repo-best-practices

Generate a standardized project directory structure and configuration files for data lakehouse projects.

Updated Mar 10, 2026

Search Tags:#best practices #scaffolding #project setup #repository structure #databricks #interview preparation

Ready to run

npx skills add https://github.com/slysik/databricks-claude-coding --skill repo-best-practices

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: repo-best-practices
Source: https://github.com/slysik/databricks-claude-coding/tree/main/.pi/skills/repo-best-practices
Command: npx skills add https://github.com/slysik/databricks-claude-coding --skill repo-best-practices

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill establishes a production-quality directory structure and essential configuration files for a new project before any code is written, streamlining the initial setup for interviews and new projects.

Core Features & Use Cases

Project Scaffolding: Creates a standardized, clean repository layout including README.md, databricks.yml, src/, docs/, and tests/.
Interview Preparation: Ensures a strong foundation for code generation and deployment, demonstrating best practices from the outset.
Domain Adaptability: Supports various domains like retail, media, IoT, SaaS, and FinServ with a consistent structure.

Quick Start

Use the repo-best-practices skill to create a new project scaffold for the retail domain.

Frequently Asked Questions about repo-best-practices

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I scaffold a clean repository structure for a Databricks data lakehouse project?▼

You can scaffold a clean repository structure by generating a standardized directory layout with README.md, databricks.yml, src/, docs/, and tests/ configured for data lakehouse projects. This creates a production-ready project foundation before any code is written.

What's the best way to structure a data lakehouse repository for interview preparation?▼

Structuring a data lakehouse repository for interview preparation involves generating standardized configuration files and organized source, docs, and tests directories. This demonstrates production-quality best practices from the outset, providing a strong foundation for code generation and deployment.

Does this project scaffolding approach support domains outside of retail?▼

Yes, this project scaffolding supports domain adaptability across various industries including retail, media, IoT, SaaS, and FinServ. It adapts project names and core entities while maintaining a consistent, standardized directory structure across different domains.

Can I use the generated databricks.yml configuration for immediate project deployment?▼

The generated databricks.yml configuration facilitates rapid project initialization for interviews and new development by providing essential deployment setup. It establishes a production-quality foundation that streamlines the initial setup before writing actual application code.

What essential files are included when scaffolding a new project repository?▼

Scaffolding a new project repository includes generating essential files like README.md, databricks.yml, and organized directories for src/, docs/, and tests/. This standardized layout ensures a clean, production-ready foundation for immediate development.

Why set up a project scaffold before writing any data lakehouse code?▼

Setting up a project scaffold before writing data lakehouse code establishes a production-quality directory structure and essential configuration files. This streamlines initial setup for interviews and new projects, ensuring best practices are demonstrated from the outset.