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Machine Learning / AGENT SKILL
run-train
0
Run documented deep learning training commands conservatively and record execution evidence.
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 standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.
Understand the trade-offs.
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The full skill.
Original instructions from the publisher’s SKILL.md
# 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`.
Skill folder
Files included alongside SKILL.md in the publisher’s repository.