{"slug":"probabl-ai-data-science-python-stack","name":"data-science-python-stack","description":"Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, ","long_description":"---\nname: data-science-python-stack\ndescription: >\n  Opinionated Python stack for data-science / ML work — one library\n  per job, organized into tiers (mandatory / user choice / optional /\n  transitive). SKILL.md is the index; per-library\n  `references/<library>.md` files carry scope, \"pick this when\" /\n  \"pick something else when\", and pairings.\n\n  TRIGGER when (any of these):\n  (1) **a library import fails** in this stack's domain — the answer\n  is install, not substitute (see § \"Missing dependency\");\n  (2) **a library choice has to be made** — explicitly (the user asks\n  \"which library for X?\") or implicitly (code is about to introduce a\n  new dependency, or the project is being scaffolded and the tabular\n  library hasn't been picked yet);\n  (3) starting a new Python data-science / ML project;\n  (4) the user or current code reaches for a substitute outside the\n  stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or\n  reaches for `mlflow` to log params/metrics, or for `cross_val_score`\n  + handwritten reporting — redirect: tracking → `skore` Project API,\n  evaluation / reporting → `skore` report classes, `mlflow` stays\n  only for model serving / registry.\n\n  SKIP when: the project is non-Python; the work is web / backend /\n  infra unrelated to data science; the library is already chosen and\n  installed and the task is implementation inside it (bug fix, feature\n  work, refactor) with no new dependency in play.\n\n  HOW TO USE: **read this SKILL.md end-to-end before recommending or\n  installing anything** — picking from a single index entry hides the\n  tier (whether the library is mandatory, a user-choice, optional, or\n  already transitively present) and the pairings, and both matter.\n  Then read the linked `references/<library>.md` for the chosen\n  library's scope and tradeoffs. Don't silently substitute one library\n  for another; if no entry fits, surface the gap to the user.\n---\n\n# Data Science Python Stack\n\nOpinionated stack — one library per job, organized into four tiers\nplus an orthogonal **agent feature**:\n\n1. **Mandatory** — installed at project start, no exceptions.\n2. **User choice (competing-library jobs)** — multiple valid libraries\n   for the same job; the user picks via `AskUserQuestion` before any\n   import lands.\n3. **Optional** — install only when the project's task requires it.\n4. **Transitive** — already pulled in by the mandatory tier; do not\n   install explicitly, but know they're available.\n5. **Agent feature (orthogonal)** — deps that the *agent* uses\n   to audit a workspace and to power the editor LSP integration\n   (`ipython`, `pyright`), kept out of the production-shape\n   runtime via a manager-specific scope. Install logistics owned\n   by `python-env-manager` § \"Agent feature\"; consumed by\n   `audit-ml-pipeline` and the opencode LSP integration.\n\n## Stop conditions — read before naming any library\n\n- **No silent pick on a competing-library job.** Whenever the stack\n  offers two or more libraries for the same job (see § \"Competing\n  libraries — general rule\" and the Tier 2 table), the user picks\n  via `AskUserQuestion` before any `Write` that imports the library\n  and before any install command runs. \"Already pulled in\n  transitively\" / \"user said 'quick'\" / \"the folder has no\n  preference signalled\" are **not** waivers. A silent pick is a\n  Stop-condition violation, full stop.\n- **No substitute when import fails.** When code in this stack needs\n  a library but `import` fails, install it; do not rewrite to a\n  non-stack equivalent (see § \"Missing dependency\"). The most\n  common silent-rewrite path —\n  `import skrub` fails → rewrite as `sklearn.Pipeline`,\n  `import skore` fails → rewrite as `cross_val_score` —\n  silently undoes the workflow skills' contract.\n- **Harness-level \"no clarifying questions\" hints do NOT waive the\n  competing-library `AskUserQuestion`.** The Tier 2 pick is an\n  operating-contract gate, not a clarifying question. The same\n  applies to user urgency phrasing: \"quick baseline\", \"just do it\",\n  \"go fast\", \"you pick\", \"whatever\" do NOT resolve a competing-\n  library gate. See § \"Free-text resolution\" in the general rule\n  below for what *does* resolve a gate.\n- **Post-hoc audit — required before ending the turn.** Before\n  declaring the turn complete, verify each competing-library job\n  invoked in this turn has either (a) an `AskUserQuestion` answer\n  recorded this session, or (b) a matching row in\n  `journal/JOURNAL.md` Status `Workspace decisions`. If any\n  competing-library job ran without one of those, surface the\n  non-compliance to the user explicitly as part of your final\n  message — do not hide it.