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.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add lllllllama/rigorpilot-skills --skill "run-train" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/lllllllama/rigorpilot-skills /tmp/rigorpilot-skills && cp -r /tmp/rigorpilot-skills/skills/run-train ~/.claude/skills/run-train-lllllllamaThis skill is a directory: SKILL.md is the entry point; the files below ship with it.
---
name: run-train
description: 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.
---
# 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`.
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when implementing any feature or bugfix, before writing implementation code