How do I install the Run Train skill?

Run npx skillmds@latest add lllllllama/run-train in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Run Train safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Run Train?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Run Train free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Run Train?

lllllllama (@lllllllama) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Run Train by lllllllama — Agent Skill

Name: Run Train
Rating: 4.666666666666667 (3 reviews)
Author: 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.

lllllllama Updated Sep 9, 2026

File contents

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.

---name: run-traindescription: 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.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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