--- name: frame-ml-problem description: > Record the problem, the deployment setting, the comparison metric, the baseline, and the fold count in the journal before model code. Ask every missing decision in one turn, from `frame show`. Does not write Python, estimator hyperparameters, or splitter constructors. TRIGGER when the user asks which metric to compare on, how new rows should be split, which baseline to use, or says a problem constraint changed. Not when they ask to run evaluation or CV. HOW TO USE: run `python -m skore_skills frame show`. Read each reference named in `questions` once, ask every key in `missing` in one message, and write every answered value. If a key is still unanswered, ask it and stop. If the write fills every required decision, set Status to `locked`, say those choices are reused and can be changed by name, then follow `proceed`. Do not ask to confirm. If that command is missing, or the recorded task is not classification, regression, or multi-output regression, follow Fallback below and do not invent the closed menu. metadata: modelTier: medium --- # Frame ML Problem Write `## Modeling decisions` in `journal/JOURNAL.md`. The table is the contract. This skill does not declare a learner and does not evaluate one. ## Human-facing prose Details: `setup-workspace` `references/human_facing_prose.md`. Journal rows describe this dataset. Do not name the skills framework, the CLI, or a splitter class in the table. Questions use data-science language — not skill ids, `G-*` names, or the wrapper CLI. ## Procedure 1. Run `python -m skore_skills status`. If `status.setup.pending` is non-empty and `status.skills.setup-ml-project` is true, load `setup-ml-project` and stop. Do not start this skill. When it returns, continue. Do not load it again on this turn. If that skill is not installed, name the pending pieces in one line and stop. Do not invent `git init`, scaffold, or `env init`. If `status.setup.env` or `status.setup.workspace` is `declined`, stop in one line. A declined `git` or `editable` is not asked again; continue. If `data_analysis` is `missing` and `explore-ml-data` is installed, **AskUserQuestion**: explore first (default) or continue from facts the user stated. Explore loads `explore-ml-data` and stops. Do not invent dataset facts. Do not ask this again once `data_analysis` is `present` or `skipped`. 2. Run `python -m skore_skills frame show`. When the user is changing a locked constraint and named one decision, do not add `--revise` and do not treat `proceed` as the end of the turn. Show this command once, in a fence. The only sentence for it is: That command drops that one recorded decision. Then run `frame show` with no `--revise`. ``` python -m skore_skills frame clear --cell ``` Ask the replacement in this message, the way step 5 asks a missing decision. Do not say that `frame show` will ask it. When the named decision is the comparison metric and the message does not state the new value, say that metric role, prediction goal, and folds stay filled. Then name the experiment file from the workspace state and say it still uses the previous metric and is not run, and that the next build or evaluate rewrites it after the table is complete again. The last line is: The comparison metric is MAE. Which metric replaces it? Nothing follows that line. When the named decision is the prediction goal and the message already states the new value, write that value. Metric role and the comparison metric are unanswered. Status is `draft`. Ask those two in this message. Do not write Status `locked`. Otherwise, if the message already states the new value, write it and ask only the decisions that are still empty. Do not ask Modify / Keep / Stop. Do not say the new value waits until next turn. Do not load `model-ml-pipeline` or git-close on this turn. When they are changing a constraint and did not name a decision, **AskUserQuestion** one pick among the filled decisions (skip `n/a`) and stop. Do not also ask for a typed answer. Do not `--revise` and do not edit the journal on that turn. JSON `action` is authoritative. Do not invent a menu. If the command is missing or exits without JSON, follow Fallback below. Do not open another reference. Do not guess candidates. 3. `stop` — say the JSON `reason` and stop. 4. `ask` / `uncovered` — follow Fallback below, including the table written in this message and the Status rule there. Say the reuse and change lines, and say there is no splitter translation. Do not load `build-ml-pipeline`. Stop this turn. 5. `ask` / `missing_keys` — read each distinct `reference` in `questions` once before asking. Do not open any other file under `references/`. Ask every key in `missing` in one message. For a question that has `candidates`, those are the options. When `candidates` is absent, ask for the value the reference describes. Draw on three sources, and only what they actually say: the EDA report, free-form text that came with the data if any is present (notes, a dictionary, or a README beside the raw files), and facts the user stated. If none of that text is present, do not invent it. When one of them already states the fact, quote it in the question. Write every Value the user answered in this turn. Do not rename the Variable column. Do not stop after the first decision. When the deployment makes other rows inapplicable, set those rows to `n/a` in the same edit. Horizon, gap, and time role are `n/a` unless deployment is time. Generalize-to is `n/a` unless deployment is groups. A fold count of `1` is one train/test split drawn from a single table. When the EDA report, the text shipped with the data, or the user already names a separate training table and test table, offer using that split in the folds question and write `predefined` if they choose it. Do not offer it otherwise. Do not write `prefit` in the table. Do not type `Revised on`; only that command writes that date. If any key in `missing` is