python-packaging

Overview of python-packaging skill

What the python-packaging skill is for

The python-packaging skill helps an agent turn a Python codebase into a distributable package with the right project structure, packaging metadata, build configuration, and release workflow. It is aimed at people who need more than “make this installable” and want output aligned with modern Python packaging standards, especially pyproject.toml, wheels, source distributions, and PyPI-style publishing.

Who should use this python-packaging skill

This skill is a strong fit if you are:

• packaging a reusable library
• shipping a CLI tool with console entry points
• preparing a project for PyPI or a private package index
• migrating from ad hoc scripts or old setup.py patterns
• deciding between setuptools, hatchling, flit, or Poetry-based packaging

It is less useful if your project is only an internal script repo with no install or distribution requirements.

The real job to be done

Most users do not just want files generated. They want to make correct packaging decisions early: layout, metadata, dependencies, editable installs, build backend, test-install flow, and publication path. The python-packaging skill is valuable because it frames those choices explicitly instead of treating packaging as a single boilerplate dump.

What differentiates it from a generic packaging prompt

The main advantage of the python-packaging skill is decision coverage. It does not stop at a minimal pyproject.toml. It also covers:

• recommended src/ layout
• modern PEP-based packaging standards
• backend selection tradeoffs
• wheels vs source distributions
• package metadata and classifiers
• entry points for CLIs
• advanced patterns like package data and namespace packages

That makes it more useful for Deployment-oriented work than a generic “create Python package files” prompt.

What to check before installing

Before you adopt the python-packaging skill, confirm that you can answer these basics:

• Is this a library, CLI, plugin, or namespace package?
• Do you need PyPI publication or only internal distribution?
• Do you already manage dependencies with Poetry or another tool?
• Do you need package data, typed packages, or compiled extensions?
• Are you targeting modern packaging only, or supporting legacy workflows too?

If those decisions are still fuzzy, this skill can still help, but the quality of the result depends heavily on the quality of your inputs.

How to Use python-packaging skill

Install the python-packaging skill

Install from the repository with:

npx skills add https://github.com/wshobson/agents --skill python-packaging

If your environment already has the repository locally, make sure the skill is available from plugins/python-development/skills/python-packaging.

Read these files first

For this python-packaging skill, start with:

• SKILL.md
• references/advanced-patterns.md

SKILL.md gives the main workflow and tool choices. references/advanced-patterns.md matters if your project needs data files, namespace packages, richer release setup, or installation testing beyond a basic package skeleton.

Know what input the skill needs

The python-packaging usage quality depends on concrete repo context. Give the agent:

• package name
• import name
• project type: library, CLI, plugin, internal package
• target Python versions
• desired build backend
• dependency groups: runtime, optional, dev
• whether you want src/ layout
• whether you will publish to PyPI, TestPyPI, or a private index
• whether you need console scripts, data files, or namespace packaging

Without that, the agent can still scaffold something, but it may choose the wrong backend or metadata shape.

Turn a rough goal into a strong prompt

Weak prompt:

Package this Python project for release.

Better prompt:

Use the python-packaging skill to convert this repo into a distributable package. Use a src layout, setuptools with pyproject.toml, Python 3.10+, one console entry point named my-tool, optional dev dependencies for pytest and ruff, and prepare for publishing to PyPI. Show the exact files to add or change and explain any assumptions.

Why this works better:

• names the packaging style
• specifies backend and Python floor
• includes CLI requirements
• defines dependency grouping
• sets the release target
• asks for file-level changes, not vague advice

Choose the right backend on purpose

A practical python-packaging guide should not treat build backends as interchangeable.

Use the skill to make an explicit choice:

• setuptools: safest default, broad ecosystem support
• hatchling: cleaner modern default if you want fewer legacy conventions
• flit: good for simple pure-Python libraries
• Poetry: useful if you already depend on Poetry workflows, but may be too opinionated if you only want packaging

If you do not care strongly, ask the agent to recommend one and justify it against your repo shape.

Prefer src layout unless you have a reason not to

The skill strongly points toward src/package_name/, and that is usually the right decision for installable packages. It reduces accidental imports from the repository root and catches packaging mistakes earlier.

Ask for a flat layout only when you intentionally want a minimal local-only structure and understand the tradeoff.

Use the skill for Deployment, not just scaffolding

python-packaging for Deployment is where this skill has more value than a generic prompt. Ask it to cover the full path:

1. define metadata in pyproject.toml
2. build wheel and source distribution
3. install the built artifacts in a clean environment
4. verify entry points and imports
5. prepare upload steps for PyPI or internal index

If you only ask for boilerplate files, you miss the release-quality checks that usually cause the real delays.

