run-train

Use this as the Rigor Train skill. The installed slug remains run-train for

compatibility.

Use the shared operating principles in

../../references/agent-operating-principles.md; this skill should keep

training evidence bounded while leaving repository-specific monitoring details

to the model.

When to apply

• When the training command has already been selected and should be executed conservatively.

• When the researcher wants startup verification, short-run verification, full training kickoff, or resume handling.

• When the run needs structured training status, checkpoint, and metric reporting.

When not to apply

• When the main task is environment setup or asset download.

• When the researcher wants inference-only or evaluation-only execution.

• When the task is speculative exploration, multi-variant sweeps, or autonomous idea implementation.

• When the user still needs repository intake or paper gap resolution.

Clear boundaries

• This skill executes a selected training command and normalizes the resulting evidence.

• It does not choose the overall research goal on its own.

• It does not own exploratory branching or speculative code adaptation.

• It should record partial, blocked, resumed, and kicked-off states clearly.

• It should preserve reproducibility context such as configs, seeds,

checkpoints, logs, metrics, and runtime assumptions when available.

Input expectations

• selected training goal

• runnable training command

• environment and asset assumptions

• run mode such as startup verification, short-run verification, full kickoff, or resume

Output expectations

• train_outputs/SUMMARY.md

• train_outputs/COMMANDS.md

• train_outputs/LOG.md

• train_outputs/SCIENTIFIC_CHANGELOG.md

• train_outputs/COMPARABILITY_REPORT.md

• train_outputs/status.json

Notes

Use references/training-policy.md, ../../references/deep-learning-experiment-principles.md, scripts/run_training.py, and scripts/write_outputs.py.