{"slug":"probabl-ai-explore-ml-data","name":"explore-ml-data","description":"Owns data understanding BEFORE any model is designed. Places and executes `data/eda.py` (a jupytext `# %%` script) via the shared in-process runner, reads the streamed digest, then writes a persisted `data/eda.md` report (plus linked `data/eda_<table>.html` skrub `TableReport` pages) and the `## Data understanding (EDA)` section of `journal/JOURNAL.md`. The point is to surface the dataset facts — shape, dtypes, missingness, cardinality, target balance / skew, datetime / group structure, feature associations — that JUSTIFY the later learner / splitter / metric decisions, so the user understands *why* the modelling choices are made. Uses `skrub.TableReport` for dataframe overviews and the shared runner `audit-ml-pipeline/scripts/run_cells.py`. Stops at \"EDA executed, `data/eda.md` + HTML written, JOURNAL EDA section updated.\" Never designs the model, never edits `src/<pkg>/`, never modifies the user's raw data files. TRIGGER — any of: - `iterate-ml-experiment` § 0 bootstrap, BEFORE the b","long_description":"---\nname: explore-ml-data\ndescription: >\n  Owns data understanding BEFORE any model is designed. Places and\n  executes `data/eda.py` (a jupytext `# %%` script) via the shared\n  in-process runner, reads the streamed digest, then writes a\n  persisted `data/eda.md` report (plus linked `data/eda_<table>.html`\n  skrub `TableReport` pages) and the `## Data understanding (EDA)`\n  section of `journal/JOURNAL.md`. The point is to surface the\n  dataset facts — shape, dtypes, missingness, cardinality, target\n  balance / skew, datetime / group structure, feature associations —\n  that JUSTIFY the later learner / splitter / metric decisions, so the\n  user understands *why* the modelling choices are made. Uses\n  `skrub.TableReport` for dataframe overviews and the shared runner\n  `audit-ml-pipeline/scripts/run_cells.py`. Stops at \"EDA executed,\n  `data/eda.md` + HTML written, JOURNAL EDA section updated.\" Never\n  designs the model, never edits `src/<pkg>/`, never modifies the\n  user's raw data files.\n\n  TRIGGER — any of:\n  - `iterate-ml-experiment` § 0 bootstrap, BEFORE the baseline design\n    note — the G-EDA gate fires here (run / skip).\n  - The user asks to \"explore the data\", \"do an EDA\", \"profile the\n    dataset\", \"what does the data look like\", \"understand the data\".\n  - A new or changed data source needs (re-)understanding before the\n    next experiment.\n\n  SKIP when: the workspace isn't scaffolded / bootstrapped yet —\n  `iterate-ml-experiment` § 0 owns bootstrap ordering and will\n  dispatch here at the G-EDA step; don't run standalone ahead of\n  scaffolding (route to `iterate-ml-experiment` / `organize-ml-\n  workspace`); there is no data to explore yet; the user wants to\n  inspect a finished run's skore report rather than the raw dataset\n  (`audit-ml-pipeline`); the user is past data understanding and wants\n  pipeline / evaluation mechanics (`build-ml-pipeline` /\n  `evaluate-ml-pipeline`); a pure symbol lookup (`python-api`); EDA is\n  already recorded (`data/eda.md` + the JOURNAL EDA section exist) and\n  the user is not asking to refresh it.\n\n  HOW TO USE: run the Detection step (does `data/eda.md` + the JOURNAL\n  EDA section already exist?), emit the Pre-flight checklist as\n  visible text, read the Stop conditions, then place `data/eda.py`\n  from `templates/eda.py`, execute it via the shared runner, read the\n  digest, and author `data/eda.md` + the JOURNAL EDA section. Always\n  resolve skrub / pandas / polars symbols via `python-api`, never from\n  memory.\n---\n\n# Explore ML Data\n\nUnderstand the dataset before designing a model. One project-level\nEDA per workspace: an executable `data/eda.py`, a persisted\n`data/eda.md` narrative, rich `data/eda_<table>.html` reports, and a\nshort JOURNAL section that links them. The findings feed the baseline\ndesign note's learner / splitter / metric choices.\n\n## Next-step pointers — where you go after this skill\n\n| You came here for… | → next |\n|---|---|\n| Bootstrap, before the first baseline | → back to `iterate-ml-experiment` § 0; the EDA findings inform the auto-drafted `01_baseline.md` |\n| User free-text (\"explore the data\") | → surface the findings; no further dispatch unless the user asks to model |\n| Re-understand a changed data source | → re-run, overwrite `data/eda.*`, refresh the JOURNAL EDA section |\n\nAlways re-emit the Pre-flight checklist with evidence before\ndeclaring the turn done.\n\n## Where this sits in the loop\n\nEDA is a **bootstrap-time gate (G-EDA)** owned by this skill and\nfired by `iterate-ml-experiment` § 0 **before** the baseline design\nnote. Ordering matters: the dataset facts (class balance, datetime /\ngroup columns, missingness, cardinality) are exactly what justifies\nthe splitter (`G-CV-SPLITTER`), the metric default, and the learner\ndefault. Running EDA after the model is designed defeats the purpose.\n\n```\nscaffold → JOURNAL → goal from data/README.md\n   │\n   └─► G-EDA (run | skip)  ◄── this skill\n         │ run\n         └─► data/eda.py → execute → data/eda.md + HTML + JOURNAL §EDA\n   │\n   └─► auto-draft 01_baseline.md  (cites the EDA findings)\n```\n\n## Where things live — visual map\n\nTwo locations are kept separate: the **raw data source** (read-only,\nmay live anywhere) and the **EDA deliverables** (always under\n`<project>/data/`).