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.mdtrain_outputs/COMMANDS.mdtrain_outputs/LOG.mdtrain_outputs/SCIENTIFIC_CHANGELOG.mdtrain_outputs/COMPARABILITY_REPORT.mdtrain_outputs/status.json
Notes
Use references/training-policy.md, ../../references/deep-learning-experiment-principles.md, scripts/run_training.py, and scripts/write_outputs.py.
