python-code-style

Overview of python-code-style skill

What python-code-style skill helps you do

The python-code-style skill gives an agent a concrete playbook for Python formatting, linting, naming, type hints, and docstring standards. It is most useful when you want more than “make this Python cleaner” and need actionable, tool-aligned guidance for writing new code, reviewing pull requests, or setting project conventions.

Best fit for teams and reviewers

This skill fits developers, tech leads, and reviewers who want consistent Python output across files and contributors. It is especially relevant for:

• new Python projects choosing tooling
• code review workflows that need style decisions to be repeatable
• teams standardizing ruff, mypy, or pyright
• authors improving public API documentation and type coverage

Real job-to-be-done

Most users are not looking for generic PEP 8 reminders. They want the agent to:

• apply modern Python defaults quickly
• suggest config that can live in pyproject.toml
• clean up naming and structure without overediting logic
• improve docstrings and type hints in a way that supports real maintenance

Key differentiators

Compared with a plain prompt, python-code-style is more decision-oriented. It emphasizes:

• automated formatting over manual style debate
• ruff as the default modern lint/format anchor
• explicit naming conventions
• documentation as part of code quality, not a separate afterthought
• type annotations for public APIs

What to know before installing

This is a guidance skill, not a code transformer with bundled scripts. The repository only exposes SKILL.md, so adoption depends on how well you prompt the agent and how clearly you provide project context. If you want one-click enforcement, you will still need to wire the recommended tools into your own repo and CI.

How to Use python-code-style skill

python-code-style install context

Install the skill into your compatible skills setup:

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

After install, the most relevant source to read is:

• plugins/python-development/skills/python-code-style/SKILL.md

Because this skill has no extra rules/, resources/, or helper scripts, most of the value is in invoking it with strong context.

When to call python-code-style skill

Use the python-code-style skill when your task is primarily about style and maintainability, such as:

• “standardize this module to modern Python conventions”
• “review this PR for naming, docstrings, and typing issues”
• “propose pyproject.toml linting config for this package”
• “rewrite these docstrings consistently”
• “make this codebase review-ready for stricter CI”

Do not use it as your main skill for debugging runtime failures or redesigning architecture.

What input the skill needs

The skill works best when you provide:

• Python version target, such as 3.11 or 3.12
• current tooling, if any: ruff, black, flake8, mypy, pyright
• whether you want review comments, config proposals, or rewritten code
• a code sample or affected files
• team constraints, such as line length, strict typing, or docstring style

Without those inputs, the agent will default to reasonable modern guidance, but the result may not match your repository standards.

Turn a rough goal into a strong prompt

Weak prompt:

Clean up this Python file.

Stronger prompt:

Use the python-code-style skill to review this Python module for formatting, naming, docstrings, and public API type hints. Target Python 3.11. We use ruff and want to consolidate older flake8/isort habits into pyproject.toml. Keep behavior unchanged. Return: 1) prioritized findings, 2) suggested config, 3) patched code for the top issues.

That stronger version works better because it defines scope, tools, output shape, and safety constraints.

Best prompt pattern for code review

For python-code-style for Code Review, ask the agent to separate correctness from style:

Use the python-code-style skill for a style-focused review only.
Check:
- formatter/linter consistency
- naming clarity
- docstrings for public functions/classes
- type hints on public APIs
- import organization
- obvious readability issues

Do not suggest architecture changes unless they directly improve style consistency.
Classify findings as:
- must-fix for team standardization
- should-fix for readability
- optional polish

This avoids noisy reviews that mix style with unrelated redesign advice.

Best prompt pattern for repository setup

If you are adopting standards in a new repo, ask for config and rationale together:

Use the python-code-style skill to propose a minimal Python style baseline for a new service.
Constraints:
- Python 3.12
- use `ruff` and `mypy`
- prefer one main config file in `pyproject.toml`
- line length 100
- strict typing for public APIs, practical typing elsewhere

Return:
1. recommended `pyproject.toml` sections
2. naming and docstring rules the team should enforce
3. a short rollout plan for existing files

This gets you an install-ready baseline instead of abstract advice.

Tooling guidance the skill is optimized for

The source strongly centers modern tooling:

• ruff for linting and formatting
• mypy for type checking
• pyright as another supported type-checking option

A practical takeaway: if your repo still uses several older single-purpose tools, the python-code-style skill is good for planning simplification, especially around consolidating style checks.

Suggested workflow after installation

A practical usage flow:

1. read SKILL.md once to understand its defaults
2. give the agent your Python version and current toolchain
3. start with one representative file or one PR
4. ask for findings before asking for rewrites
5. convert the approved standards into pyproject.toml and CI checks

This sequence reduces overcorrection and helps teams align on standards before bulk edits.

Repository-reading path that saves time

Because the repo only exposes a single skill document, skim it in this order:

1. “When to Use This Skill”
2. “Core Concepts”
3. “Quick Start”
4. “Fundamental Patterns”

That path tells you quickly whether the skill matches your stack and whether its defaults align with your team’s style philosophy.

