/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
$ npx -y skills add lllllllama/rigorpilot-skills --skill run-train --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/run-train
Context preview
The summary Claude sees to decide when to auto-load this skill.
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
SKILL.md
run-train.SKILL.mdname: 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`.
Read more
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`.
Research-first Agent Skills for Deep Learning Experiments. Main idea: RigorPilot keeps AI-assisted deep learning research grounded in comparability, reproducible evidence, and auditable changes while an agent reproduces, improves, or explores a research
Repo: lllllllama/rigorpilot-skills
Other skills on rigorpilot-skills.
- /ai-research-explore
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of
Open skill - /ai-research-reproduction
Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake,
Open skill - /analyze-project
Rigor Analyze / Rigor Audit read-only skill for deep learning research repositories. Use when the user wants to read and understand a repository, inspect model structure and training or inference entrypoints, review configs and insertion points, or flag suspicious implementation
Open skill - /env-and-assets-bootstrap
Rigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented
Open skill - /explore-code
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter
Open skill - /explore-run
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or
Open skill