\n\n## Forbidden shortcuts (observed in real traces)\n\n| Shortcut | Why it feels right | Why it's wrong |\n|----------|--------------------|----------------|\n| `pandas` is already pulled in by `skore` → skip the Tier 2 ask | \"Free\" library, no install needed | Tier 2 is a *project-shape* decision (every `data.py` signature, every fixture); transitive presence is not a pick |\n| User said \"quick baseline\" → assume `pandas` | Task urgency reads as permission | Urgency phrasing never waives a competing-library gate (Stop conditions above) |\n| Folder has no existing tabular code → infer pandas | \"No preference signalled\" | Inference is a silent pick; the gate requires a structured ask or a recorded `JOURNAL.md` decision |\n| One competing option requires a new `pixi add` → pick the \"free\" one | Avoids an install step | Install cost is not the criterion; project fit is |\n| User picked `pytorch` last project → reuse without asking | Continuity is friendly | Each workspace records its own `Workspace decisions`; cross-project memory is forbidden |\n\n## Competing libraries — general rule\n\nThis is the meta-rule that governs every \"user choice\" entry in\nthis skill. It applies to the Tier 2 table below and to any new\ncompeting-library job added in the future. It also applies inside\nTier 3 when two optional libraries cover the same job (e.g.\n`pytorch` vs `keras` as the deep-learning framework).\n\n### The rule\n\nWhenever the stack offers two or more libraries for the same job:\n\n1. **`AskUserQuestion` before any import or install.** Use the\n   options listed for the job in the competing-jobs table; do not\n   editorialize the option labels.\n2. **Persist the answer in `journal/JOURNAL.md` Status under\n   `Workspace decisions`.** This block is immutable until the user\n   explicitly pivots. On future sessions, **read Status first**;\n   do not re-ask a recorded decision. The persistence contract\n   lives in `iterate-ml-experiment`'s `JOURNAL.md` template — the\n   `Workspace decisions` block is the source of truth for cross-\n   session continuity.\n3. **No silent default.** Even when one option is \"free\"\n   (already pulled in transitively) and the other costs an\n   install, never pick silently. The free option becoming the\n   pick is fine; *the picking happens via `AskUserQuestion`*.\n\n### Free-text resolution\n\nA user message resolves a competing-library gate **only** if it\nnames one of the listed options for the job. Apply in priority\norder:\n\n- **Exact match** (case-insensitive, whitespace-trimmed) to an\n  option label: resolves the gate. (\"use polars\", \"let's go\n  with pytorch\", \"pandas please\" → resolved.)\n- **Library named in a free-text intent** (\"rewrite the loader\n  in polars\", \"I want a keras model\"): resolves the gate for\n  that job.\n- **No library named** (\"make it fast\", \"you pick\", \"whatever\",\n  \"no preference\", \"quick baseline\"): does **NOT** resolve.\n  Fall through to the structured `AskUserQuestion`.\n- **\"You pick\" / \"no preference\" specifically** — surface the\n  **default-on-no-preference** for the job (from the Tier 2\n  table) and ask for confirmation. Never silently pick; never\n  skip the confirmation step.\n\n### Adding a new contested job\n\nWhen a new job appears in the stack with two viable libraries,\nadd a row to the Tier 2 competing-jobs table. **Every row must\nname an explicit `Default-on-no-preference`** — rows without one\nare forbidden, because they re-create the silent-pick loophole\nthis rule exists to close. If a sensible default cannot be\nnamed, the job does not belong in the table; surface the gap to\nthe user and pick per-project via a free-form `AskUserQuestion`.\n\n## When to invoke this skill\n\nTwo events trigger this skill before any other action:\n\n1. **A library import fails** in the stack's domain. The answer is\n   install (see § \"Missing dependency\" below), never substitute.\n2. **A library choice has to be made** — for tabular data at project\n   start, or any time code is about to introduce a new dependency\n   (deep learning, model serving, notebooks, …).\n\nIn both cases, **read the whole SKILL.md before deciding**. The tier\nstructure below determines whether a library should already be\npresent, needs a user prompt, or is opt-in — that decision can't be\nmade from a single index entry.\n\n## Missing dependency — install, do not substitute\n\nWhen code in this stack needs a library but `import` fails, the answer\nis **install it**, not substitute. Specifically:\n\n- Surface the missing dependency to the user with the exact install\n  command. **Invoke `python-env-manager` to detect the project's\n  environment manager (pixi / uv / poetry / hatch / conda / pip+venv)\n  and produce the right install command** — don't infer the manager\n  from memory; the project may not use the default. **Stop and wait\n  for confirmation before doing anything else.**\n- Do **not** rewrite the code to use a non-stack equivalent\n  (`sklearn.Pipeline` for `skrub`, `cross_val_score` + handwritten\n  metric prints for `skore`. Substitution silently breaks the contract\n  that the workflow skills (`build-ml-pipeline`,\n  `evaluate-ml-pipeline`, `organize-ml-workspace`) rely on.\n- This rule **overrides** \"make the code run\". If the user prefers a\n  substitute, they will say so — until they do, install. Reaching\n  for a substitute because the dependency is missing is the most\n  common way the stack gets silently undone, so treat the missing\n  import as a hard stop.\n\n## How to use this skill\n\n1. Read this whole SKILL.md before picking — the tier structure\n   determines whether the library should already be installed, needs\n   a user-choice prompt, or is opt-in.\n2. Match the task to an entry in the right tier.\n3. Read the linked `references/<library>.md` for the chosen library's\n   scope and tradeoffs before introducing it.\n4. Install via `pixi` by default. If the project already uses a\n   different manager (pip+venv, uv, conda), follow that instead.