run-train
This skill executes selected deep learning training commands and standardizes the output of training evidence, ensuring experimental reproducibility. It assists users with startup verification, short-run verification, full training kickoff, or resuming training, automatically recording commands, configurations, seeds, logs, checkpoints, status, and metrics. This significantly enhances the efficiency of deep learning research and experiment management.
npx skills add https://github.com/lllllllama/rigorpilot-skills --skill run-trainBefore / After Comparison
1 组Before this skill, users manually running deep learning training had to spend significant time collecting, organizing, and standardizing training logs, checkpoints, and metrics. This process was time-consuming, error-prone, and made experiment results difficult to trace and reproduce.
This skill automatically executes training commands and standardizes all outputs. Users no longer need to manually organize results, significantly reducing the time spent on collecting and standardizing experiment data, ensuring reproducibility and consistency.
run-train
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.
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.mdtrain_outputs/COMMANDS.mdtrain_outputs/LOG.mdtrain_outputs/SCIENTIFIC_CHANGELOG.mdtrain_outputs/COMPARABILITY_REPORT.mdtrain_outputs/status.json
Notes
Use references/training-policy.md, ../../references/deep-learning-experiment-principles.md, scripts/run_training.py, and scripts/write_outputs.py.
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