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:

# List recent runs in the project
ssh <server> "python3 -c \"
import wandb
api = wandb.Api()
runs = api.runs('<entity>/<project>', per_page=10)
for r in runs:
    print(f'{r.id}  {r.state}  {r.name}  {r.summary.get(\"eval/loss\", \"N/A\")}')
\""

# Pull specific metrics from a run (last 50 steps)
ssh <server> "python3 -c \"
import wandb, json
api = wandb.Api()
run = api.run('<entity>/<project>/<run_id>')
history = list(run.scan_history(keys=['train/loss', 'eval/loss', 'eval/ppl', 'train/lr'], page_size=50))
print(json.dumps(history[-10:], indent=2))
\""

# Pull run summary (final metrics)
ssh <server> "python3 -c \"
import wandb, json
api = wandb.Api()
run = api.run('<entity>/<project>/<run_id>')
print(json.dumps(dict(run.summary), indent=2, default=str))
\""

What to extract:

Training loss curve — is it converging? diverging? plateauing?
Eval metrics — loss, PPL, accuracy at latest checkpoint
Learning rate — is the schedule behaving as expected?
GPU memory — any OOM risk?
Run status — running / finished / crashed?

W&B dashboard link (include in summary for user):

https://wandb.ai/<entity>/<project>/runs/<run_id>

This gives the auto-review-loop richer signal than just screen output — training dynamics, loss curves, and metric trends over time.

Step 4: Summarize Results

Present results in a comparison table:

| Experiment | Metric | Delta vs Baseline | Status |
|-----------|--------|-------------------|--------|
| Baseline  | X.XX   | —                 | done   |
| Method A  | X.XX   | +Y.Y              | done   |

Step 5: Interpret

Compare against known baselines
Flag unexpected results (negative delta, NaN, divergence)
Suggest next steps based on findings

Step 6: Feishu Notification (if configured)

After results are collected, check ~/.claude/feishu.json:

Send experiment_done notification: results summary table, delta vs baseline
If config absent or mode "off": skip entirely (no-op)

Key Rules

Always show raw numbers before interpretation
Compare against the correct baseline (same config)
Note if experiments are still running (check progress bars, iteration counts)
If results look wrong, check training logs for errors before concluding
Vast.ai cost awareness: When monitoring vast.ai instances, report the running cost (hours * $/hr from vast-instances.json). If all experiments on an instance are done, remind the user to run /vast-gpu destroy <instance_id> to stop billing
Modal cost awareness: Modal auto-scales to zero — no idle billing. When reporting results from Modal runs, note the actual execution time and estimated cost (time * $/hr from the GPU tier used). No cleanup action needed

Monitor Experiment