{"slug":"probabl-ai-python-code-style","name":"python-code-style","description":"Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Three responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, (2) run ruff against any Python files Claude has just generated or edited, and (3) contextualize each touched file's comments to the data-science problem — rewriting any leftover template / workflow prose (skill names, gates, runner, digest, guard-rails) into concise, problem-specific docs so the user's committed files read like a colleague wrote them, not like a generated scaffold. Stops at \"the touched files pass `ruff check` and document the problem, not the process.\" TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run AND the file's comments are contextualized to the problem; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and th","long_description":"---\nname: python-code-style\ndescription: >\n Owns Python code style for this stack: ruff for lint + format, numpydoc\n for docstrings. Three responsibilities — (1) place the project's\n `ruff.toml` from the bundled template once the stack and workspace\n are in place, (2) run ruff against any Python files Claude has\n just generated or edited, and (3) contextualize each touched file's\n comments to the data-science problem — rewriting any leftover\n template / workflow prose (skill names, gates, runner, digest,\n guard-rails) into concise, problem-specific docs so the user's\n committed files read like a colleague wrote them, not like a\n generated scaffold. Stops at \"the touched files pass `ruff check`\n and document the problem, not the process.\"\n\n TRIGGER when (any of these):\n (1) a Python file was just created or edited via Write / Edit /\n MultiEdit — invoke this skill before declaring the task done so\n ruff is run AND the file's comments are contextualized to the\n problem;\n (2) a fresh ML workspace was just scaffolded by\n `organize-ml-workspace` and the project has no `ruff.toml` at\n its root yet — drop the bundled template;\n (3) the user asks about lint, format, docstring style, or reaches\n for `black` / `isort` / `flake8` / `pydocstyle` (redirect to\n ruff — the stack's canonical linter, owned by\n `data-science-python-stack` Tier 1).\n\n SKIP when: the project is non-Python; the only edits in this turn\n are to Markdown / TOML / JSON / YAML; the file lives in a\n third-party vendored directory the user doesn't own.\n\n HOW TO USE: run ruff manually on the files you just touched — do\n not configure a PostToolUse hook for this. **Read the \"Stop\n conditions\" block and emit the Pre-flight checklist as visible\n text in your response — both are mandatory before running ruff.**\n---\n\n# Python Code Style\n\nSingle owner of \"what does well-styled Python look like in this\nstack\": ruff (lint + format) and numpydoc docstrings. This skill is\nexplicitly **manual** — Claude runs ruff on the files it has just\ntouched, no hook involved.\n\n## Stop conditions — read before anything else\n\n- **Do not configure a PostToolUse / PreToolUse hook for ruff.** This\n skill is intentionally manual. A hook tightens the loop in ways\n that bite (every micro-edit triggers a fix cycle, partial files\n fail D-rule checks mid-write, retries can stall the turn). If the\n user explicitly asks for an automated hook later, redirect to\n `update-config` — but the default is \"Claude runs ruff itself.\"\n- **Do not substitute ruff with `black` / `isort` / `flake8` /\n `pydocstyle` / `pylint`.** Ruff is the canonical linter in this\n stack (`data-science-python-stack` Tier 1). If `import ruff` /\n `pixi run ruff --version` fails, route through `python-env-manager`\n to install — don't silently fall back.\n- **One fix attempt per file, then surface.** If `ruff check`\n reports issues after Claude's first fix, address them once. If the\n *same* issue persists after the second pass, stop editing that\n file and surface the remaining diagnostics + diff to the user.\n This is the anti-infinite-loop guardrail — do not enter a third\n cycle on the same warning.\n- **Don't lint files outside the user's code.** The hook scope is\n `src//`, `experiments/`, `audit/`, `data/eda.py` (the\n explore-ml-data EDA script), top-level `*.py` scripts, and any\n package directory the user owns. Skip vendored paths, generated\n files, the rest of user-owned `data/`, and anything under `.pixi/`,\n `.venv/`, `node_modules/`, etc.\n- **Never write `ruff.toml` from memory.** The bundled\n `templates/ruff.toml` is the single source of truth — it encodes\n the per-file ignores (`experiments/**`), the numpydoc convention,\n and the rule selection this stack expects. Initial setup requires\n **`Read .agents/skills/python-code-style/templates/ruff.toml`**\n *this turn*, then `Write /ruff.toml` verbatim from\n that file's content. Authoring a custom `ruff.toml` from training-\n data memory drops half the contract silently. If you catch\n yourself typing `[lint]` / `[format]` / `select = [...]` without\n having read the template this turn, STOP and `Read` it first.\n- **Don't call `warnings.filterwarnings(...)` unless the user\n explicitly asks for it.