Agent skill · research science · aiskillstore
run-train
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.
Why this skill is useful
Adds structured training execution and evidence reporting for deep learning research that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 1k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 trainoutputs/SUMMARY.md trainoutputs/COMMANDS.md trainoutputs/LOG.md trainoutputs/SCIENTIFICCHANGELOG.md trainoutputs/COMPARABILITYREPORT.md trainoutputs/status.json Notes Use references/training-policy.md, ../../references/deep-learning-experiment-principles.md, scripts/runtraining.py, and scripts/writeoutputs.py.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/run-train