\n5. Don't substitute libraries silently. If no entry fits the task,\n   surface the tradeoff to the user.\n\n## Tier 1 — Mandatory (install at project start)\n\nThese five libraries are always installed in a data-science / ML\nproject. The first three co-own the modeling workflow:\nscikit-learn provides the estimators, skrub provides the\ndata-cleaning + DataOps layer that sits before them, skore\nevaluates the result and persists it as a project on disk. The\nfourth, `ruff`, owns lint + format and is non-negotiable: every\nproject Claude touches should pass `ruff check`. The fifth,\n`pytest`, runs the smoke test that every approved experiment\nmust have per the `test-ml-pipeline` / `smoke-test-ml-pipeline`\ncontract — without pytest the smoke-test gate can't enforce\npredict-time correctness, so pytest stays mandatory even when\nno other tests have been written yet. Each is named explicitly\neven when transitively present, because the workflow skills\n(`build-ml-pipeline`, `evaluate-ml-pipeline`,\n`python-code-style`, `test-ml-pipeline`) depend on them\ndirectly and should not silently lose them if upstream packaging\nchanges.\n\n- [`scikit-learn`](references/scikit-learn.md) — tabular ML\n  algorithms, preprocessing, model-selection helpers. Use\n  `HistGradientBoosting{Classifier,Regressor}` instead of pulling in\n  xgboost or lightgbm. **Evaluation, cross-validation reports, and\n  model comparison are owned by `skore`** — don't inline\n","tagline":"Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pa","category":"data-analysis","tags":["agent-skill"],"author":"probabl-ai","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github candidate review","sourceDetail":"probabl-ai/skills","creatorName":"probabl-ai","creatorUrl":"https://github.com/probabl-ai","sourceUrl":"https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/probabl-ai-data-science-python-stack#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. 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issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"21d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"BSD-3-Clause"},{"id":"documentation","label":"README/SKILL.md completeness","score":86,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":64,"weight":0.12,"status":"info","detail":"credential or environment access, network or browser surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add probabl-ai/skills --skill data-science-python-stack"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":22,"weight":0.07,"status":"fail","detail":"secrets or environment access, shell or command execution"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack"},{"id":"review_status","label":"Review status","score":88,"weight":0.05,"status":"pass","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"info","label":"GitHub adoption","detail":"119 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"119 stars, 7 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"21d since push"},{"status":"pass","label":"License clarity","detail":"BSD-3-Clause"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"info","label":"Dependency/runtime risk","detail":"credential or environment access, network or browser surface"},{"status":"pass","label":"Install availability","detail":"npx skills add probabl-ai/skills --skill data-science-python-stack"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"fail","label":"Permission surface","detail":"secrets or environment access, shell or command execution"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack"},{"status":"pass","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"pass","label":"OpenAgentSkill usage","detail":"1 views, 0 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern"],"warnings":["Financial research output is not financial advice; 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require human review before any live investment decision.","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","Permission surface: secrets or environment access, shell or command execution"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"human_review_before_install","reason":"Human review or sandbox validation is required before automatic installation."},"bestFor":["data-analysis","agent-skill"],"doNotUseFor":["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","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"knownRisks":["Financial research output is not financial advice; require human review before any live investment decision.","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","Permission surface: secrets or environment access, shell or command execution"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"outcome_stats":null,"safety":{"score":35,"level":"avoid_auto_install","label":"Avoid automatic install","safety_tier":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","summary":"This skill should not be selected by an agent without explicit human security review.","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","auto_install_policy":"block","reasons":["Metadata combines secrets access with shell or command execution","High-risk permission hints: Shell or command execution, Secrets or environment access"]},"auto_install_allowed":false,"human_review_required":true,"blocked":true,"audit_risk":"needs_review","permission_hints":[{"id":"shell","label":"Shell or command execution","reason":"Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.","severity":"high"},{"id":"browser","label":"Browser