Opinionated, Pythonic way to **declare** the pipeline that goes from a data source to a predictor: data loading, preprocessing, feature engineering, estimator selection, and their composition. The pipeline is built as a **skrub DataOps graph**; every step is either a pure-Python function (stateless) attached via `.skb.apply_func`, or a scikit-learn-compatible estimator (stateful) attached via `.skb.apply`. Stops at the declared object. Out of scope: `fit` invocation, train/test split, hyperparameter tuning, persistence, evaluation. Deep-learning declarations are covered via internal `references/*.md`; skrub and scikit-learn mechanics live in sibling skills. TRIGGER when: writing or editing code that declares any link in the chain *data source → predictor* — data readers/loaders feeding a model (`read_csv`, `read_parquet`, `Dataset` classes), preprocessing or feature-engineering steps (transformers, encoders, imputers, scalers, text/image featurizers), **pure-Python data-processing functions destined for the p
Decide where files live in an ML experimentation project: where the reusable code goes, where each experiment goes, where reports are persisted. Owns the layout, the file-creation rules (one file per experiment, ask before editing an existing one), and the jupytext-style `# %%` script convention. Stops at "the file exists in the right place with the right skeleton". Never imposes a `data/` layout — the user owns that. TRIGGER when: starting a new ML project / scaffolding a workspace; about to create the first experiment file in a project; about to create `src/<pkg>/data.py`, `features.py`, `pipeline.py`, or `evaluate.py` for the first time; about to write a Jupyter notebook (`.ipynb`) for experimentation — redirect to a `# %%` script under `experiments/`; user asks where something should live, how to organize the project, or how to set up the workspace; about to add a *new experiment iteration* (must decide: new file vs. edit existing — ask the user). SKIP when: the file is clearly part of the package's exist
Single source of truth for "which Python environment manager does this project use, and how do I install a package with it?". Owns the detection table (pixi / uv / poetry / hatch / conda+mamba / pip+venv), the install / remove / upgrade commands per manager, and the bootstrap path when no manager is in place (default recommendation: pixi). Stops at "the install command was issued with the right manager and the package is importable". TRIGGER when (any of these): (1) **about to install / add / pin / upgrade / remove a Python package** — `pip install`, `pixi add`, `uv add`, `poetry add`, `conda install`, etc. — under any framing; (2) `data-science-python-stack` § "Missing dependency" surfaced a missing import and an install is the next step; (3) a workflow skill's Stop condition fired on a missing dependency (`build-ml-pipeline`, `evaluate-ml-pipeline`, `organize-ml-workspace`); (4) starting a new Python project and no manager is in place yet (bootstrap with pixi unless the user picks ot
Source the next ML experiment proposal by inspecting the skore report from the previous run — residuals, calibration, per-slice metrics, threshold behavior, statistical checks surfaced by skore. Hand the proposal back to `iterate-ml-experiment`, which writes it into `plan/NN_short_name.md` and seeks the user's approval. Stops at "a proposal (question, motivation, method outline) has been returned"; does not write any plan file itself, and does not author acceptance criteria — the user judges the result. TRIGGER when: `iterate-ml-experiment` is picking a sourcing strategy, the previous experiment has a skore report on disk, and the user says "the report shows X", "calibration looks bad", "why is slice Y so off?", "residuals look weird", or "what does the report tell us?"; the user is open-ended after a recent run — try this strategy first when a fresh report exists. SKIP when: the previous experiment hasn't run yet (no report to read); the user has a concrete modelling idea (use `iterate-from-user`); the issue
Source the next ML experiment proposal by searching the scientific / engineering literature (papers, blog posts, library docs) for techniques applicable to the current problem. Hand the proposal back to `iterate-ml-experiment`, which writes it into `plan/NN_short_name.md` and seeks the user's approval. Stops at "a proposal (question, motivation, method outline, source citations) has been returned"; does not write any plan file itself, and does not author acceptance criteria — the user judges the result. TRIGGER when: `iterate-ml-experiment` is picking a sourcing strategy and the user asks "any papers on this?", "what does the literature say?", "how do people usually handle X?", or "search for techniques for Y"; the user has run out of obvious next steps and is open-ended; a sibling strategy (methodology / diagnostic) has surfaced a problem with no obvious in-house fix and external prior art would help. SKIP when: the user has a concrete idea already (use `iterate-from-user`); the gap is clearly methodological
Source the next ML experiment proposal by auditing the *methodology* of the previous experiment(s) — split strategy, leakage risk, target encoding, sample size, metric choice, baseline comparability, randomness control. Hand the proposal back to `iterate-ml-experiment`, which writes it into `plan/NN_short_name.md` and seeks the user's approval. Stops at "a proposal (question, motivation, method outline) has been returned"; does not write any plan file itself, and does not author acceptance criteria — the user judges the result. TRIGGER when: `iterate-ml-experiment` is picking a sourcing strategy and the user says "did we get the split right?", "is this leaking?", "small sample size?", "is the baseline fair?", "is this metric the right one?"; the previous experiment's result looks suspicious (too good, too noisy, too flat) and the user wants to check the setup before iterating further; a literature / diagnostic strategy has surfaced something that turns out to be a methodology issue, not a modelling issue. SKI
Source the next ML experiment proposal from the user directly, from a GitHub issue tracker the user has pointed us at, or from a spec / notes / reference repo the user has pointed us at. Hand the proposal back to `iterate-ml-experiment`, which writes it into `plan/NN_short_name.md` and seeks the user's approval. Stops at "a proposal (question, motivation, method outline) has been returned"; does not write any plan file itself, and does not author acceptance criteria — the user judges the result. TRIGGER when: `iterate-ml-experiment` is picking a sourcing strategy and the user has offered a concrete idea ("I want to try X", "let's add Y", "tweak the encoder"); the user pastes or links a GitHub issue / discussion that describes the next experiment; the user says "use the issue tracker" or "check issue #N"; the user points us at another repo / folder / set of notes ("look at the spec at <path>", "see the notes in <repo-url>", "read what's in <dir>"). SKIP when: the user is open-ended ("what's next?") with no ide
Owns the iteration loop on top of an ML workspace: the `plan/PLAN.md` history log and the per-experiment `plan/NN_short_name.md` design notes that must be written **and validated by the user** before the matching `experiments/NN_short_name.py` is created. Drives the conversational loop where the next experiment is proposed, refined, and committed to plan, then dispatches to one of the `iterate-from-*` strategy skills when the source of the next proposal needs to be picked. Stops at "the plan file is on disk and the user has approved it". TRIGGER when: a session starts in an ML workspace that already has a `plan/` folder (read `PLAN.md` first to see where things stand); the user says "what's next", "resume", "where were we", "let's iterate", "propose the next experiment", or similar; about to create a new `experiments/NN_*.py` (the matching `plan/NN_*.md` must exist and be validated first); the user wants to record an outcome from a finished experiment in `PLAN.md`; the user asks to compare past experiments or