gpu-status

name	gpu-status
description	Show GPU availability across all SSH servers listed in this project's CLAUDE.md. Use when user says "check GPUs", "which GPUs are free", "gpu status", "GPU 状态", or needs to know where to run experiments.
argument-hint	optional: server name to filter

GPU Status

Check GPU availability: $ARGUMENTS

Workflow

Step 1: Read Servers from .aris/project.json

Read the project config file:

cat .aris/project.json

This is a structured JSON file with all server details:

{
  "projectId": "...",
  "servers": [
    {
      "name": "papermachine.egr.msu.edu",
      "ssh": "ssh -J chenzh85@scully chenzh85@papermachine",
      "host": "papermachine.egr.msu.edu",
      "user": "chenzh85",
      "proxyJump": "[e-mail]",
      "sshKeyPath": "~/.auto-researcher/id_ed25519",
      "remotePath": "/egr/research-dselab/chenzh85/AutoRDL"
    }
  ]
}

Extract servers from the JSON. If $ARGUMENTS names a specific server, filter to that server only.

If .aris/project.json is missing or has no servers, tell the user:

No remote servers configured. Add SSH server targets to this project in the ARIS workspace.

Step 2: Query Each Server

For each server, run nvidia-smi via SSH:

ssh -o ConnectTimeout=10 -o StrictHostKeyChecking=no \
    [-J <proxyJump>] [-i <sshKeyPath>] \
    <user>@<host> \
    "nvidia-smi --query-gpu=index,name,memory.used,memory.total,utilization.gpu,temperature.gpu --format=csv,noheader" 2>&1

The SSH command is in the ssh field of each server object. Use it directly — no parsing needed.

If nvidia-smi is not found, try /usr/bin/nvidia-smi. If the server is unreachable or has no GPUs, note it and move on.

Query all servers in parallel (use multiple tool calls in one message).

Step 3: Determine Availability

A GPU is free if:

• memory.used < 500 MiB — completely idle
• memory.used < 2000 MiB AND utilization.gpu < 10% — likely idle (small framework overhead)

A GPU is partially available if:

• Used memory is less than 50% of total memory

A GPU is busy if:

• Used memory exceeds 50% of total memory

Step 4: Present Results

Display a table per server:

### papermachine.egr.msu.edu

| GPU | Model       | VRAM Used   | VRAM Total | Util % | Temp | Status    |
|-----|-------------|-------------|------------|--------|------|-----------|
| 0   | RTX A5000   | 234 MiB     | 24564 MiB  | 0%     | 32C  | Free      |
| 1   | RTX A5000   | 22100 MiB   | 24564 MiB  | 87%    | 71C  | Busy      |
| ...                                                                       |

Then a summary:

### Summary

- papermachine: 5 free, 1 partial, 2 busy (8 GPUs)
- chatdse:      4 free, 0 partial, 0 busy (4 GPUs)
- ...
Total free GPUs: 28

Step 5: Recommend

Based on availability:

• If user mentioned a specific workload, suggest which server + GPU(s) to use
• Prefer servers with the most contiguous free GPUs for multi-GPU jobs
• Prefer GPUs with lower temperature (cooler = less thermal throttling risk)
• Note any servers that were unreachable

Key Rules

• Never modify anything on the servers — this skill is read-only
• If SSH times out after 10 seconds, mark server as unreachable and continue
• Query all servers in parallel when possible (use multiple tool calls)
• The source of truth is the local CLAUDE.md — do NOT call any API endpoints