---
id: gh-run-train
name: "run-train"
url: https://skills.yangsir.net/skill/gh-run-train
author: lllllllama
domain: data-ai
tags: ["deep-learning", "training", "reproducibility", "experiment-management"]
install_count: 330000
rating: 4.90 (120 reviews)
github: https://github.com/lllllllama/rigorpilot-skills/tree/main/skills/run-train
---

# run-train

> 此技能用于执行深度学习训练命令，并标准化训练过程中的证据输出，确保实验的可复现性。它能帮助用户进行启动验证、短程验证、完整训练启动或恢复，自动记录命令、配置、种子、日志、检查点、状态和指标，极大地提升了深度学习研究的效率和实验管理水平。

**Stats**: 330,000 installs · 4.9/5 (120 reviews)

## Before / After 对比

### 深度学习实验结果整理效率

**Before**:

用户在手动执行深度学习训练后，需要花费大量时间手动收集、整理和标准化训练日志、检查点和指标，这不仅耗时，还容易出错，导致实验结果难以追溯和复现。

**After**:

此技能自动执行训练命令并标准化所有输出，用户无需手动整理，显著减少了实验结果的收集和标准化时间，确保了实验的可复现性和一致性。

| Metric | Before | After | Change |
|---|---|---|---|
| 实验结果整理时间 | 60分钟 | 10分钟 | -83% |

## Readme

# 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.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`, `../../references/deep-learning-experiment-principles.md`, `scripts/run_training.py`, and `scripts/write_outputs.py`.


---
*Source: https://skills.yangsir.net/skill/gh-run-train*
*Markdown mirror: https://skills.yangsir.net/api/skill/gh-run-train/markdown*