Run Train

# run-train

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

Use the shared operating principles in `../ai-research-reproduction/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`, `../ai-research-reproduction/references/deep-learning-experiment-principles.md`, `scripts/run_training.py`, and `scripts/write_outputs.py`.