automation","reason":"Skill may drive a browser or interact with web pages.","severity":"medium"},{"id":"network","label":"Network access","reason":"Skill likely fetches remote pages, APIs, repositories, or external services.","severity":"medium"},{"id":"filesystem","label":"Filesystem access","reason":"Skill may read or write project files, documents, generated artifacts, or local workspace state.","severity":"medium"},{"id":"secrets","label":"Secrets or environment access","reason":"Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.","severity":"high"}],"policy_warnings":["High-risk permission hints: Shell or command execution, Secrets or environment access","Permission surface may require sandboxing"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","auto_install_policy":"block","auto_install_allowed":false,"blocked":true,"human_review_required":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","reasons":["Metadata combines secrets access with shell or command execution","High-risk permission hints: Shell or command execution, Secrets or environment access"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"failed","score":68,"risk_level":"high","decision":{"recommendation":"do_not_auto_install","reason":"Agent safety gate: This skill should not be selected by an agent without explicit human security review.","auto_install_allowed":false,"policy":"block","human_review_required":true},"blockers":["Agent safety gate: This skill should not be selected by an agent without explicit human security review.","Permission surface: secrets or environment access, shell or command execution"],"warnings":["Trust score: Good trust signals with a few areas worth checking before rollout.","Audit score: Needs review","High-risk permission hints: Shell or command execution, Secrets or environment access","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","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","Permission surface: secrets or environment access, shell or command execution"],"validation_plan":["Inspect repository, README/SKILL.md, license, and recent commits before production use.","Install in an isolated workspace or sandbox with no production secrets available.","Run the smallest representative task and record files touched, commands run, network access, and outputs.","Compare the selected skill against at least one alternative when the eval status is review or failed.","Promote only after the agent reports a successful verification result and unresolved warnings are accepted."],"checks":[{"id":"task_fit","label":"Task fit","status":"pass","score":94,"required_for_auto_install":true,"detail":"Task wording matches this skill metadata.","evidence":["Evaluate data-science-python-stack before installing it in an agent workflow","data-analysis","Workflow automation workflows; Claude Code teams; builders willing to evaluate younger projects"]},{"id":"install_path","label":"Install path","status":"pass","score":92,"required_for_auto_install":true,"detail":"Install handoff is available.","evidence":["npx skills add probabl-ai/skills --skill data-science-python-stack"]},{"id":"install_safety","label":"Install command safety","status":"pass","score":92,"required_for_auto_install":true,"detail":"standard package or runtime install path","evidence":["npx skills add probabl-ai/skills --skill data-science-python-stack"]},{"id":"trust_score","label":"Trust score","status":"warn","score":74,"required_for_auto_install":true,"detail":"Good trust signals with a few areas worth checking before rollout.","evidence":["Strong shortlist","119 GitHub stars","BSD-3-Clause"]},{"id":"audit_score","label":"Audit score","status":"warn","score":79,"required_for_auto_install":true,"detail":"Needs review","evidence":["Permission surface may require sandboxing"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"fail","score":35,"required_for_auto_install":true,"detail":"This skill should not be selected by an agent without explicit human security review.","evidence":["Do not auto-install. 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SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, ","category":"data-analysis","url":"https://www.openagentskill.com/skills/probabl-ai-data-science-python-stack","repository":"https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack","github_repo":"probabl-ai/skills"},"suited_tasks":["Workflow automation workflows","Claude Code teams","builders willing to evaluate younger projects","Move data between tools","Transform files","Trigger repeatable actions","Navigate pages","Click and type safely"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add probabl-ai/skills --skill data-science-python-stack","ready":true,"targets":[{"id":"openagentskill-cli","label":"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-data-science-python-stack"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"data-science-python-stack\" agent skill from https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack. 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: Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"probabl-ai-data-science-python-stack\",\"task\":\"Install data-science-python-stack\",\"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."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"data-science-python-stack\" as a Claude Code skill from https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack. 