tooling-python-generator_skill

Overview

This skill generates modern Python project scaffolding tailored to your chosen project type and dependency manager. It produces cross-platform layouts, pyproject.toml or Pipfile/requirements templates, testing, type-checking, linting, formatting, pre-commit hooks, and packaging metadata. The output is deterministic, reproducible, and ready for CI/CD integration.

How this skill works

Provide inputs for project_name, project_type (library|application|cli|data-pipeline), python_version (3.9, 3.10, 3.11, 3.12) and dependency_manager (poetry|pipenv|pip-tools). The skill validates inputs, generates a file/directory structure, and fills configuration files for pytest, mypy, ruff, black, pre-commit, and build tooling. For full setups it also generates Makefile targets, packaging metadata, entry points for CLIs, and CI/CD templates where requested.

When to use it

• Starting a new Python library, application, CLI, or data-pipeline project that needs modern tooling.
• Migrating legacy projects to a reproducible, typed, and linted workflow.
• Standardizing developer environments across multiple repositories.
• Preparing a package for PyPI publishing with proper build metadata and CI.
• Onboarding new contributors with a consistent developer experience.

Best practices

• Prefer src/ layout for libraries and flat layout for application-style projects following src-vs-flat guidance.
• Use Poetry for libraries destined for PyPI, pipenv for application-focused deployables, pip-tools for minimal environments.
• Pin runtime dependencies for applications; use caret ranges for library runtime deps and pin dev deps for reproducibility.
• Enforce typing incrementally: enable strict mypy for libraries, relax for integration-heavy applications.
• Run ruff and black in pre-commit to keep diffs small and CI fast; include mypy and test hooks where appropriate.

Example use cases

• Create a typed Python library with src/project_name, py.typed, poetry-managed packaging, and CI that builds wheels and publishes on tag.
• Scaffold a CLI tool using src layout, a poetry [tool.poetry.scripts] entry point, and shell completion hooks.
• Set up an application repo with pipenv, flat package layout, pinned dependencies, integration test config, and Docker-friendly settings.
• Provision a data-pipeline project with Jupyter-friendly docs, numpy/pandas stubs, pytest markers for slow/integration tests, and perf-focused linters.