Ask for install-testing explicitly

One common packaging mistake is assuming the package works because imports succeed inside the repo. Use the skill to generate a verify step such as:

• build distributions
• create a fresh virtual environment
• install the wheel
• run import smoke tests
• run the CLI entry point

This catches missing package data, bad entry point declarations, and layout errors before release.

Use advanced patterns only when your repo needs them

Read references/advanced-patterns.md if your project needs:

• packaged data files like JSON, templates, or static assets
• py.typed support
• namespace packages split across repos
• more advanced versioning or distribution patterns

Do not ask the agent to include these by default. They add complexity and should be driven by actual project requirements.

Good repository-reading workflow

A practical workflow for python-packaging install and setup decisions is:

1. inspect current repo layout
2. identify import package name vs project name
3. decide backend and release target
4. define metadata and dependencies
5. add entry points or package data if needed
6. build and test the distribution
7. review publish steps

This sequence matters because backend choice and layout affect nearly every later file.

What a strong output should contain

A good result from the python-packaging skill usually includes:

• updated or new pyproject.toml
• package directory layout
• dependency declarations
• build-system configuration
• CLI entry points if needed
• package data config if needed
• install/build/test commands
• publication guidance for the chosen index

If the output only gives a tiny pyproject.toml with no validation path, it is probably too shallow.

python-packaging skill FAQ

Is the python-packaging skill beginner friendly?

Yes, if you already know what your project is supposed to become. It is not ideal for someone who is new to both Python and packaging concepts at the same time, because backend choice, metadata, and distribution targets still require decisions.

Is this better than a normal prompt about Python packaging?

Usually yes. A normal prompt often produces a generic setup.py or a thin pyproject.toml without explaining tradeoffs. The python-packaging skill is better when you need a packaging plan that matches real distribution goals.

Does it support modern Python packaging?

Yes. The repository content clearly centers modern standards such as pyproject.toml, PEP 517/518, PEP 621, and editable-install concepts like PEP 660.

Should I use python-packaging if I already use Poetry?

Yes, but be explicit. Tell the agent you want Poetry preserved if that is a hard requirement. Otherwise it may reasonably suggest another backend that better fits packaging-only needs.

When should I not use the python-packaging skill?

Skip it if:

• the project is not meant to be installed
• you only need a local script folder
• you need a highly specialized compiled-extension build system beyond the skill’s practical scope
• your organization already mandates an internal packaging template you must follow exactly

Can it help with namespace packages and package data?

Yes. That is one of the stronger reasons to read the advanced reference. These are common failure points in packaging, and the skill provides patterns that go beyond a minimal starter config.

How to Improve python-packaging skill

Give packaging constraints, not just end goals

To improve python-packaging results, specify constraints such as:

• exact Python support range
• public vs private index
• backend preference
• typed package requirements
• whether data files must be included
• whether imports must stay backward compatible

These details shape file layout and metadata more than most users expect.

Show the current repo tree

A short directory tree greatly improves output quality. For example:

src/, tests/, existing requirements.txt, current CLI module, and any assets folder. This helps the agent avoid inventing the wrong package boundary or missing non-code files.

State what must remain stable

Common packaging work breaks import paths or command names. Tell the agent what cannot change:

• package import name
• existing CLI command
• current versioning strategy
• CI commands
• release process requirements

This is especially important when migrating from legacy packaging files.

Ask for justification, not only generated files

A better prompt is:

Use the python-packaging skill and explain why you chose setuptools over hatchling for this repo.

That forces a decision trace you can review, instead of accepting a default that may not fit your workflow.

Watch for these common failure modes

The most common weak outputs are:

• mixing old setup.py habits with modern pyproject.toml unnecessarily
• choosing flat layout when src/ would be safer
• forgetting package data inclusion
• mismatching project name and import package name
• defining console scripts without validating install behavior
• giving publish steps without clean-environment install tests

Review for these before implementing.

Iterate after the first draft

After the first result, refine with targeted follow-ups:

• Convert this to hatchling.
• Add package data for templates and py.typed.
• Prepare this package for TestPyPI first.
• Keep the current import path but add a console script.
• Make this a namespace package across two repos.

The skill becomes more useful when treated as a packaging decision partner, not a one-shot file generator.

Improve python-packaging for Deployment workflows

If your main use case is release readiness, ask the skill to produce:

• build commands
• artifact verification steps
• clean-environment install tests
• publish commands
• rollback or version bump guidance

That turns the python-packaging guide into something actionable for Deployment instead of a passive config suggestion.

Use the advanced reference selectively

references/advanced-patterns.md is best used when the base setup is already clear. Start simple, then pull in only the advanced patterns that solve a real packaging problem. This keeps the final package easier to maintain and reduces accidental complexity.