Python Code Style

Python Code Style

Single owner of "what does well-styled Python look like in this stack": ruff (lint + format) and numpydoc docstrings. This skill is explicitly manual — Claude runs ruff on the files it has just touched, no hook involved.

Stop conditions — read before anything else

• Do not configure a PostToolUse / PreToolUse hook for ruff. This skill is intentionally manual. A hook tightens the loop in ways that bite (every micro-edit triggers a fix cycle, partial files fail D-rule checks mid-write, retries can stall the turn). If the user explicitly asks for an automated hook later, redirect to update-config — but the default is "Claude runs ruff itself."
• Do not substitute ruff with black / isort / flake8 / pydocstyle / pylint. Ruff is the canonical linter in this stack (data-science-python-stack Tier 1). If import ruff / pixi run ruff --version fails, route through python-env-manager to install — don't silently fall back.
• One fix attempt per file, then surface. If ruff check reports issues after Claude's first fix, address them once. If the same issue persists after the second pass, stop editing that file and surface the remaining diagnostics + diff to the user. This is the anti-infinite-loop guardrail — do not enter a third cycle on the same warning.
• Don't lint files outside the user's code. The hook scope is src/<pkg>/, experiments/, audit/, data/eda.py (the explore-ml-data EDA script), top-level *.py scripts, and any package directory the user owns. Skip vendored paths, generated files, the rest of user-owned data/, and anything under .pixi/, .venv/, node_modules/, etc.
• Never write ruff.toml from memory. The bundled templates/ruff.toml is the single source of truth — it encodes the per-file ignores (experiments/**), the numpydoc convention, and the rule selection this stack expects. Initial setup requires Read .agents/skills/python-code-style/templates/ruff.toml this turn, then Write <project-root>/ruff.toml verbatim from that file's content. Authoring a custom ruff.toml from training- data memory drops half the contract silently. If you catch yourself typing [lint] / [format] / select = [...] without having read the template this turn, STOP and Read it first.
• Don't call warnings.filterwarnings(...) unless the user explicitly asks for it. Same for warnings.simplefilter, @pytest.mark.filterwarnings, and filterwarnings = [...] in pytest.ini / pyproject.toml. Warnings are signal in this stack.
• Documentation describes the problem, not the workflow. A committed file's module docstring, header, and comments must describe the data-science problem and the file's role in it — never the skills, the gates (G-*), the cell runner, the run digest, the journal / backlog / design-note machinery, or "the process we are following". That guidance is agent-facing and lives in the skills, not in the user's files. When you touch a file that now carries real content, rewrite any leftover generic template or workflow prose into concise, problem-specific docs grounded in the current context (the project goal, the experiment's hypothesis, the dataset). If the file is still an empty skeleton (no content / no context yet), leave its placeholder — the contextualization happens when the content lands. Details: § "Contextualize the comments".

Pre-flight — emit this checklist as visible text before running ruff

Pre-flight (python-code-style):
- [ ] ruff importable in the project's env (`pixi run ruff --version`
      succeeds, per `data-science-python-stack` Tier 1)
- [ ] `ruff.toml` present at project root.
      If absent AND stack + workspace are already set up: the
      bundled template MUST be read **this turn** before being
      written verbatim.
      Evidence: Read .agents/skills/python-code-style/templates/ruff.toml
                (this turn) + Write <project-root>/ruff.toml (this turn)
                | "n/a — ruff.toml already at project root"
      **Inline-authored ruff.toml from memory is NOT evidence.**
- [ ] File list ready: <abs paths of .py files touched this turn>
- [ ] Decision recorded: this is the first ruff pass on these files
      (proceed) | second pass (proceed but stop on persistent
      issues) | third pass on same warning (STOP, surface to user)
- [ ] One-fix-per-file rule acknowledged: max two passes per warning,
      then surface remaining diagnostics + diff to the user.
- [ ] Comments contextualized: each touched file with real content has
      problem-specific docs and NO workflow/skill/gate/runner/digest
      meta (§ "Contextualize the comments")
      Evidence: per file, "rewrote header to <problem context>" |
                "no leftover template/workflow prose" |
                "n/a — empty skeleton, no context yet"

Scope

• In scope: running ruff format + ruff check --fix + ruff check on Python files Claude has just generated or edited; authoring numpydoc docstrings on public functions and classes; contextualizing each touched file's comments to the data-science problem and stripping workflow/process meta (§ "Contextualize the comments"); dropping the ruff.toml template into a fresh project.
• Out of scope: type hints (mypy / pyright are not in the stack); naming conventions ruff doesn't enforce; setting up PostToolUse / PreToolUse hooks; linting non-Python files.

