Create distributable Python packages with pyproject.toml and CI publishing workflows.
npx skills add https://github.com/jacexh/skills --skill python-packaging-jacexh
Please help me install this Agent Skill. Skill: python-packaging Source: https://github.com/jacexh/skills/tree/main/skills/python-packaging Command: npx skills add https://github.com/jacexh/skills --skill python-packaging-jacexh
Packaging and publishing Python projects involves many repetitive, error-prone steps—project layout, metadata, build backends, distribution formats, and CI publishing—making releases slow and fragile. This Skill consolidates best practices and concrete patterns so maintainers can produce reproducible builds, correct metadata, and reliable uploads to TestPyPI/PyPI or private indexes.
Generate a complete pyproject.toml, recommended src/ layout, and a GitHub Actions workflow that builds wheels and uploads to TestPyPI for my library named "my-package".
High-intent search queries and answers about installing and using this skill.
To create a distributable Python package, use pyproject.toml to define project metadata following PEP 517/518/621 standards, configure a build backend, and structure your code using the recommended src/ layout for reproducible builds.
The best way to build Python wheels and source distributions (sdist) is by configuring a PEP 517 compliant build backend in pyproject.toml, which automates artifact generation and ensures consistent, reproducible package builds across different environments.
Automate Python package publishing by creating a GitHub Actions workflow that builds wheels and sdist, runs twine checks, validates uploads to TestPyPI, and then publishes releases to PyPI or private indexes automatically on release tags.
Yes, modern Python packaging supports multi-package monorepos and namespace package patterns by configuring pyproject.toml correctly, allowing maintainers to manage multiple distributable libraries within a single repository structure.
Handle C extensions and multi-architecture wheel builds by applying advanced packaging patterns that compile platform-specific binaries and include package data, ensuring your source distributions and wheels are compatible across target architectures.
Use the src layout for Python packaging to prevent import errors during testing and ensure the installed package is evaluated rather than the local working directory, providing more reliable builds and editable installs for development workflows.
ollieb89Package Python libraries into wheels and sdists for PyPI publishing.
queeliusAutomate publishing Python packages to PyPI with metadata audits and tests.
trotsky1997Automates the PyPI publishing workflow for Python packages.
haxlysPackage Python projects for PyPI or private registries using modern standards.