still unanswered, set Status to `draft` once any decision is filled, ask those keys, and stop. Do not invent their values. Do not set Status to `locked`. Do not ask to confirm the table. If the write fills every required decision, set Status to `locked` in that same edit, not `draft`. Run `python -m skore_skills frame show` again in this turn. On `proceed`, say the reuse and change lines, then follow step 8. On `ask` / `set`, write Status `locked` only, say those lines, run `frame show` again, and follow step 8. If that `frame show` still returns `missing_keys`, the table was not complete: ask those keys and stop, and leave Status `draft`. Reuse and change lines, quoting JSON `context` in 2–4 lines: these choices are reused for the rest of the experiment so models stay comparable, and any one of them can be changed by naming it (for example the comparison metric). Do not say "lock" in those lines. Do not AskUserQuestion. 6. When the user named one decision and Status is `draft`, do not treat `set` as accepting the table. Show the same fenced command for that decision and stop. Do not write the new value. Do not say any other decision was dropped. The command's JSON list of dropped keys is the record. Status stays `draft`. That command stamps `Revised on`; do not type that date. The next `frame show` asks only keys that are still empty or invalid. 7. `ask` / `set` — the table was already complete and Status is still `draft`. Write Status `locked` only. Say the reuse and change lines from step 5. Run `frame show` again and follow step 8. Do not AskUserQuestion. The user sentence that opened this screen is not a choice. `ask` / `revise` is not a user question. Do not present Modify / Keep / Stop. Ignore those `choices`. Drop the named decision and ask the replacement, as in step 2. 8. `proceed` — the table is locked, and the user is not changing a named decision. If they are, step 2 already handled it. If `translation` is null, say that this lock has no splitter translation. Do not load `build-ml-pipeline` and do not return to `model-ml-pipeline`. Stop. If `model-ml-pipeline` dispatched this turn, return to that coordinator and stop. Do not start build, write a design note, or run the git close from here. If no experiment script exists and History has no running, done, or abandoned model row, and `status.skills.model-ml-pipeline` is true, load that skill and stop. Do not write a design note here. Do not `git end-turn`. Otherwise run `python -m skore_skills git end-turn --stage implement`. If JSON `action` is `invoke`, load `persist-ml-git` only if `status.skills.persist-ml-git` is true and stop. Otherwise load `triage-ml-task` only if that skill is installed. ## Live preview After any turn that changes `journal/JOURNAL.md` (filled decisions, Status, that command, or lock), coalesce all edits, then, when `policy.site` is `true` and `export-ml-site` is installed, run `python -m skore_skills site build --if-stale` before the next question or stop. Link `report.html` in that message. `policy.site` `null` or `false` does not enable or build the site. A read-only `frame show` with no Markdown change does not build. ## Stop conditions - Do not write Python, a pipeline, a test, or a design note. - Do not put a class name or a constructor argument in the journal. `TimeSeriesSplit`, `KFold`, `GroupKFold`, and `gap=` stay out of the table. - Do not re-ask a key that is absent from `missing`. - Do not add an option that is absent from `candidates`. - Do not open a reference the JSON did not name. When the command is missing, follow Fallback below instead of opening a file. - A recorded decision changes when that command drops it, then filling it like any missing one. A complete fill sets Status to `locked` again. Do not ask Modify / Keep / Stop. - That command is the only journal edit that drops a recorded decision, and only for the decision the user named. It stamps `Revised on`; do not type that date. Do not rewrite `experiments/`, `audit/`, or a report in this skill. - After that decision is dropped, do not run an existing experiment script. Say that it still uses the previous splitter and metric. Show the command once, in a fence. The next build or evaluate rewrites it after the table is complete again. ## Fallback Use this when `frame show` is missing, or when the problem is not classification, regression, or multi-output regression. Do not open any other file under `references/`. Do not invent the closed candidate menu. Tell the user, in a few lines, that the closed menu does not cover this case, so the comparison has to be written in words. Ask one question: what would count as a better result, and what is an honest baseline. Draw on the EDA report, free-form text that came with the data if any is present, and facts they stated. Quote a fact those sources already give. If that text is absent, do not invent it. Write the table in this same message: - Prediction goal: `uncovered` - Metric: the comparison, in their words, or empty if they did not state it - Baseline note: the baseline, in their words, or empty if they did not state it - Every other decision row: `n/a`, unless they already stated a value Do not use placeholders. Do not leave the table for a later message. Status is `locked` only when both the comparison and the baseline are already stated. Otherwise Status is `draft`. The words lock, modify, and stop stay out of the message. Do not name a splitter class or write code. Say these choices are reused for the rest of the experiment so models stay comparable, and that any one of them can be changed by naming it. There is no splitter translation. If they say it is actually classification or regression, write that goal instead of `uncovered` and stop. Do not set Status to `locked`. The next `frame show` uses the closed menu. If the command is missing, there is no JSON. The same write uses the same lines. There is no splitter translation.