What to run, in what order

For every Python file touched this turn (call them <files>), run inside the project's environment manager — pixi run for pixi projects, equivalent for uv / poetry / conda (per python-env-manager):

pixi run ruff format <files>
pixi run ruff check --fix <files>
pixi run ruff check <files>

Three steps, in order:

1. ruff format — applies the formatter (line length, quoting, trailing commas, blank lines around defs). Idempotent.
2. ruff check --fix — auto-fixes everything ruff knows how to fix in place: import sorting (I), legacy syntax (UP), detectable bug patterns (B).
3. ruff check (no --fix) — final pass. Anything reported here needs Claude's attention: missing docstrings (D), undefined names (F), code structure issues. Address them, then re-run the trio. Apply the one-fix-per-file rule from Stop conditions.

If a file under experiments/ or audit/, or the data/eda.py EDA script, has a D100 ("missing module docstring") or D103 ("missing function docstring") warning, that's expected for # %% cells; the bundled ruff.toml per-file-ignores D100 + D103 (and E402, B018) for experiments/**, audit/**, and data/eda.py. If you're seeing them, the ruff.toml isn't loaded — check that it lives at the project root.

Audit files (audit/<NN>_<short_name>.py, owned by audit-ml-pipeline) and the EDA script (data/eda.py, owned by explore-ml-data) lint the same way as experiment files: same # %% cell convention, same per-file ignores, same NumPyDoc convention for any helper functions. After writing or editing one of these files, run the same trio (ruff format → ruff check --fix → ruff check).

Contextualize the comments

ruff makes a file well-formed; this pass makes it well-documented for the problem. Templates ship with neutral placeholders and a little authoring scaffolding so the generating skill knows what each cell / module is for. None of that should survive into the user's committed file — the user's files document the data-science problem, not the process that produced them.

After the ruff trio, for every touched file that now carries real content, do a quick documentation pass:

1. Fill the header for this problem. Replace any <placeholder> or generic header with a one- or two-line description of what this file does here: the experiment's hypothesis (experiment.py), what this module contributes to the pipeline (src/<pkg>/*.py), which report this file reviews and what it tests (audit/<stem>.py), what the dataset is and what the analysis looks at (data/eda.py). Pull the wording from the live context — the project goal, the approved design note, the dataset.
2. Strip the workflow meta. Delete leftover process commentary: skill names, gate IDs (G-*), § cross-references, "the agent", "the (cell) runner", "the digest", "run cell by cell", journal / backlog / sourcing jargon, and inline guard-rails like "MUST NOT call put / bare expressions, don't print". Those guard-rails stay enforced — they live in the owning skill's SKILL.md, which is where the agent reads them, not in the user's file.
3. Keep the substance. Genuinely useful problem / engineering context and the numpydoc docstrings stay. Cell markers (# %%) and any remaining <...> placeholders the agent still has to fill stay until they are filled.

The result should read like a colleague wrote the file for this project — not like a generated scaffold. Skip a file that is still an empty skeleton (e.g. a freshly scaffolded src/<pkg>/*.py with no body yet): there is no context to write about until the content lands, and the contextualization happens on the edit that fills it.

This pass is owned here so the rule is enforced uniformly. Every file-writing skill already hands off to this skill after a write; that hand-off now also covers contextualizing the comments.

Numpydoc — the docstring convention

Public functions and classes carry numpydoc-format docstrings; ruff's D rules with pydocstyle.convention = "numpy" enforce the shape.

