Triage ML Task

This is the session owner. Stage skills do the work; you only route and ask. Do not execute another skill's methodology.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. Questions and replies describe the work (explore the data, build a model) — not skill ids, G-* names, or the wrapper CLI. Run python -m skore_skills … yourself; do not quote it.

Every question here carries its own context: 2–4 lines on what the answer authorizes, the workspace facts it rests on — echoed inline from status (scaffold, data_analysis, loop_stage) — and what each option leads to. A file link is an addition, never the context.

Procedure

1. Run python -m skore_skills status. Read skills, data_analysis, loop_stage, and the filesystem snapshot. If .skore is missing, that is expected. Do not treat a missing file as an empty project when src/ or journal/ exist.

2. Certain request — load that skill. Tell the user the work you are starting, not the catalog id. Do not list the catalog. status.skills is a per-id dict. Load the mapped skill only if status.skills.<id> is true; else one-line skip and do not invent that skill's steps:

   User intent	Skill
   env / pixi / uv / python environment	setup-python-env
   scaffold / layout / package folders	setup-workspace
   git init / first commit / ignore	setup-git
   add or install a named package	add-python-package
   exploratory data analysis / explore the data	explore-ml-data
   evaluate / metrics / CV / run skore.evaluate	evaluate-ml-pipeline (child gate may STOP)
   audit / open / narrate an existing report	audit-ml-pipeline (child gate may STOP)
   build / model a pipeline	model-ml-pipeline
   smoke / pytest row-count / why is smoke failing	smoke-test-ml-pipeline (debug; does not start evaluate)
   backlog / history / record the run / what next	manage-ml-backlog
   review this stem / review the last experiment	review-ml-experiment
   I want to try X / here is an idea / what if we / a pasted URL or issue	shape-user-idea
   papers / literature / what do people do for (no design note in progress)	search-ml-literature
   notebook / ipynb	export-ml-notebook
   notebook viewer on the site / executed report	export-ml-notebook (--html)
   website / mkdocs / documentation site	export-ml-site
   export (generic)	export-ml-project
   sync / migrate reports / switch skore mode / upload reports to hub or mlflow	sync-ml-reports
   set up / bootstrap this project (generic)	setup-ml-project
   “is this leakage” on the table	explore-ml-data (even if data_analysis is present). Do not load research-ml-practice.
   research / literature on a modeling design (design note exists or modeling in progress)	model-ml-pipeline. Do not load research-ml-practice.
   which comparison metric / how new rows should be split / which baseline / a problem constraint changed	frame-ml-problem. Not a request to run evaluation.
   what did we decide / show stored choices / change a stored project choice	review-ml-choices

   An explicit sync, generic export, or changed modeling constraint still uses those rows. Do not send them through review-ml-choices.

   A git init, first commit, or ignore request loads setup-git and stops. Do not write git init, a .gitignore, or a commit plan. With no shell, the whole answer is that setup-git is loaded. Do not describe the commands that skill will run.

   "What should we try next?" while loop_stage is backlog and manage-ml-backlog is installed loads that skill and stops. Do not ask the user to choose explore, build, review, or export.

   Certain EDA: run python -m skore_skills status, load explore-ml-data, stop. Do not inventory data/, list missingness or distributions, or start EDA methodology.

   Certain generic export: run python -m skore_skills status, then load export-ml-project. Do not list notebook / --html / site as sibling options.

   Modeling while status.data_analysis is missing: if the certain skill is model-ml-pipeline (or the user asked to build the first experiment) and status.skills.explore-ml-data is true, do not load modeling yet. AskUserQuestion two options: run exploratory data analysis first (default) vs proceed to modeling with user-supplied facts. Do not invent dataset facts here. If data_analysis is present or skipped, load model-ml-pipeline with no extra gate (still only if that id is true).

3. Uncertain (open session, “what can you do”, finished stage, mixed intent) — AskUserQuestion with the installed entry work only. One pick, then load the mapped skill.

   Offer a label only if status.skills.<id> is true. User-visible labels (no ids):

   • Set up the project → setup-ml-project
   • Explore the data → explore-ml-data
   • Build a model → model-ml-pipeline
   • Review the last experiment → review-ml-experiment
   • Record / decide what next → manage-ml-backlog
   • Export → export-ml-project
   • Sync reports → sync-ml-reports
   • Review choices → review-ml-choices

   If none of those ids are true, say so in one line; do not invent a menu.

   Do not put evaluate or audit on this board (certain requests still load evaluate-ml-pipeline / audit-ml-pipeline). Do not put shaping an idea or literature search on this board (certain requests and manage-ml-backlog still load shape-user-idea / search-ml-literature).

   If status.data_analysis is missing and status.skills.explore-ml-data is true, recommend exploring the data first. Do not auto-load it. When data_analysis is present or skipped and loop_stage is backlog, recommend recording the run / deciding what next when manage-ml-backlog is true. Do not auto-load it.

   Do not put internals on this board (build-ml-pipeline, smoke-test-ml-pipeline except as a certain debug load, frame-ml-problem, choose-python-library, research-ml-practice, plot-ml-figure, stack refs).

Stop conditions

• Do not design experiments, write pipelines, or run exploratory data analysis yourself. Certain EDA is load explore-ml-data only — no data inventory and no EDA checklist.
• Do not load every skill.
• Do not invent workspace facts when status is unavailable.
• Do not treat a missing .skore as an empty project when src/ or journal/ exist.
• Do not invent a missing skill's steps from memory.
• Do not put evaluate-ml-pipeline or audit-ml-pipeline on the uncertain entry board (certain requests still load them).
• A missing review, backlog, user-idea, or literature skill is a one-line skip. Do not invent that skill's procedure.
• Do not put shape-user-idea or search-ml-literature on the uncertain entry board.
• Do not put frame-ml-problem on the uncertain entry board (a certain metric, split, baseline, or changed-constraint request still loads it).
• Certain generic export: run python -m skore_skills status, load export-ml-project only — no sibling-skill menu.

End of every other skill's turn returns here when this skill is installed.