run-train

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.

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/status.json

Notes

Use references/training-policy.md, scripts/run_training.py, and scripts/write_outputs.py.