Does this skill support existing GitHub Actions?

Yes, it includes specific templates to update your .github/workflows to use official Astral setup-uv actions for testing, building, and deployment.

What is the main benefit of migrating to uv?

uv provides significantly faster package installations, deterministic builds via uv.lock, and a unified tool for environment and package management.

Can I use this for non-Keboola packages?

While optimized for Keboola's python-component patterns, the logic for pyproject.toml conversion and uv integration is applicable to most Python projects.

How does it handle package versioning during migration?

The skill implements a strategy using git tags where the pyproject.toml version is set to a placeholder, and the actual version is injected during the CI process.

Does it replace pip entirely?

Yes, the migration follows a 'zero pip' philosophy, replacing all legacy pip commands with modern uv equivalents for better performance and reliability.

Python uv Build Migrator

Name: Python uv Build Migrator
Author: keboola

bykeboola

•

Deployment & DevOps

Migrates Python packages from legacy setup.py to the modern uv build system with deterministic dependency management.

This skill automates the transition of Python packages—specifically Keboola components—from legacy setup.py and requirements.txt configurations to the modern pyproject.toml format using the uv build tool. It provides a structured, multi-phase migration path that includes linting cleanup, metadata conversion, and the implementation of robust CI/CD workflows using GitHub Actions. By leveraging deterministic dependency management through uv.lock, it ensures faster, more reliable builds while following PEP 517/518 standards and established industry best practices.

Key Features

01Automated conversion of setup.py to pyproject.toml metadata

02Deterministic dependency locking with uv.lock generation

03Standardized GitHub Actions workflow updates for CI/CD modernization

047 GitHub stars

05Integrated linting baseline enforcement using flake8 and ruff

06Automated Test PyPI and production release pipeline configuration

Use Cases

01Transitioning projects to standardized PEP 517/518/639 compliant package structures

02Modernizing legacy Python components to use the high-performance uv build system

03Replacing pip-based CI/CD workflows with faster, more reliable uv-powered actions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add keboola/ai-kit migrate-component-to-uv

For use in Claude.ai and ChatGPT

Download Skill