python-packaging-patterns
Structure Python projects for distribution with pyproject.toml, src layouts, dependency management, and publishing workflows. Covers packaging tools (hatch, setuptools, flit, poetry), versioning strategies, and editable installs. Triggers on Python project setup, packaging configuration, or dependency management requests.
Best use case
python-packaging-patterns is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structure Python projects for distribution with pyproject.toml, src layouts, dependency management, and publishing workflows. Covers packaging tools (hatch, setuptools, flit, poetry), versioning strategies, and editable installs. Triggers on Python project setup, packaging configuration, or dependency management requests.
Teams using python-packaging-patterns should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/python-packaging-patterns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How python-packaging-patterns Compares
| Feature / Agent | python-packaging-patterns | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structure Python projects for distribution with pyproject.toml, src layouts, dependency management, and publishing workflows. Covers packaging tools (hatch, setuptools, flit, poetry), versioning strategies, and editable installs. Triggers on Python project setup, packaging configuration, or dependency management requests.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Python Packaging Patterns
Structure Python projects for reliable distribution and dependency management.
## Project Layout
### The `src` Layout (Recommended)
```
my-project/
├── pyproject.toml
├── src/
│ └── my_package/
│ ├── __init__.py
│ ├── core.py
│ └── cli.py
├── tests/
│ ├── conftest.py
│ └── test_core.py
└── README.md
```
**Why src layout:** Prevents accidental imports from the working directory. Forces installation before testing, catching packaging errors early.
### Flat Layout (Simple Projects)
```
my-project/
├── pyproject.toml
├── my_package/
│ ├── __init__.py
│ └── core.py
└── tests/
```
Acceptable for internal tools and single-organ repos where distribution is not a concern.
## pyproject.toml Configuration
### Minimal Configuration
```toml
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "my-package"
version = "0.1.0"
description = "A concise description"
requires-python = ">=3.11"
license = "MIT"
dependencies = [
"httpx>=0.27",
]
[project.optional-dependencies]
dev = [
"pytest>=8.0",
"ruff>=0.5",
"mypy>=1.10",
]
```
### Build Backend Selection
| Backend | When to Use |
|---------|-------------|
| **hatchling** | Default choice. Fast, minimal config, good monorepo support |
| **setuptools** | Legacy projects, C extensions, complex build needs |
| **flit** | Pure Python, minimal config, simple projects |
| **poetry-core** | When using Poetry for dependency management |
### Optional Dependency Groups
Organize optional dependencies by use case:
```toml
[project.optional-dependencies]
dev = ["pytest>=8.0", "ruff>=0.5", "mypy>=1.10"]
docs = ["sphinx>=7.0", "myst-parser"]
dashboard = ["fastapi>=0.110", "uvicorn"]
metrics = ["prometheus-client"]
```
Install specific groups: `pip install -e ".[dev,dashboard]"`
## Entry Points
### Console Scripts
```toml
[project.scripts]
my-cli = "my_package.cli:main"
```
### Plugin Entry Points
```toml
[project.entry-points."my_app.plugins"]
csv = "my_package.plugins.csv:CsvPlugin"
json = "my_package.plugins.json:JsonPlugin"
```
## Version Management
### Single Source of Truth
```toml
# In pyproject.toml
[project]
dynamic = ["version"]
[tool.hatch.version]
path = "src/my_package/__init__.py"
```
```python
# In __init__.py
__version__ = "0.1.0"
```
### CalVer for System Packages
For infrastructure packages where semantic versioning adds little value:
```python
__version__ = "2026.03.1" # YYYY.MM.patch
```
## Dependency Pinning Strategy
| Context | Strategy | Tool |
|---------|----------|------|
| Library | Loose bounds (`>=1.0,<2.0`) | pyproject.toml |
| Application | Exact pins | pip-compile / uv lock |
| CI | Lockfile | uv.lock / requirements.txt |
### Generating Lockfiles
```bash
# Using uv (recommended)
uv pip compile pyproject.toml -o requirements.txt
uv pip compile pyproject.toml --extra dev -o requirements-dev.txt
# Using pip-tools
pip-compile pyproject.toml -o requirements.txt
```
## Editable Installs
```bash
# Standard editable install
pip install -e .
# With dev dependencies
pip install -e ".[dev]"
# In a fresh venv
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
```
## Tool Configuration in pyproject.toml
### Ruff
```toml
[tool.ruff]
line-length = 100
target-version = "py311"
[tool.ruff.lint]
select = ["E", "F", "I", "N", "W", "UP"]
```
### Pytest
```toml
[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"
pythonpath = ["."]
```
### Mypy
```toml
[tool.mypy]
strict = true
python_version = "3.11"
```
## Publishing
### To PyPI
```bash
# Build
python -m build
# Upload (use trusted publishing when possible)
python -m twine upload dist/*
```
### Trusted Publishing (GitHub Actions)
```yaml
- uses: pypa/gh-action-pypi-publish@release/v1
with:
password: ${{ secrets.PYPI_API_TOKEN }}
```
## Common Patterns
### Namespace Packages
For multi-repo packages sharing a namespace:
```
# Repo A
src/organvm/engine/__init__.py
# Repo B
src/organvm/dashboard/__init__.py
```
Use implicit namespace packages (no `__init__.py` at namespace level).
### Conditional Dependencies
```toml
dependencies = [
"tomli>=2.0; python_version < '3.11'",
"typing-extensions>=4.0; python_version < '3.12'",
]
```
### Package Data
```toml
[tool.hatch.build.targets.wheel]
packages = ["src/my_package"]
[tool.hatch.build.targets.wheel.force-include]
"assets" = "my_package/assets"
```
## Anti-Patterns to Avoid
- **setup.py without pyproject.toml** — Always use pyproject.toml as the single config source
- **Pinning exact versions in libraries** — Use compatible ranges to avoid dependency conflicts
- **Importing from project root in tests** — Use src layout or ensure editable install
- **Multiple version sources** — Keep version in exactly one place
- **requirements.txt as sole dependency spec** — Use pyproject.toml; generate lockfiles from itRelated Skills
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