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 Run python -m skore_skills … yourself; do not quote it.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, setup.pending, 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, after the setup gate below. 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:

   Setup gate for a lifecycle skill. Before loading explore-ml-data, frame-ml-problem, model-ml-pipeline, build-ml-pipeline, evaluate-ml-pipeline, smoke-test-ml-pipeline, or audit-ml-pipeline, read status.setup.pending. If it is non-empty and status.skills.setup-ml-project is true, load setup-ml-project and stop. Tell the user the requested work waits on those pieces. Do not show the entry menu. After setup returns, load the certain lifecycle skill. Do not load setup again on this turn. If setup.env or setup.workspace is declined, stop in one line. A declined git or editable does not block. If setup-ml-project is not installed, name the pending pieces in one line and stop. Do not invent git init, scaffold, or env init. Uncertain sessions skip this gate.

   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. If status.setup.pending is non-empty, load setup-ml-project (setup gate above) and stop. Otherwise load explore-ml-data and 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 with setup.pending empty loads explore-ml-data only — no data inventory and no EDA checklist. Pending setup loads setup-ml-project first.
• 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.