run-train
Skill495 starsChanged 6 months agoMITView 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.
In this repository11 other files it gives its agents.
- AGENTS.mdAGENTS.md
- ai-research-exploreSkill
- ai-research-reproductionSkill
- analyze-projectSkill
- env-and-assets-bootstrapSkill
- explore-codeSkill
- explore-runSkill
- minimal-run-and-auditSkill
- paper-context-resolverSkill
- repo-intake-and-planSkill
- safe-debugSkill
The file
We split this skill into the rules it gives an agent
11 rules. About 0 are common, 8 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 8
Use this as the Rigor Train skill.
- Very specific: line 11
Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md.
- Distinctive: line 18
When the training command has already been selected and should be executed conservatively.
- Distinctive: line 19
When the researcher wants startup verification, short-run verification, full training kickoff, or resume handling.
- Distinctive: line 20
When the run needs structured training status, checkpoint, and metric reporting.
- Distinctive: line 24
When the main task is environment setup or asset download.
- Distinctive: line 25
When the researcher wants inference-only or evaluation-only execution.
- Distinctive: line 26
When the task is speculative exploration, multi-variant sweeps, or autonomous idea implementation.
- Distinctive: line 27
When the user still needs repository intake or paper gap resolution.
- Distinctive: line 43
run mode such as startup verification, short-run verification, full kickoff, or resume
- Very specific: line 56
Use references/training-policy.md, ../ai-research-reproduction/references/deep-learning-experiment-principles.md, scripts/run_training.py, …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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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