poetry
$
npx mdskill add TerminalSkills/skills/poetryManage Python projects with Poetry for dependencies, packaging, and virtual environments
- Solves Python project setup, dependency management, and PyPI publishing tasks
- Uses Poetry, Python 3.8+, and standard packaging tools
- Analyzes user requests to determine project structure and dependency needs
- Executes Poetry commands and returns configuration or output to the user
SKILL.md
.github/skills/poetryView on GitHub ↗
---
name: poetry
description: >-
Manage Python projects with Poetry. Use when a user asks to manage Python
dependencies, create virtual environments, publish packages to PyPI,
handle dependency resolution, or set up a Python project structure.
license: Apache-2.0
compatibility: 'Python 3.8+'
metadata:
author: terminal-skills
version: 1.0.0
category: development
tags:
- poetry
- python
- dependencies
- packaging
- virtual-environment
---
# Poetry
## Overview
Poetry is a Python dependency manager and build tool. It handles virtual environments, dependency resolution (with a lock file), project scaffolding, and PyPI publishing. The modern replacement for pip + setuptools + virtualenv.
## Instructions
### Step 1: New Project
```bash
# Install Poetry
curl -sSL https://install.python-poetry.org | python3 -
# Create new project
poetry new my-api
cd my-api
# Or init in existing directory
poetry init
```
### Step 2: Manage Dependencies
```bash
# Add dependencies
poetry add fastapi uvicorn sqlalchemy
poetry add pydantic-settings
# Add dev dependencies
poetry add --group dev pytest pytest-asyncio pytest-cov ruff mypy
# Remove
poetry remove requests
# Update
poetry update # update all within constraints
poetry update fastapi # update specific package
poetry lock # regenerate lock file without installing
```
### Step 3: pyproject.toml
```toml
# pyproject.toml — Project configuration
[tool.poetry]
name = "my-api"
version = "1.0.0"
description = "Project management API"
authors = ["Team <team@example.com>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.11"
fastapi = "^0.110"
uvicorn = {extras = ["standard"], version = "^0.27"}
sqlalchemy = "^2.0"
pydantic-settings = "^2.0"
[tool.poetry.group.dev.dependencies]
pytest = "^8.0"
pytest-asyncio = "^0.23"
pytest-cov = "^4.1"
ruff = "^0.3"
mypy = "^1.8"
[tool.poetry.scripts]
serve = "my_api.main:start"
migrate = "my_api.db:run_migrations"
[tool.ruff]
target-version = "py311"
line-length = 100
[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
```
### Step 4: Use
```bash
# Activate virtual environment
poetry shell
# Run without activating
poetry run python main.py
poetry run pytest
poetry run serve # custom script from pyproject.toml
# Export for Docker (no Poetry needed in container)
poetry export -f requirements.txt -o requirements.txt --without-hashes
# Build and publish to PyPI
poetry build
poetry publish
```
## Guidelines
- Always commit `poetry.lock` — ensures reproducible installs across environments.
- Use `poetry export` for Docker — don't install Poetry in production containers.
- Group dev dependencies with `--group dev` — they're excluded from production installs.
- `poetry.lock` resolves ALL transitive dependencies — no more "works on my machine."
- Alternative: `uv` (from Astral, makers of Ruff) — 10-100x faster, compatible with pip/Poetry.