migrate-cookiecutter

Convert cookiecutter data science repositories to the CEDA package standard.

Updated Nov 8, 2024

Search Tags:#python #refactor #migrate #data-science #project-structure #cookiecutter #package-standards

Ready to run

npx skills add https://github.com/cedanl/.github --skill migrate-cookiecutter

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: migrate-cookiecutter
Source: https://github.com/cedanl/.github/tree/main/.claude/skills/migrate-cookiecutter
Command: npx skills add https://github.com/cedanl/.github --skill migrate-cookiecutter

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Migrate Python cookiecutter data science repos to the CEDA package standard, aligning structure, tooling, and metadata for consistent packaging and long-term maintainability.

Core Features & Use Cases

Analyze current repo layout and identify sources of truth (src/, module/, data/, pyproject.toml).
Create a migration plan that consolidates settings in a package metadata directory and updates imports accordingly.
Move source code to src/project_name, adjust data paths, and standardize configuration to support automated deployments.

Quick Start

Invoke the migration command on a repository to start the guided cookiecutter migration to the CEDA package standard.

Frequently Asked Questions about migrate-cookiecutter

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

FAQPage Schema

How do I migrate a cookiecutter Python project to the CEDA package standard?▼

To migrate a cookiecutter Python project, this Skill analyzes your repository layout, identifies sources of truth like pyproject.toml, and moves source code to a unified src/project_name structure. It updates imports and standardizes tooling configuration for automated deployment.

What is the CEDA package standard for data science repositories?▼

The CEDA package standard is a unified project structure that enforces a single source of truth in package metadata. It organizes Python code into a src/project_name layout, standardizes tooling configuration, and aligns data paths for long-term maintainability.

How do I update imports when moving source code to a src layout?▼

Updating imports during a src layout migration is handled by analyzing current repository paths and adjusting references. This Skill consolidates settings in a package metadata directory and automatically updates imports to match the new src/project_name structure.

Can I use this migration tool with any Python cookiecutter data science repo?▼

You can apply this migration tool to Python projects organized with cookiecutter layouts. It is designed for data science repositories and requires a recognizable structure with existing sources of truth like src/, module/, or pyproject.toml to succeed.

What's the best way to standardize tooling configuration in a migrated Python package?▼

The best way to standardize tooling configuration is through a guided migration that consolidates settings into a package metadata directory. This Skill imposes a single source of truth, ensuring configurations support automated deployments consistently.

Why does my cookiecutter project structure fail automated deployments?▼

Cookiecutter project structures often fail automated deployments due to scattered settings and inconsistent metadata. This Skill resolves the issue by consolidating configurations and moving code to a standardized src/project_name layout with a single source of truth.