launch

# Launch: pre-flight checklist for long ML training jobs Long training jobs are expensive to fail. A 12-hour run that crashes on epoch 3 from a missing dataset path or a default `workers=8` against an NFS mount is a full day lost. This skill walks five quick checks before you commit the GPUs. The agentic Stop hook in this plugin will route here from `reason` when an assistant tries to launch a run without going through the checklist. ## When to run The user just asked to: - launch / kick off / start / fire up a training run - restart a run that died - kill a current run (also runs the cleanup half of the checklist) - review a launch command before submitting Or the user is about to run any of: `python train.py`, `accelerate launch`, `torchrun`, `deepspeed`, `sbatch train.sh`, `tmux new-session ... python ... train`, `wandb sweep`. ## The checklist ### 1. Config diff against a reference run The most expensive failure is launching with the wrong knobs. Before starting: ```bash find configs/ recipes/ experiments/ -maxdepth 3 \( -name '*.yaml' -o -name '*.yml' -o -name '*.json' -o -name '*.toml' \) -mtime -30 2> /dev/null | head ``` Pick the most-recently-modified config that resembles the intended run (same model family, same task). Diff against the intended config: ```bash diff -u configs/baseline_v1.yaml configs/intended.yaml ``` Walk every diff line. For each, ask: _is this difference intentional and motivated, or is it a stale default I forgot to set?_ Common si...