\n\n| Path | Durability | Who writes it | What it holds |\n|---|---|---|---|\n| raw data source (`data/`, `raw/`, an absolute path, external) | user-owned, **READ-ONLY** | the user | The dataset. EDA reads it; never modifies it. May be anywhere — not assumed to be `data/` |\n| `data/eda.py` | **Durable** (committed) | This skill, once per workspace | The jupytext `# %%` EDA cells. Source of truth. Openable as a notebook for the rich view |\n| `data/eda.md` | **Durable** (committed) | This skill (authored from the digest) | The prose narrative: findings + **modelling implications** that the baseline note cites |\n| `data/eda_<table>.html` | **Durable** (committed) | `data/eda.py` via `TableReport.write_html(...)` | The rich, interactive skrub report per table — for the human |\n| `scratch/eda/eda.md` | Ephemeral (gitignored), optional | `run_cells.py` when given a 2nd arg | Per-cell digest the agent reads. Same content as stdout |\n| `journal/JOURNAL.md` § Data understanding (EDA) | **Durable** (committed) | This skill | 2–4 line summary + link to `data/eda.md` |\n\n**Mnemonic:** the raw data is *read-only and lives wherever the user\nkeeps it*; `data/eda.py` is *source*; `data/eda.md` + the HTML are the\n*durable deliverables, always under `data/`*; `scratch/eda/` and\nstdout are the *ephemeral run digest*.\n\n## Read-only-against-raw-data contract\n\nThe central rule. Surfaced as the first Stop condition below.\n\n**Allowed — this skill writes ONLY (deliverables always under\n`<project>/data/`, created if absent):**\n\n- `data/eda.py` — the EDA script (created / overwritten in place).\n- `data/eda.md` — the authored narrative.\n- `data/eda_<table>.html` — the skrub `TableReport` pages.\n- `scratch/eda/` — the ephemeral digest.\n- `journal/JOURNAL.md` § Data understanding (EDA).\n\n**Forbidden:**\n\n- Modifying, deleting, renaming, re-encoding, or \"cleaning\" the\n  user's raw data files — **wherever they live** (`data/`, another\n  folder, an absolute/external path). EDA **reads** them; it never\n  rewrites them. Data cleaning is the pipeline's job\n  (`build-ml-pipeline`), declared at fit time, not a one-off mutation.\n- Writing anywhere outside the five paths above — no `src/<pkg>/`\n  edits, no `reports/` writes, no new experiment files.\n- Designing the model: no `skore.evaluate(...)`, no `project.put(...)`,\n  no learner selection here. EDA *informs* those; it does not make\n  them.\n\n## Stop conditions — read before anything else\n\n- **Read-only against the user's raw data.** See § Read-only-\n  against-raw-data contract. `data/eda.py` reads the raw files\n  (wherever they live) and writes only the `data/eda.*` deliverables.\n- **Deliverables always under `<project>/data/`; the raw source is\n  separate.** Write `data/eda.py` / `data/eda.md` /\n  `data/eda_<table>.html` under `<project>/data/` (create the folder\n  if absent). The raw data the script *reads* may live anywhere\n  (`data/`, another in-repo folder, an absolute or external path) —\n  decouple the two: a `RAW = <LOAD_RAW_DATA>` source vs an `EDA_DIR`\n  output. Never assume the raw data is in `data/`.\n- **EDA precedes model design (G-EDA).** In bootstrap, the gate fires\n  **before** `journal/01_baseline.md` is drafted. It is binary:\n  **run** (place + execute `data/eda.py`, write the deliverables) or\n  **skip** (record `Status: skipped — <date>` in the JOURNAL section\n  and proceed). Do not silently bypass — fire the `AskUserQuestion`.\n  Free-text \"go fast\" / \"quick baseline\" does NOT resolve it.\n- **Agent feature required to execute.** The cell runner needs\n  `ipython`. If it is missing and the user chose **run**, STOP and\n  delegate to `python-env-manager` § \"Agent feature\"\n  (`G-AGENT-FEATURE`). Do NOT type `pixi add ... ipython` yourself;\n  do NOT fabricate EDA output with hand-written `print()`s. If the\n  user declines the agent feature, **fall back to the skip path**\n  (record `Status: skipped`) — never loop between run and install.\n- **Symbol from memory is forbidden.** Any `skrub` / `pandas` /\n  `polars` symbol (`TableReport`, `TableReport.json`, `write_html`,\n  `column_associations`, the tabular reader, …) must come from\n  `python-api` *this turn*. Cache hits under\n  `scratch/api/<lib>/<version>/` count; inline memory does not.\n  **`TableReport.json()`'s key names are not formally documented and\n  drift across skrub versions — confirm them via `python-api` and\n  parse defensively (`.get(...)`).**\n- **Library-agnostic — read facts off skrub, not pandas/polars.** The\n  workspace may use pandas OR polars (G-TABULAR), whose summary\n  methods differ (`select_dtypes` doesn't even exist in polars). The\n  structured facts come from `skrub` (`TableReport(...).json()`,\n  `column_associations`), which accept both. The ONLY library-\n  specific line is `RAW = <LOAD_RAW_DATA>`. Do not write\n  `df.isna()`/`df.nunique()`/`df.select_dtypes(...)` etc.\n- **`skrub.TableReport` for dataframe overviews.