Setup Workspace

Set Up Workspace

Decide where artifacts live. Do not design an experiment here.

Human-facing prose

Details: references/human_facing_prose.md. Scaffolded design notes, JOURNAL, and folder READMEs describe this workspace's analysis — not the skills framework, the CLI, or the command that produced an output. Questions and replies use the same data-science language (import name, notebooks, documentation site) — not G-* names, catalog skill ids, or the wrapper CLI. Say "workspace setup", not setup-workspace. <!-- results-embed: … --> is a site marker. Authoring hints stay in this skill. style is ruff only.

Detection

• src/ or journal/ present → existing. Reuse names and folders. Do not scaffold again.
• Manager manifest without src/<pkg>/ → manager-only. Still scaffold; keep existing pyproject.toml. No --force.
• Otherwise → fresh. Ask G-PKG-NAME, then scaffold.

Pre-flight

Tick, then immediately run the matching sequence step. Do not stop after listing the boxes.

- [ ] Layout: fresh | manager-only | existing
- [ ] G-PKG-NAME: ask if fresh/manager-only (unless policy.package or src/<pkg>/ already names it)
- [ ] scaffold --package <pkg> | existing: no scaffold, no invent
- [ ] fresh/manager-only: persist notebooks + site true; install
- [ ] dispatched → return | standalone → git end-turn --stage setup

Sequence

1. If fresh or manager-only, resolve G-PKG-NAME. Ask with the AskUserQuestion tool when it is not already recorded (the src/<pkg>/ import name; folder name as the default option). Each ask in this skill states in 2–4 lines what the answer authorizes — the scaffolded tree, the persisted policy key, the toolchain a gate implies — and the detected facts it rests on; a file link is an addition, never the context. “You pick” / “go fast” does not resolve it. Do not confirm in prose instead of the tool. A matching [project] name + src/<pkg>/ already resolves it — do not re-ask. A recorded policy.package also resolves it. Do not re-ask. When setup-ml-project dispatched this turn and policy.package is set, do not ask again. When src/<pkg>/ is absent, scaffold that name. A status package of null does not reopen the ask. Persist the resolved import name with python -m skore_skills policy set package <pkg>.

2. Fresh / manager-only:

   python -m skore_skills scaffold --package <pkg>

   The CLI writes the tree and each folder README.md. Do not recreate those files from memory.

3. Existing: do not scaffold again. Do not invent files. No rename, no overwrite, no --force. Do not persist or ask notebooks/site.

4. After scaffold on fresh or manager-only, if policy.notebooks and policy.site are both null (never set): persist both on this turn —

   python -m skore_skills policy set notebooks true

   python -m skore_skills policy set site true

   Do not AskUserQuestion for notebooks or site. Persist both before the user-facing line. That line says executed notebooks and the documentation site are on; the user can turn either off later. Do not say they are still unset or null. Do not write notebooks or site into JOURNAL; policy is the record. Do not ask.

   Same turn after persist:

   Load add-python-package only if status.skills.add-python-package is true and policy.env.managed is true. Else one-line skip: name jupytext / nbclient / ipywidgets / nbconvert / mkdocs-material; do not invent pixi add / uv add; skip site init. Do not invent that skill's steps.

   When that load is allowed:

   • notebooks true → add-python-package for jupytext, nbclient, and ipywidgets (all env route agent), plus nbconvert when site is also true — stage turns write the notebook viewer with --html. Do not leave them as ask. Do not convert.
   • site true → add-python-package for mkdocs-material (agent), then python -m skore_skills site init.

   If either flag is already true or false, do not overwrite and do not install from this step.

5. If setup-ml-project dispatched this turn and is in this session, return to it; else stop. Standalone: python -m skore_skills git end-turn --stage setup. If JSON action is invoke, load persist-ml-git only if status.skills.persist-ml-git is true and stop; it returns to triage. If persist is missing, name the pending staged paths and stop. Otherwise load triage-ml-task only if status.skills.triage-ml-task is true; else stop.