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/python-packaging

Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.

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$ npx -y skills add wshobson/agents --skill python-packaging --agent claude-code

SKILL.md

python-packaging.SKILL.md
name: python-packaging
description: Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.

Python Packaging

Comprehensive guide to creating, structuring, and distributing Python packages using modern packaging tools, pyproject.toml, and publishing to PyPI.

When to Use This Skill

  • Creating Python libraries for distribution
  • Building command-line tools with entry points
  • Publishing packages to PyPI or private repositories
  • Setting up Python project structure
  • Creating installable packages with dependencies
  • Building wheels and source distributions
  • Versioning and releasing Python packages
  • Creating namespace packages
  • Implementing package metadata and classifiers

Core Concepts

1. Package Structure

  • **Source layout**: `src/package_name/` (recommended)
  • **Flat layout**: `package_name/` (simpler but less flexible)
  • **Package metadata**: pyproject.toml, setup.py, or setup.cfg
  • **Distribution formats**: wheel (.whl) and source distribution (.tar.gz)

2. Modern Packaging Standards

  • **PEP 517/518**: Build system requirements
  • **PEP 621**: Metadata in pyproject.toml
  • **PEP 660**: Editable installs
  • **pyproject.toml**: Single source of configuration

3. Build Backends

  • **setuptools**: Traditional, widely used
  • **hatchling**: Modern, opinionated
  • **flit**: Lightweight, for pure Python
  • **poetry**: Dependency management + packaging

4. Distribution

  • **PyPI**: Python Package Index (public)
  • **TestPyPI**: Testing before production
  • **Private repositories**: JFrog, AWS CodeArtifact, etc.

Quick Start

Minimal Package Structure

my-package/
├── pyproject.toml
├── README.md
├── LICENSE
├── src/
│   └── my_package/
│       ├── __init__.py
│       └── module.py
└── tests/
    └── test_module.py

Minimal pyproject.toml

[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"

[project]
name = "my-package"
version = "0.1.0"
description = "A short description"
authors = [{name = "Your Name", email = "you@example.com"}]
readme = "README.md"
requires-python = ">=3.8"
dependencies = [
    "requests>=2.28.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.0",
    "black>=22.0",
]

Package Structure Patterns

Pattern 1: Source Layout (Recommended)

my-package/
├── pyproject.toml
├── README.md
├── LICENSE
├── .gitignore
├── src/
│   └── my_package/
│       ├── __init__.py
│       ├── core.py
│       ├── utils.py
│       └── py.typed          # For type hints
├── tests/
│   ├── __init__.py
│   ├── test_core.py
│   └── test_utils.py
└── docs/
    └── index.md

**Advantages:**

  • Prevents accidentally importing from source
  • Cleaner test imports
  • Better isolation

**pyproject.toml for source layout:**

[tool.setuptools.packages.find]
where = ["src"]

Pattern 2: Flat Layout

my-package/
├── pyproject.toml
├── README.md
├── my_package/
│   ├── __init__.py
│   └── module.py
└── tests/
    └── test_module.py

**Simpler but:**

  • Can import package without installing
  • Less professional for libraries

Pattern 3: Multi-Package Project

project/
├── pyproject.toml
├── packages/
│   ├── package-a/
│   │   └── src/
│   │       └── package_a/
│   └── package-b/
│       └── src/
│           └── package_b/
└── tests/

Detailed patterns and worked examples

Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.

Read more
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Questions about python-packaging.

Which coding agents can use python-packaging?
python-packaging installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.