** Every table gets a\n  `TableReport(RAW, title=..., verbose=0)` written to\n  `data/eda_<table>.html` (the user-facing artifact) AND read via\n  `.json()` for the digest. `verbose=0` keeps progress prints out of\n  the digest.\n- **Never end a cell on a bare `TableReport`.** Outside a notebook,\n  `repr(TableReport(df))` is the useless `<TableReport: use .open()\n  to display>`. Use `report.write_html(...)` (a statement) for the\n  HTML, and end cells on **text-friendly** expressions (`RAW.shape`,\n  a `dict`/`list` built from `report.json()`,\n  `skrub.column_associations(RAW)`) so the digest carries real\n  values. Mirrors audit's `.frame()` rule.\n- **Never gitignore the whole `data/`; ask about the inputs.** The\n  deliverables live in `data/` and must stay committable, so the\n  whole `data/` folder must never be in `.gitignore`. If the raw\n  inputs should be kept out of git (large / local-only), fire an\n  `AskUserQuestion` offering to ignore **specific input patterns**\n  (e.g. `data/raw/`, `data/*.parquet`) — default: don't. Then verify\n  the deliverables are tracked (`git check-ignore data/eda.md` must\n  return nothing). Never auto-edit `.gitignore` — that is\n  `organize-ml-workspace`'s to write; surface the patch and ask.\n- **One project-level EDA.** A single `data/eda.py` covers the whole\n  dataset; multi-table data gets one `TableReport` cell per table\n  inside that one file (run the target/structure cells on the\n  target-bearing table). No `eda_v2.py`, no per-experiment EDA files,\n  not part of the four-way stem pairing. Re-understanding overwrites\n  `data/eda.py` in place.\n- **Don't design the model here.** No splitter pick, no metric pick,\n  no learner pick. Record *implications* in `data/eda.md`; the picks\n  happen in their owning gates (`G-CV-SPLITTER`, the baseline note).\n- **Harness \"no clarifying questions\" hints do NOT waive G-EDA or\n  G-AGENT-FEATURE.** Both fire regardless.\n- **Post-hoc audit — required before ending the turn.** Walk every\n  pre-flight row; surface unfilled Evidence cells explicitly.\n\n## Forbidden shortcuts\n\n| Shortcut | Why it's wrong |\n|---|---|\n| Design the baseline first, EDA \"later if there's time\" | Inverts G-EDA. The point is to justify the modelling choices *before* making them. EDA runs first in bootstrap |\n| End a cell on a bare `TableReport(df)` to \"show the report\" | Outside a notebook that repr is `<TableReport: use .open() to display>` — zero signal in the digest. Use `write_html(...)` + a text summary built from `report.json()` |\n| `print(...)` instead of a bare summary expression | The runner captures bare last-expressions via `result.result`; `print(...)` lands in stdout and is harder to scan. Use bare expressions |\n| Use pandas/polars methods (`df.isna()`, `df.nunique()`, `df.select_dtypes(...)`) for the summaries | Breaks on the other library (","tagline":"Owns data understanding BEFORE any model is designed. Places and executes `data/eda.py` (a jupytext `# %%` script) via the shared in-process runner, reads the streamed digest, then writes a persisted `data/eda.md` report (plus linked `data/eda_<table>.html` skrub `TableReport` pa","category":"security","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/explore-ml-data","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/probabl-ai-explore-ml-data#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":38.1},"quality":{"score":68,"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":"30d 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":68,"base_score":76,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. 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":["68/100 Trust Score v5","76/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is missing","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; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"30d 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 explore-ml-data"},{"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":46,"weight":0.07,"status":"warn","detail":"secrets or environment access, filesystem or document access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/probabl-ai/skills/tree/main/skills/explore-ml-data"},{"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":"30d 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 explore-ml-data"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"warn","label":"Permission surface","detail":"secrets or environment access, filesystem or document access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/probabl-ai/skills/tree/main/skills/explore-ml-data"},{"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":"3 views, 0 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["Legacy review approval recorded","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern","Outcome loop is ready but needs first real agent run"],"warnings":["Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Stars/forks activity: 119 stars, 7 forks; 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Review the current source before installing.","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":false,"command":null,"policy":"human_review_before_install","label":"Human review before install","notes":["The tracked source changed or could not be synchronized. 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