project-standards

To standardize AI project directory structure, apply consistent folder layouts for source, tests, docs, and scripts, alongside clear rules for files, variables, and modules to ensure readability and maintainability.

The best way to scaffold a new AI project for onboarding is using a ready-to-use template that enforces directory layout, coding styles, and documentation practices, reducing ambiguity and wasted effort for new contributors.

Enforcing coding style and git workflow improve maintainability by standardizing commit messages and module naming, which ensures consistent collaboration, reduces ambiguity, and harmonizes workflow practices across teams.

Yes, you can audit an existing codebase for documentation and versioning by checking its adherence to standardized directory layouts, naming conventions, and documentation templates designed to track changes consistently.

No specific dependencies are required to enforce project structure and coding standards; the skill operates independently to provide directory layout guidance, naming rules, and documentation templates for software, data science, and research teams.

You should avoid standardized scaffolding if your AI project operates entirely outside software development, data science, or research contexts, as the provided directory layouts and conventions target structured team workflows.