run-train
🎯SkillA skill that carefully runs a training command you already chose for a deep learning project. It saves the command, settings, logs, and results in one standard folder.
Installation
npx skills add lllllllama/rigorpilot-skills --skill run-trainRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh · Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/run-trainInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder →- This project
.claude/skills/run-train/ - All projects
~/.claude/skills/run-train/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
What this skill does
Overview
run-train is the Rigor Train skill. It runs a training command you have already selected and organizes the evidence from the run, such as configs, seeds (numbers that make random steps repeatable), checkpoints (saved copies of the model), logs, and metrics. It focuses on careful, repeatable runs, not on exploring new ideas or setting up the environment.
Key Features
- Choose a run mode: check that training starts, check a short run, start full training, or resume a stopped run.
- Get a summary, the commands used, and a log in
train_outputs/SUMMARY.md,train_outputs/COMMANDS.md, andtrain_outputs/LOG.md. - Also get
train_outputs/SCIENTIFIC_CHANGELOG.mdandtrain_outputs/COMPARABILITY_REPORT.md. - Check
train_outputs/status.json, which clearly marks runs as partial, blocked, resumed, or kicked off. - Keep configs, seeds, checkpoints, logs, metrics, and runtime assumptions together so you can repeat the run.
Who is this for?
Use it when you have a training command ready and want to confirm it starts, then run it fully or resume it later. It is not for environment setup, inference-only or evaluation-only runs, sweeps (trying many settings), or trying out new ideas.
Summarized by AI from this skill's SKILL.md. Suggest a better one
SKILL.md
View on GitHub →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
../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.mdtrain_outputs/COMMANDS.mdtrain_outputs/LOG.mdtrain_outputs/SCIENTIFIC_CHANGELOG.mdtrain_outputs/COMPARABILITY_REPORT.mdtrain_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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