run-train

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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.

将此作为Rigor Train技能使用。为保持兼容性，已安装的别名仍为

run-train

。

请遵循

../../references/agent-operating-principles.md

中的共享操作原则；此技能应确保训练证据的规范性，同时将仓库特定的监控细节留给模型处理。

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 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.

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.

selected training goal
runnable training command
environment and asset assumptions
run mode such as startup verification, short-run verification, full kickoff, or resume

Use

references/training-policy.md

../../references/deep-learning-experiment-principles.md

scripts/run_training.py

, and

scripts/write_outputs.py

请使用

references/training-policy.md

、

../../references/deep-learning-experiment-principles.md

、

scripts/run_training.py

和

scripts/write_outputs.py

。