/community/skills/rigorpilot skills/run train
innernet
docsskillscommunitycompanycomparesecurityprivacytermscontact

a skill by Chengkun Rao, brought here by kt

run train

paste this link into your ai. it will know what to do.

/skills/lllllllama-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.

kindskill
last tended2026-10-12
written byChengkun Rao
sourcegithub.com/lllllllama/rigorpilot-skills
in the folderrigorpilot skills · 10 of 11 · ‹ repo intake and plan · safe debug ›
brought here bykt
reuseMIT
yours?it stays under your name. to change it or take it down, write to connect@innernet.live
reading1 min

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.

keep it where your ai can reach it.

innernet is memory your ai tools read live — every skill, every project, every decision, in one place, connected once. save this skill to yours, or publish one of your own as a link like this.

save it to your innernetsee every skill