/aris-monitor-experiment

name: aris-monitor-experiment
description: Monitor running experiments, check progress, collect results. Use when user says "check results", "is it done", "monitor", or wants experiment output.
argument-hint: "[server-alias or screen-name]"
allowed-tools: Bash(ssh *), Bash(echo *), Read, Write, Edit
license: MIT
metadata:
  author: wanshuiyin/ARIS
  version: "1.0.0"

Monitor Experiment Results

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 `/aris-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