migrate-cookiecutter

Convert cookiecutter data science repositories to the CEDA package standard.

Updated Nov 8, 2024
One-click install
npx skills add https://github.com/cedanl/.github --skill migrate-cookiecutter
Or copy as Structured Prompt for Agent
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.