coding-standards
π―Skillfrom affaan-m/everything-claude-code
|Validates and enforces consistent code quality, style guidelines, and best practices across programming languages and project structures.
Overview
Coding Standards is a skill from affaan-m/everything-claude-code that validates and enforces consistent code quality, style guidelines, and best practices across programming languages and project structures. It ensures AI-generated code follows established team conventions.
Key Features
- Cross-language code style enforcement
- Naming convention validation and correction
- Project structure and organization standards
- Code review checklist automation
Who is this for?
This skill is for development teams who want their AI coding assistant to follow specific coding standards consistently. It is useful for organizations with established style guides who need AI-generated code to conform to their conventions.
Same repository
affaan-m/everything-claude-code(43 items)
Installation
npx skills add https://github.com/affaan-m/everything-claude-code --skill coding-standardsNeed more details? View full documentation on GitHub β
More from this repository10
Battle-tested Claude Code configurations from an Anthropic hackathon winner
Implements robust backend design patterns like repository, factory, singleton, and dependency injection for scalable and maintainable server-side architectures.
Validates and secures code by providing comprehensive security checks for authentication, input handling, secrets management, and sensitive feature implementation.
Provides reusable React component patterns like composition, compound components, and render props to enhance code modularity and flexibility.
Provides reusable design patterns and idiomatic Go solutions for efficient, scalable, and maintainable software architecture.
Enforces test-driven development by guiding developers to write comprehensive tests first, ensuring 80%+ code coverage across unit, integration, and E2E testing.
Provides reusable SQL query patterns, database design strategies, and performance optimization techniques for PostgreSQL development
Dynamically updates and refines AI model knowledge through iterative feedback, adaptive learning techniques, and intelligent knowledge integration.
Streamlines Go testing with comprehensive unit, integration, and benchmark strategies, mocking frameworks, and test coverage analysis.
Dynamically adapts and improves AI performance through iterative feedback, knowledge expansion, and self-optimization techniques.