** Same for `warnings.simplefilter`,\n `@pytest.mark.filterwarnings`, and `filterwarnings = [...]` in\n `pytest.ini` / `pyproject.toml`. Warnings are signal in this\n stack.\n- **Documentation describes the problem, not the workflow.** A\n committed file's module docstring, header, and comments must\n describe the **data-science problem** and the file's role in it —\n never the skills, the gates (`G-*`), the cell runner, the run\n digest, the journal / backlog / design-note machinery, or \"the\n process we are following\". That guidance is agent-facing and lives\n in the skills, not in the user's files. When you touch a file that\n now carries **real content**, rewrite any leftover generic template\n or workflow prose into concise, problem-specific docs grounded in\n the current context (the project goal, the experiment's hypothesis,\n the dataset). If the file is still an empty skeleton (no content /\n no context yet), leave its placeholder — the contextualization\n happens when the content lands. Details: § \"Contextualize the\n comments\".\n\n## Pre-flight — emit this checklist as visible text before running ruff\n\n```\nPre-flight (python-code-style):\n- [ ] ruff importable in the project's env (`pixi run ruff --version`\n succeeds, per `data-science-python-stack` Tier 1)\n- [ ] `ruff.toml` present at project root.\n If absent AND stack + workspace are already set up: the\n bundled template MUST be read **this turn** before being\n written verbatim.\n Evidence: Read .agents/skills/python-code-style/templates/ruff.toml\n (this turn) + Write /ruff.toml (this turn)\n | \"n/a — ruff.toml already at project root\"\n **Inline-authored ruff.toml from memory is NOT evidence.**\n- [ ] File list ready: \n- [ ] Decision recorded: this is the first ruff pass on these files\n (proceed) | second pass (proceed but stop on persistent\n issues) | third pass on same warning (STOP, surface to user)\n- [ ] One-fix-per-file rule acknowledged: max two passes per warning,\n then surface remaining diagnostics + diff to the user.\n- [ ] Comments contextualized: each touched file with real content has\n problem-specific docs and NO workflow/skill/gate/runner/digest\n meta (§ \"Contextualize the comments\")\n Evidence: per file, \"rewrote header to \" |\n \"no leftover template/workflow prose\" |\n \"n/a — empty skeleton, no context yet\"\n```\n\n## Scope\n\n- **In scope:** running `ruff format` + `ruff check --fix` + `ruff\n check` on Python files Claude has just generated or edited;\n authoring numpydoc docstrings on public functions and classes;\n contextualizing each touched file's comments to the data-science\n problem and stripping workflow/process meta (§ \"Contextualize the\n comments\"); dropping the `ruff.toml` template into a fresh project.\n- **Out of scope:** type hints (mypy / pyright are not in the\n stack); naming conventions ruff doesn't enforce; setting up\n PostToolUse / PreToolUse hooks; linting non-Python files.\n\n## What to run, in what order\n\nFor every Python file touched this turn (call them ``), run\ninside the project's environment manager — `pixi run` for pixi\nprojects, equivalent for uv / poetry / conda (per\n`python-env-manager`):\n\n```bash\npixi run ruff format \npixi run ruff check --fix \npixi run ruff check \n```\n\nThree steps, in order:\n\n1. **`ruff format`** — applies the formatter (line length, quoting,\n trailing commas, blank lines around defs). Idempotent.\n2. **`ruff check --fix`** — auto-fixes everything ruff knows how to\n fix in place: import sorting (`I`), legacy syntax (`UP`),\n detectable bug patterns (`B`).\n3. **`ruff check`** (no `--fix`) — final pass. Anything reported\n here needs Claude's attention: missing docstrings (`D`),\n undefined names (`F`), code structure issues. Address them, then\n re-run the trio. Apply the **one-fix-per-file rule** from Stop\n conditions.\n\nIf a file under `experiments/` or `audit/`, or the `data/eda.py`\nEDA script, has a `D100` (\"missing module docstring\") or `D103`\n(\"missing function docstring\") warning, that's expected for `# %%`\ncells; the bundled `ruff.toml` per-file-ignores `D100` + `D103`\n(and `E402`, `B018`) for `experiments/**`, `audit/**`, and\n`data/eda.py`. If you're seeing them, the `ruff.toml` isn't loaded\n— check that it lives at the project root.\n\nAudit files (`audit/_.py`, owned by\n`audit-ml-pipeline`) and the EDA script (`data/eda.py`, owned by\n`explore-ml-data`) lint the same way as experiment files: same\n`# %%` cell convention, same per-file ignores, same NumPyDoc\nconvention for any helper functions. After writing or editing one of\nthese files, run the same trio (`ruff format` → `ruff check --fix`\n→ `ruff check`).\n\n## Contextualize the comments\n\nruff makes a file *well-formed*; this pass makes it *well-documented\nfor the problem*. Templates ship with neutral placeholders and a\nlittle authoring scaffolding so the generating skill knows what each\ncell / module is for. None of that should survive into the user's\ncommitted file — the user's files document the **data-science\nproblem**, not the process that produced them.\n\nAfter the ruff trio, for every touched file that now carries **real\ncontent**, do a quick documentation pass:\n\n1. **Fill the header for this problem.