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: Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"probabl-ai-data-science-python-stack\",\"task\":\"Install data-science-python-stack\",\"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."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"data-science-python-stack\" from https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack 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: Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"probabl-ai-data-science-python-stack\",\"task\":\"Install data-science-python-stack\",\"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."}],"handoff_url":"https://www.openagentskill.com/api/skills/probabl-ai-data-science-python-stack/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/probabl-ai-data-science-python-stack"},"trust":{"score":74,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"119 GitHub stars","repoActivity":"119 stars, 7 forks","lastPushed":"21d since push","license":"BSD-3-Clause","repository":"https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack","install":"npx skills add probabl-ai/skills --skill data-science-python-stack","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["data-analysis","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","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","Permission surface: secrets or environment access, shell or command execution"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. 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TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"probabl-ai-data-science-python-stack\",\"task\":\"Install data-science-python-stack\",\"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."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"data-science-python-stack\" as a Claude Code skill from https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack. 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: Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"probabl-ai-data-science-python-stack\",\"task\":\"Install data-science-python-stack\",\"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."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"data-science-python-stack\" from https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack 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: Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. 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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."}],"handoff_url":"https://www.openagentskill.com/api/skills/probabl-ai-data-science-python-stack/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/probabl-ai-data-science-python-stack"},"trust":{"score":74,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"119 GitHub stars","repoActivity":"119 stars, 7 forks","lastPushed":"21d since push","license":"BSD-3-Clause","repository":"https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack","install":"npx skills add probabl-ai/skills --skill data-science-python-stack","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["data-analysis","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","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","Permission surface: secrets or environment access, shell or command execution"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. 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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: Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"probabl-ai-data-science-python-stack\",\"task\":\"Install data-science-python-stack\",\"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 \"data-science-python-stack\" as a Claude Code skill from https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack. 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: Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"probabl-ai-data-science-python-stack\",\"task\":\"Install data-science-python-stack\",\"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 \"data-science-python-stack\" from https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack 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: Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library `references/<library>.md` files carry scope, \"pick this when\" / \"pick something else when\", and pairings. TRIGGER when (any of these): (1) **a library import fails** in this stack's domain — the answer is install, not substitute (see § \"Missing dependency\"); (2) **a library choice has to be made** — explicitly (the user asks \"which library for X?\") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for `mlflow` to log params/metrics, or for `cross_val_score` + handwritten reporting — redirect: tracking → `skore` Project API, After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"probabl-ai-data-science-python-stack\",\"task\":\"Install data-science-python-stack\",\"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/data-science-python-stack","github_repo":"probabl-ai/skills","version":"1.0.0","license":"BSD-3-Clause","urls":{"web":"https://www.openagentskill.com/skills/probabl-ai-data-science-python-stack","repository":"https://github.com/probabl-ai/skills/tree/main/skills/data-science-python-stack","api":"/api/agent/skills/probabl-ai-data-science-python-stack","install_api":"/api/skills/probabl-ai-data-science-python-stack/install"},"meta":{"created_at":"2026-09-06T22:26:58.623186+00:00","updated_at":"2026-09-06T22:26:58.681402+00:00","agent_friendly":true}}