Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to st

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 .

run-train