** Replace any ``\n or generic header with a one- or two-line description of what this\n file does *here*: the experiment's hypothesis (`experiment.py`),\n what this module contributes to the pipeline (`src//*.py`),\n which report this file reviews and what it tests\n (`audit/.py`), what the dataset is and what the analysis\n looks at (`data/eda.py`). Pull the wording from the live context —\n the project goal, the approved design note, the dataset.\n2. **Strip the workflow meta.** Delete leftover process commentary:\n skill names, gate IDs (`G-*`), `§` cross-references, \"the agent\",\n \"the (cell) runner\", \"the digest\", \"run cell by cell\", journal /\n backlog / sourcing jargon, and inline guard-rails like \"MUST NOT\n call `put` / bare expressions, don't `print`\". Those guard-rails\n stay enforced — they live in the owning skill's SKILL.md, which is\n where the agent reads them, not in the user's file.\n3. **Keep the substance.** Genuinely useful problem / engineering\n context and the numpydoc docstrings stay. Cell markers (`# %%`)\n and any remaining `<...>` placeholders the agent still has to fill\n stay until they are filled.\n\nThe result should read like a colleague wrote the file for this\nproject — not like a generated scaffold. Skip a file that is still an\nempty skeleton (e.g. a freshly scaffolded `src//*.py` with no\nbody yet): there is no context to write about until the content\nlands, and the contextualization happens on the edit that fills it.\n\nThis pass is **owned here** so the rule is enforced uniformly. Every\nfile-writing skill already hands off to this skill after a write;\nthat hand-off now also covers contextualizing the comments.\n\n## Numpydoc — the docstring convention\n\nPublic functions and classes carry numpydoc-format docstrings; ruff's\n`D` rules with `pydocstyle.convention = \"numpy\"` enforce the shape.\n\n**A bare one-line summary is NOT sufficient for public functions.**\nThe `Parameters` / `Returns` (and `Raises` when applicable) sections\nare mandatory — even when the function is small, even when the user\nsays \"just the summary is fine\". Approving a one-line docstring on\na public function silently fails the contract this skill enforces;\nthe function looks `D`-rule-clea","tagline":"Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Three responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, (2) run ruff against any Python files Claude has just generate","category":"research","tags":["agent-skill"],"author":"probabl-ai","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github fast track","sourceDetail":"probabl-ai/skills","creatorName":"probabl-ai","creatorUrl":"https://github.com/probabl-ai","sourceUrl":"https://github.com/probabl-ai/skills/tree/main/skills/python-code-style","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/probabl-ai-python-code-style#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":119,"forks":7,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":37.65},"quality":{"score":67,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"119","tone":"neutral"},{"label":"Freshness","value":"21d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"BSD-3-Clause","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":66,"base_score":74,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. 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Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["66/100 Trust Score v5","74/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":62,"weight":0.13,"status":"info","detail":"119 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":51,"weight":0.08,"status":"warn","detail":"119 stars, 7 forks; 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Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"python-code-style\" as a Claude Code skill from https://github.com/probabl-ai/skills/tree/main/skills/python-code-style. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Three responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, (2) run ruff against any Python files Claude has just generated or edited, and (3) contextualize each touched file's comments to the data-science problem — rewriting any leftover template / workflow prose (skill names, gates, runner, digest, guard-rails) into concise, problem-specific docs so the user's committed files read like a colleague wrote them, not like a generated scaffold. 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Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"python-code-style\" from https://github.com/probabl-ai/skills/tree/main/skills/python-code-style into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Three responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, (2) run ruff against any Python files Claude has just generated or edited, and (3) contextualize each touched file's comments to the data-science problem — rewriting any leftover template / workflow prose (skill names, gates, runner, digest, guard-rails) into concise, problem-specific docs so the user's committed files read like a colleague wrote them, not like a generated scaffold. 