exp-new

name	exp-new
description	Create a new experiment folder by copying reference/ as a template. Auto-generates SESSION_NOTES.md and run.sh. Claude experiments use expA00, A01, ...; human experiments use exp200, 201, ... in sequential numbering.
argument-hint	<experiment_name> [--human for human-style exp200_xxx]

New Experiment Folder Skill

Steps

1. Parse arguments:

   • Get experiment name from $ARGUMENTS (required)
   • Check for --human flag

2. Determine folder name:

   • Claude: start at workspace/expA00_<name>, advance to A01, A02, ..., B00, ... based on existing folders
   • Human: start at workspace/exp200_<name>, advance to 201, 202, ...
   • ls workspace/ first to determine the next number

3. Copy template:

   cp -r reference/ workspace/<new-folder>/
   

4. Generate SESSION_NOTES.md (don't overwrite if it exists):

# SESSION_NOTES — <folder name>

## Session Info
- Date: YYYY-MM-DD
- Working folder: workspace/<folder name>
- Goal: <inferred from name or asked from user>

## Base
- Base: reference/ (PyTorch Lightning + timm + smp)
- Parent exp (if any): ...

## Approaches Tried and Results
<!-- Append after each run -->

## File Structure
- train.py
- src/
- config.yaml
- run.sh

## Key Insights
<!-- Insights from training -->

## Next Steps
- [ ] ...

## Performance History
| Trial | Change | CV | LB | Notes |
|-------|--------|-----|-----|-------|

## Command History
<!-- Major commands run -->

5. Generate run.sh (with execute permission):

#!/bin/bash
# Train + inference in one script
# IMPORTANT: cd "$(dirname "$0")" must run before python.
#            Without it, results/ may be created at the repo root or elsewhere.
# Usage: bash run.sh data.fold=0 trainer.max_epochs=30
#        Arguments override config.yaml in OmegaConf dotlist format (there is no --config flag)
set -e

cd "$(dirname "$0")"

# Train (train.py lives directly in the experiment folder; pass arguments through)
python train.py "$@"

# Inference (as needed)
# python predict.py ckpt_path=results/<experiment_name>/fold0/last.ckpt

6. Update experiment.name in config.yaml to the new folder name

7. Make the results/ output path relative to the experiment folder:

   • Verify train.py's output path is not cwd-dependent
   • If needed, rewrite to use the absolute path resolved from the experiment folder: output_dir = Path(__file__).resolve().parent / 'results' / experiment_name / f'fold{fold}' (one parent because train.py lives directly in the experiment folder)
   • Run a quick training trial to confirm output appears in workspace/<new-folder>/results/... before reporting to the user

8. Report:

   • Path of the created folder
   • Paths of SESSION_NOTES.md and run.sh
   • Next action (edit config.yaml → bash workspace/<new-folder>/run.sh)

Notes

• If a folder with the same name exists, do not overwrite. Ask the user how to handle numbering
• Stop with a warning if reference/ is missing
• Don't include datasets or model files in the template (covered by .gitignore)