developing efficient, maintainable, and scalable GUI applications with CustomTkinter. It covers code organization, performance, security, testing, and common pitfalls.

This rule provides a comprehensive guide to Cypress best practices, covering code organization, performance, security, testing strategies, and tooling to ensure robust and maintainable end-to-end tests.

Comprehensive best practices and coding standards for D3.js projects, covering code organization, performance, security, testing, and common pitfalls.

Comprehensive best practices and coding standards for using Dask in Python, focusing on performance, code organization, and common pitfalls. Provides actionable guidance for developers using Dask for parallel and distributed computing.

This rule outlines best practices for coding standards, observability, and effective use of the Datadog library in Python projects. It covers coding style, metric/tag naming, dashboard design, security, and performance optimization.

This rule file provides comprehensive guidelines for Deno development, covering best practices for code organization, security, performance, testing, and documentation. Adhering to these standards ensures maintainable, efficient, and secure Deno applications.

This rule file provides guidelines for writing stable and maintainable end-to-end tests using Detox, covering code structure, testing strategies, and performance considerations. It includes best practices for test ID usage, dealing with flakiness, and integrating with CI/CD pipelines.

This rule provides comprehensive guidelines for DigitalOcean infrastructure and application development, covering code organization, security, performance, and deployment best practices. It aims to ensure consistent, scalable, and secure cloud solutions on the DigitalOcean platform.

This rule provides best practices and coding standards for developing applications with the discord-api library. It covers code organization, performance, security, testing, and common pitfalls to ensure robust and maintainable Discord integrations.

This rule file provides comprehensive best practices for Django's Object-Relational Mapper (ORM), covering code organization, performance, security, testing, and common pitfalls. It aims to guide developers in building efficient, maintainable, and secure Django applications.

A comprehensive guide to best practices for developing REST APIs using Django REST Framework (DRF), covering code structure, design patterns, security, performance, and testing.

Comprehensive guide to Django best practices covering code organization, performance, security, testing, and common pitfalls. This rule ensures adherence to community standards for maintainable and efficient Django applications.

This rule file provides comprehensive guidance on Docker best practices, covering Dockerfile construction, image optimization, and security considerations. It aims to improve the efficiency, maintainability, and security of Docker-based projects.

This rule outlines best practices for using Drizzle ORM in TypeScript and JavaScript projects. It covers code organization, performance, security, testing, and common pitfalls.

This rule file outlines best practices for developing with DuckDB, covering code organization, performance optimization, security considerations, and testing strategies. It aims to improve code quality, maintainability, and overall project health when using DuckDB.