Practical constraints and tradeoffs

The skill is opinionated in useful ways, but those opinions affect fit:

• it favors automation over manual formatting judgment
• it leans toward modern typing expectations
• it assumes style consistency is worth enforcing via tools
• it is stronger on standards and review than on framework-specific conventions

If your team intentionally avoids strict type hints or uses a highly customized house style, expect to adapt the outputs rather than accept them verbatim.

python-code-style skill FAQ

Is python-code-style skill worth using if I already know PEP 8?

Yes, if your problem is consistency at team scale. PEP 8 knowledge alone does not tell an agent how to prioritize ruff, what to enforce in config, or how to make style review repeatable across a codebase.

Is python-code-style suitable for beginners?

Yes, especially for small, concrete tasks like:

• improving one module’s naming
• adding docstrings to public functions
• proposing a starter pyproject.toml

Beginners should ask for explanations with each recommendation so the output teaches standards instead of just applying them.

How is this different from a normal prompt?

A normal prompt often produces generic cleanliness advice. The python-code-style usage pattern is better when you want the agent to anchor on Python style systems: formatter-first workflows, naming conventions, type coverage, and documentation quality.

Does it help with tool configuration?

Yes. The upstream skill explicitly points users toward ruff and mypy, and it includes configuration-oriented guidance. That makes it useful not just for reviewing code, but for deciding what standards your repo should enforce.

Is python-code-style for Code Review a good fit?

Yes. This is one of the clearest use cases. It helps when you want style review to be:

• less subjective
• more tool-compatible
• easier to turn into automated checks

It is less useful if your review need is mainly about business logic or performance.

When should I not use python-code-style skill?

Skip it when:

• your task is debugging behavior, not improving style
• your repo is not Python
• you need framework-specific best practices more than general Python standards
• you want a fully automated migration tool rather than review and guidance

Does the skill include extra scripts or templates?

No. Based on the repository structure, there are no helper scripts or supporting reference files for this skill. Plan on using the guidance through prompts, then implementing configs and checks in your own repository.

How to Improve python-code-style skill

Give the agent repo-specific standards first

The fastest way to improve python-code-style output is to state your house rules up front:

• Python version
• line length
• preferred docstring style
• strictness of typing
• whether behavior-preserving edits only are allowed

This prevents generic recommendations that conflict with your actual CI or team conventions.

Provide one representative file before whole-codebase requests

If you start with an entire repository, the first output may stay broad. Instead, provide one file that reflects your real coding style problems. Then ask the agent to generalize rules from that sample. This yields more usable standards and less cleanup churn.

Ask for prioritized findings, not a giant rewrite

A common failure mode is receiving too many low-value edits. Better prompt:

Use the python-code-style skill and give me the top 10 style issues that most affect maintainability, ordered by impact and ease of enforcement.

That makes adoption easier because teams can fix policy-level issues before cosmetic ones.

Separate style fixes from logic changes

Tell the agent explicitly:

• preserve behavior
• avoid refactors unless they are needed for clarity
• do not rename externally exposed APIs without calling it out

This matters because style passes can accidentally expand into interface changes if the prompt is too open-ended.

Improve type-hint results with API boundaries

If you want stronger typing advice, identify:

• public vs internal functions
• libraries or frameworks in use
• whether strict checking is desired
• compatibility constraints for older Python versions

The skill encourages public API type hints, but the quality of suggestions improves a lot when the agent knows where strictness should stop.

Improve docstring output with audience and style

Docstring rewrites are much better if you specify:

• Google, NumPy, or minimal style
• whether docs are for internal developers or external users
• whether examples should be included
• whether private helpers need docstrings at all

Without this, the agent may generate documentation that is technically cleaner but not aligned with your team’s documentation norms.

Watch for common failure modes

Typical weak-output patterns include:

• enforcing style tools you do not use
• overusing type hints in low-value private code
• rewriting names without considering API compatibility
• adding verbose docstrings to obvious internal helpers
• suggesting migration steps without regard to existing CI

These are prompt-fixable most of the time.

Iterate after the first pass

A high-quality python-code-style guide workflow is iterative:

1. ask for findings
2. approve or reject the standard
3. ask for revised config or patch
4. validate against your CI and reviewer expectations
5. then scale to more files

That approach is better than accepting a one-shot rewrite, especially in older codebases.

Turn accepted advice into enforceable standards

The skill becomes much more valuable when its recommendations become automation:

• pyproject.toml config
• CI lint/type jobs
• PR review checklists
• team docs for naming and docstrings

If you stop at a one-time cleanup, style drift usually returns.

Use python-code-style skill as a policy layer

The best long-term use of the python-code-style skill is not just fixing one file. It is using the skill to define a repeatable policy for how your team writes and reviews Python. That is where it adds more value than a generic prompt.