oms-ml-experiment

name	oms-ml-experiment
description	Run training and evaluation as registered experiments instead of ad-hoc launches: check the experiment board for duplicates, pre-register hypothesis runs, gate long runs through the run ledger, capture a reproducibility capsule, and record outcomes where the next session reads them.

ML Experiment Discipline

An unrecorded run is a run the next session repeats. Before launching anything that costs more than a smoke test:

1. Check for duplicates: oms experiment-board list — another agent or session may already be running or have finished this configuration.
2. Pre-register hypothesis runs: if this run tests a claim, register it before launch with oms research-runner (hypothesis, pre-registered metric, baseline). A verdict recorded after the fact is not evidence.
3. Gate the launch: long or expensive runs go through oms run-ledger — it runs the project's check.sh pre-flight, warns on duplicates, and writes one JSONL row per run. Record eval scalars with --metrics.
4. Capture reproducibility: oms run capsule bundles the exact command, environment, and data provenance so an output can be traced back to its run.
5. Record the outcome: metrics into the ledger row, the conclusion into the task packet (oms agent-task update --decision/--result) so the Work Journal daily carries it.

Rank past runs with oms run-ledger top --metric KEY before proposing a new configuration — the best known baseline is a lookup, not a memory.