code-packaging-pro

Automate Python packaging and Docker image release workflows.

1|Updated Jul 3, 2026

Search Tags:#python #docker #github-actions #package #ci #pyproject #build-artifacts

Ready to run

npx skills add https://github.com/truongnat/skills --skill code-packaging-pro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: code-packaging-pro
Source: https://github.com/truongnat/skills/tree/main/skills/code-packaging-pro
Command: npx skills add https://github.com/truongnat/skills --skill code-packaging-pro

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides guidance for packaging Python projects, creating efficient Docker images, and structuring CI pipelines to build and publish artifacts (wheels, containers) while aligning with deployment strategies.

Core Features & Use Cases

Recommends modern Python packaging patterns (pyproject.toml, setuptools-style metadata) and artifact hygiene.
Guides multi-stage Dockerfile construction for slim runtimes and non-root containers.
Designs GitHub Actions workflows for lint, test, build, and publishing across PyPI and container registries.
Clarifies the boundary between code-packaging and deployment-pro responsibilities (build vs release/promotion).

Quick Start

Configure a project with a pyproject.toml, a Dockerfile, and GitHub Actions workflows to start building wheels and container images.

Frequently Asked Questions about code-packaging-pro

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

FAQPage Schema

How do I set up GitHub Actions to build and publish Python packages?▼

Set up GitHub Actions to build and publish Python packages by designing CI workflows for lint, test, build, and publishing across PyPI and container registries. This aligns with deployment strategies and applies trusted publishing patterns for reproducible builds.

What is the best way to structure a multi-stage Dockerfile for a Python service?▼

The best way to structure a multi-stage Dockerfile for a Python service is by guiding multi-stage Dockerfile construction for slim runtimes and non-root containers. This ensures efficient Docker images while maintaining reproducible builds.

Do I need pyproject.toml to package Python code for CI pipelines?▼

Yes, you need pyproject.toml to package Python code for CI pipelines. Modern Python packaging patterns recommend using pyproject.toml with setuptools-style metadata to enforce packaging metadata and ensure artifact hygiene during builds.

What is the difference between packaging code and deploying a release?▼

The difference between packaging code and deploying a release is the boundary between build and promotion. Code-packaging handles building artifacts like wheels and containers, while deployment-pro handles release and promotion to production environments.

Can I use trusted publishing patterns for both PyPI and container registries?▼

Yes, you can use trusted publishing patterns for both PyPI and container registries. CI pipelines can be designed to build and publish artifacts across both platforms, ensuring secure and reproducible release workflows for libraries and services.