Monitor Experiment Results

⏱ External cadence is appropriate here. This skill waits on an external fact (job completion / progress), so it is a natural /loop / CronCreate surface: the wake reads status and self-judges only machine-checkable completion (exit code, file exists, epoch logged), never quality. This is the additive external-wait shape in shared-references/external-cadence.md. If a scheduled wait here ends in a verdict step (e.g. then audit results), run that verdict once after the wait clears, not re-entered per tick.

Monitor: $ARGUMENTS

Workflow

Step 1: Check What's Running

SSH server:

ssh <server> "screen -ls"

Vast.ai instance (read ssh_host, ssh_port from vast-instances.json):

ssh -p <PORT> root@<HOST> "screen -ls"

Also check vast.ai instance status:

vastai show instances

Modal (when gpu: modal in CLAUDE.md):

modal app list         # List running/recent apps
modal app logs <app>   # Stream logs from a running app

Modal apps auto-terminate when done, if it's not in the list, it already finished. Check results via modal volume ls <volume> or local output.

Step 2: Collect Output from Each Screen

For each screen session, capture the last N lines:

ssh <server> "screen -S <name> -X hardcopy /tmp/screen_<name>.txt && tail -50 /tmp/screen_<name>.txt"

If hardcopy fails, check for log files or tee output.

Step 3: Check for JSON Result Files

ssh <server> "ls -lt <results_dir>/*.json 2>/dev/null | head -20"

If JSON results exist, fetch and parse them:

ssh <server> "cat <results_dir>/<latest>.json"

Step 3.5: Pull W&B Metrics (when wandb: true in CLAUDE.md)

Skip this step entirely if wandb is not set or is false in CLAUDE.md.

Pull training curves and metrics from Weights & Biases via Python API: