Review ML Experiment

SkillFiles & storage

Lets your agent review machine learning experiment results and write one markdown idea file per candidate.

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Then ask your AI: use the Review ML Experiment skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

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About this skill

Post-evaluate review. Gate the expensive skore-check audit, then write one markdown idea file per candidate. Trigger after a successful evaluate, on "review this stem", or when review consent is Review or proceed. Do not write JOURNAL.md or a design note. Do not run cells run until the user accepts

What this skill tells your AI

The instructions your AI receives, as published by probabl-ai/skills in skills/review-ml-experiment/SKILL.md and read by Ahel’s review.

Optional loop step after evaluate. Record-outcome stays with the caller. This skill writes idea files only.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. The Review / Skip / Stop question and cost preview describe reading this report and writing follow-up ideas — 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.
    • ask — report exists, digest does not. Emit the cost preview below, then AskUserQuestion: Review (Recommended) / Skip / Stop. "Audit it" on a first run is not consent. If this turn already answered Review, do not ask again.
    • proceed — digest already on disk. Do not re-run checks. Refresh idea files from the existing digest.
  2. Skip — write no idea files. Return n/a — audit not run so the caller can record-outcome.
  3. Stop — do not audit and do not record-outcome. The persisted report stays. Load triage-ml-task when status.skills.triage-ml-task is true.
  4. Review, or proceed for idea refresh: load audit-ml-pipeline only if status.skills.audit-ml-pipeline is true. On Review, that skill runs cells run (consent stays ask until the digest exists; the Review answer is what starts it). On proceed, do not cells run. 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.
  5. 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 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.
  6. 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. On a refresh, keep an existing file's Triage value. A new candidate is open.
  7. 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.

An explicit re-audit asks the gate again before cells run, even when a digest exists, because it re-runs the checks.

Cost preview

On ask, before the question, emit 1–3 sentences. This is a local read of the persisted report, not another fit. The audit template runs every skore check, writes audit/<stem>.py and scratch/audit/<stem>/audit.md, and can be slow on a large report. Name the stem and those paths. Do not invent minutes.

Stop conditions

  • Do not run cells run before Review or an explicit re-audit confirmation.
  • Do not write JOURNAL.md or a design note.
  • Do not call skore.evaluate or project.put.
  • Do not pick a winning idea.
  • Do not invent a missing child's procedure.

Signals

GitHub stars
137
Forks
9
Last commit
Oct 2026
Advanced
Item type
skill
Key
review-ml-experiment
Source
github.com/probabl-ai/skills