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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.

Use this skillView repositoryView on skills.sh
Specification
Skill ID
lllllllama/rigorpilot-skills/run-train
Publisher
lllllllama
Repository
rigorpilot-skills
Installs
0
Files
5
Synced
Sep 16, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

lllllllama/rigorpilot-skills/run-trainInstalls these files
  • SKILL.md
  • agents/openai.yaml
  • references/training-policy.md
  • scripts/run_training.py
  • scripts/write_outputs.py

What this skill tells the agent

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.

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