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.