A bare one-line summary is NOT sufficient for public functions. The Parameters / Returns (and Raises when applicable) sections are mandatory — even when the function is small, even when the user says "just the summary is fine". Approving a one-line docstring on a public function silently fails the contract this skill enforces; the function looks D-rule-clean (D100/D103 don't fire) but the parameter shapes and return type that callers actually need are missing. Private helpers (_leading_underscore) are the only exception: the default D rules allow them to omit docstrings, but public callable surfaces always carry the full numpydoc shape.

Skeleton:

def predict_price(X, model, *, n_jobs=1):
    """Predict option prices from a feature matrix.

    Parameters
    ----------
    X : pandas.DataFrame
        Feature matrix with one row per option.
    model : sklearn.base.BaseEstimator
        Fitted estimator with a ``predict`` method.
    n_jobs : int, default=1
        Number of parallel jobs.

    Returns
    -------
    numpy.ndarray of shape (n_samples,)
        Predicted prices, one per row of ``X``.
    """

Conventions worth surfacing because they're non-obvious:

• One-line summary on the first line, in the imperative mood ("Predict ..." not "Predicts ..."). No trailing period in the summary line if D400 is enabled — but in numpy convention it is, so write the period.
• Blank line between summary and the rest.
• Parameter shapes go in the type slot, not the description, e.g. X : ndarray of shape (n_samples, n_features).
• Returns section lists the return value; if there are multiple returns, list each on its own row. Don't omit the type.
• Private helpers (_leading_underscore) don't need a docstring under the default D rules — ruff allows that.
• Modules (top of file) should start with a one-line summary. Skipped under experiments/** per the bundled ruff.toml.

Initial setup — dropping the ruff.toml template

When this skill is invoked on a fresh project that has no ruff.toml at its root and the stack + workspace have been scaffolded by their respective skills:

1. Read the bundled template with the file-reading tool this turn: Read .agents/skills/python-code-style/templates/ruff.toml. The pre-flight Evidence row for the ruff.toml present check requires this read; an inline-authored config from memory does not satisfy it.
2. Write the content verbatim to <project-root>/ruff.toml. No edits, no "improvements", no rule additions. The template encodes the per-file ignores (experiments/**), the pydocstyle.convention = "numpy" setting, and the rule selection this stack expects. Diverging from it drops half the contract.
3. Verify ruff picks it up: pixi run ruff check --show-settings . should report the numpy convention and the select list from the template.

Do not fold ruff config into pyproject.toml automatically — the project may not have one, or the user may prefer a separate file. The standalone ruff.toml is unambiguous.

Forbidden shortcuts:

When ruff finds something Claude didn't write

A common case: Claude edits one function in a file that already had unrelated D-rule violations. Ruff will report those too.

• In scope of this turn: the lines Claude touched. Fix those.
• Out of scope: pre-existing warnings in untouched code. Mention them in the response so the user can choose to address them, but don't drag every warning into the current task.

This keeps PR scope tight and avoids "while I was here" expansion that the user didn't ask for.

Companion skills

• data-science-python-stack — owns the decision that ruff is Tier 1 mandatory; this skill assumes ruff is already installed. If pixi run ruff --version fails, return there for the install.
• python-env-manager — turns "ruff is missing" into the right install command for the project's manager. Don't run pip install ruff in a pixi project.
• organize-ml-workspace — sets up the directory layout that the bundled ruff.toml's per-file ignores (experiments/** and audit/**) reference. Drop the template after this skill has run, so the paths it ignores actually exist.
• audit-ml-pipeline — generates audit files at audit/<NN>_<short_name>.py. This skill's per-file ignores cover the audit/ path the same way they cover experiments/.
• explore-ml-data — generates the EDA script data/eda.py (same # %% cell convention). The bundled ruff.toml per-file-ignores data/eda.py exactly like audit/**; lint it with the same trio after writing it.
• update-config — only relevant if the user explicitly asks for an automated lint hook later. Default is no hook.

Conventions

• Manual, not automatic. Claude calls ruff itself, file by file. No hook.
• One-fix-per-file rule. Hard cap on retries — second pass max, then surface.
• Project-root config only. ruff.toml lives at the project root and is the single source of truth. Don't add per-directory overrides unless the user asks.
• Don't widen scope on touched files. Fix what Claude wrote; surface (don't auto-fix) pre-existing issues elsewhere.