vastai-debug-bundle
Bundles diagnostic info including instance status, logs, and account details for Vast.ai troubleshooting.
Install
mkdir -p .claude/skills/vastai-debug-bundle && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/5332" && unzip -o skill.zip -d .claude/skills/vastai-debug-bundle && rm skill.zipInstalls to .claude/skills/vastai-debug-bundle
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Collect Vast.ai debug evidence for support tickets and troubleshooting.Key capabilities
- →Collect account information and balance status
- →List all instances with GPU details and SSH connectivity
- →Retrieve instance logs for troubleshooting
- →Perform remote GPU diagnostics including nvidia-smi
- →Verify API connectivity
How it works
It aggregates system-level logs, API status, and remote hardware metrics into a single diagnostic report. It uses SSH to execute commands on the instance to verify driver and memory health.
Inputs & outputs
When to use vastai-debug-bundle
- →Preparing support tickets
- →Investigating GPU instance issues
- →Collecting logs for diagnostics
About this skill
Vast.ai Debug Bundle
Current State
!vastai --version 2>/dev/null || echo 'vastai CLI not installed'
!python3 --version 2>/dev/null || echo 'Python not available'
Overview
Collect comprehensive diagnostic information for Vast.ai GPU instance issues. Covers account verification, instance inspection, log collection, GPU diagnostics, and network testing.
Prerequisites
- Vast.ai CLI installed and authenticated
- Access to the problematic instance (if still running)
Instructions
Step 1: Account and Auth Diagnostics
#!/bin/bash
set -euo pipefail
echo "=== Vast.ai Debug Bundle ==="
echo "Timestamp: $(date -u +%Y-%m-%dT%H:%M:%SZ)"
echo -e "\n--- Account Info ---"
vastai show user --raw | python3 -c "
import sys, json
u = json.load(sys.stdin)
print(f'Username: {u.get(\"username\", \"?\")}')
print(f'Balance: \${u.get(\"balance\", 0):.2f}')
print(f'API Key (first 8): {u.get(\"api_key\", \"?\")[:8]}...')
"
Step 2: Instance Status Collection
echo -e "\n--- All Instances ---"
vastai show instances --raw | python3 -c "
import sys, json
instances = json.load(sys.stdin)
for i in instances:
print(f'ID: {i[\"id\"]} | Status: {i.get(\"actual_status\", \"?\")} | '
f'GPU: {i.get(\"gpu_name\", \"?\")} | '
f'\$/hr: {i.get(\"dph_total\", 0):.3f} | '
f'SSH: {i.get(\"ssh_host\", \"?\")}:{i.get(\"ssh_port\", \"?\")}')
"
Step 3: Instance Log Collection
# Collect logs from a specific instance
INSTANCE_ID="${1:-}"
if [ -n "$INSTANCE_ID" ]; then
echo -e "\n--- Instance $INSTANCE_ID Logs ---"
vastai logs "$INSTANCE_ID" --tail 100 2>/dev/null || echo "No logs available"
echo -e "\n--- Instance $INSTANCE_ID Details ---"
vastai show instance "$INSTANCE_ID" --raw | python3 -c "
import sys, json
i = json.load(sys.stdin)
for key in ['actual_status', 'status_msg', 'gpu_name', 'gpu_ram',
'cuda_max_good', 'disk_space', 'ssh_host', 'ssh_port',
'image_uuid', 'onstart', 'reliability2']:
print(f'{key}: {i.get(key, \"?\")}')
"
fi
Step 4: Remote GPU Diagnostics (if SSH accessible)
if [ -n "$SSH_HOST" ] && [ -n "$SSH_PORT" ]; then
echo -e "\n--- GPU Diagnostics (remote) ---"
ssh -p "$SSH_PORT" -o StrictHostKeyChecking=no "root@$SSH_HOST" << 'REMOTE'
nvidia-smi
echo "---"
nvidia-smi --query-gpu=name,memory.total,memory.used,temperature.gpu,utilization.gpu --format=csv
echo "---"
python3 -c "import torch; print(f'PyTorch CUDA: {torch.cuda.is_available()}, Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"N/A\"}')" 2>/dev/null || echo "PyTorch not available"
echo "---"
df -h /workspace
free -h
REMOTE
fi
Step 5: Network Diagnostics
echo -e "\n--- API Connectivity ---"
curl -s -o /dev/null -w "HTTP %{http_code} in %{time_total}s" \
-H "Authorization: Bearer $VASTAI_API_KEY" \
"https://cloud.vast.ai/api/v0/users/current"
echo ""
Output
- Account info (username, balance, key prefix)
- All instance statuses with GPU details
- Instance logs (last 100 lines)
- Remote GPU diagnostics (nvidia-smi, CUDA, disk, memory)
- API connectivity test
Error Handling
| Issue | Diagnostic | Solution |
|---|---|---|
Instance shows error | Check status_msg in details | Destroy and reprovision on different host |
| SSH unreachable | Instance may still be loading | Wait for running status |
| GPU not detected | CUDA driver mismatch | Use image matching host CUDA version |
| Disk full | Check df -h /workspace | Increase disk or clean artifacts |
Resources
Next Steps
For rate limit handling, see vastai-rate-limits.
Examples
Quick debug: Run vastai show instance ID --raw | jq '{actual_status, status_msg, gpu_name, ssh_host, ssh_port}' for a one-line status summary.
Support ticket: Collect the full debug bundle output, include vastai logs ID, and attach nvidia-smi output from the instance.
When not to use it
- →Automated instance recovery without manual intervention
Prerequisites
Limitations
- →Instance must be running to collect remote GPU diagnostics
- →Requires SSH access to the instance
How it compares
This method automates the collection of disparate diagnostic data points into a single bundle, replacing manual inspection of individual logs and status commands.
Compared to similar skills
vastai-debug-bundle side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| vastai-debug-bundle (this skill) | 1 | 27d | Review | Beginner |
| n8n-expression-syntax | 6 | 4mo | No flags | Beginner |
| python-repl | 6 | 4mo | Review | Beginner |
| n8n-validation-expert | 6 | 4mo | No flags | Intermediate |
Try saying
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