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issue activity unavailable in current metadata"]},"coverageTags":["Research","Research agents","agent-skill"]},"audit":{"audit_score":78,"risk_level":"needs_review","risk_label":"Needs review","quality_score":67,"trust_score":74,"maintenance_score":100,"security_score":78,"install_score":92,"warnings":["Dependency or permission surface needs review","Permission surface may require sandboxing","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 119 stars, 7 forks; issue activity unavailable in current metadata","Dependency/runtime risk: command execution surface, credential or environment access","Permission surface: secrets or environment access, shell or command execution"]},"quality_signals":{"model":"v2","star_score":14.55,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"content-automation","title":"Content automation","url":"https://www.openagentskill.com/use-cases/content-automation"},{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"},{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"}],"stacks":[{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"},{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"}],"install":"npx skills add probabl-ai/skills --skill python-code-style","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add probabl-ai-python-code-style","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"python-code-style\" agent skill from https://github.com/probabl-ai/skills/tree/main/skills/python-code-style. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Three responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, (2) run ruff against any Python files Claude has just generated or edited, and (3) contextualize each touched file's comments to the data-science problem — rewriting any leftover template / workflow prose (skill names, gates, runner, digest, guard-rails) into concise, problem-specific docs so the user's committed files read like a colleague wrote them, not like a generated scaffold. Stops at \"the touched files pass `ruff check` and document the problem, not the process.\" TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run AND the file's comments are contextualized to the problem; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and th After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"probabl-ai-python-code-style\",\"task\":\"Install python-code-style\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"python-code-style\" as a Claude Code skill from https://github.com/probabl-ai/skills/tree/main/skills/python-code-style. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Three responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, (2) run ruff against any Python files Claude has just generated or edited, and (3) contextualize each touched file's comments to the data-science problem — rewriting any leftover template / workflow prose (skill names, gates, runner, digest, guard-rails) into concise, problem-specific docs so the user's committed files read like a colleague wrote them, not like a generated scaffold. Stops at \"the touched files pass `ruff check` and document the problem, not the process.\" TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run AND the file's comments are contextualized to the problem; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and th After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"probabl-ai-python-code-style\",\"task\":\"Install python-code-style\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"python-code-style\" from https://github.com/probabl-ai/skills/tree/main/skills/python-code-style into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Three responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, (2) run ruff against any Python files Claude has just generated or edited, and (3) contextualize each touched file's comments to the data-science problem — rewriting any leftover template / workflow prose (skill names, gates, runner, digest, guard-rails) into concise, problem-specific docs so the user's committed files read like a colleague wrote them, not like a generated scaffold. Stops at \"the touched files pass `ruff check` and document the problem, not the process.\" TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run AND the file's comments are contextualized to the problem; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and th After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"probabl-ai-python-code-style\",\"task\":\"Install python-code-style\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/probabl-ai/skills/tree/main/skills/python-code-style","github_repo":"probabl-ai/skills","version":"1.0.0","license":"BSD-3-Clause","urls":{"web":"https://www.openagentskill.com/skills/probabl-ai-python-code-style","repository":"https://github.com/probabl-ai/skills/tree/main/skills/python-code-style","api":"/api/agent/skills/probabl-ai-python-code-style","install_api":"/api/skills/probabl-ai-python-code-style/install"},"meta":{"created_at":"2026-09-04T18:00:50.057729+00:00","updated_at":"2026-09-04T18:00:50.143937+00:00","agent_friendly":true}}