probabl-ai/skills/review-ml-experiment

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Checking that the SKILL.md file is still in the repository.

Read the stored report, then write one idea file and one Ideas row per candidate. Trigger after a successful evaluate, on "review this stem", or when review consent is audit or proceed. Do not write History, Backlog, or a design note.

Install

npx vskill@latest install probabl-ai/skills/review-ml-experiment

SKILL.md

Review ML Experiment

Optional loop step after evaluate. Record-outcome stays with the caller. This skill writes idea files and their Ideas rows. The stem is whichever experiment was reviewed. Check results were stored with the report, so the audit reads them.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. Idea files describe this report and the follow-up — not skill ids, cells run, or the wrapper CLI.

Procedure

  1. Run python -m skore_skills status and

python -m skore_skills review consent --stem <stem>. Treat JSON action as authoritative.

  • stop — no scratch/results/<stem>/report.html. Name that

file and stop. Do not audit. Do not record-outcome.

  • audit — report exists, digest does not. Load

audit-ml-pipeline when installed. That skill runs materialize.py once and does not execute the audit file.

  • proceed — digest already on disk. Do not re-run

materialize.py unless the user asked to re-audit. A re-audit loads audit-ml-pipeline; that skill runs materialize.py once. Otherwise refresh idea files from the existing digest.

  1. Missing audit skill → one-line skip. Return

n/a — audit not run and write no idea files. Do not open the Project or call report.* here.

  1. Read the design note, the EDA summary, the last History row,

and the digest. One candidate per Issues: / Tips: line. A methodological gap the design note named and this run did not test is another candidate. A user idea or a literature query is not a candidate here: after this skill returns, shape-user-idea or search-ml-literature writes that file and its Ideas row when the user asks. Load research-ml-practice only if status.skills.research-ml-practice is true and an audit or design candidate needs sources; otherwise one-line skip. Do not invent papers, metrics, or a winner.

  1. Write one file per candidate at

journal/ideas/<stem>-<slug>.md with Experiment, Source (audit:<stem>:checks.<code> or design:<stem>), Triage open, Question, Why now, What changes, Open gaps. No acceptance criteria. For a check candidate, read the documentation URL on that line and apply that page's recommendation in What changes. On a refresh, keep an existing file's Triage value and the matching Ideas status. A new candidate is open.

  1. Upsert one ## Ideas row per file in journal/JOURNAL.md.

If that table is missing, insert it between History and Backlog. Columns: Question, Status, Experiment, Source. Question is the file's Question as plain text, not a link. Status is open, discarded, or aside, matching Triage. A promoted file has no Ideas row. Experiment is this run's stem. Source is copied verbatim. Edit only that table.

  1. Return the digest, JSON finding from

python -m skore_skills audit finding --stem <stem>, the locator from python -m skore_skills loop locator --stem <stem>, and the idea paths.

Stop conditions

  • On proceed, do not re-run materialize.py unless the user

asked to re-audit.

  • Do not write History, Backlog, Status, or a design note.

The Ideas table is the only JOURNAL.md edit.

  • Do not call skore.evaluate or project.put.
  • Do not pick a winning idea.
  • Do not invent a missing child's procedure.

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

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