getcompanion-ai/runpod-compute
skills/runpod-compute/SKILL.md
Provision and manage GPU pods on RunPod for long-running experiments. Use when the user needs persistent GPU compute with SSH access, large datasets, or multi-step experiments.
$ install --global
skillsauthnpx skillsauth add getcompanion-ai/feynman runpod-computeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- runpod-compute
- description:
- Provision and manage GPU pods on RunPod for long-running experiments. Use when the user needs persistent GPU compute with SSH access, large datasets, or multi-step experiments.
RunPod Compute
Use runpodctl CLI for persistent GPU pods with SSH access.
Setup
brew install runpod/runpodctl/runpodctl # macOS
runpodctl config --apiKey=YOUR_KEY
Commands
| Command | Description |
|---------|-------------|
| runpodctl create pod --gpuType "NVIDIA A100 80GB PCIe" --imageName "runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04" --name experiment | Create a pod |
| runpodctl get pod | List all pods |
| runpodctl stop pod <id> | Stop (preserves volume) |
| runpodctl start pod <id> | Resume a stopped pod |
| runpodctl remove pod <id> | Terminate and delete |
| runpodctl gpu list | List available GPU types and prices |
| runpodctl send <file> | Transfer files to/from pods |
| runpodctl receive <code> | Receive transferred files |
SSH access
ssh root@<IP> -p <PORT> -i ~/.ssh/id_ed25519
Get connection details from runpodctl get pod <id>. Pods must expose port 22/tcp.
GPU types
NVIDIA GeForce RTX 4090, NVIDIA RTX A6000, NVIDIA A40, NVIDIA A100 80GB PCIe, NVIDIA H100 80GB HBM3
When to use
- Long-running experiments needing persistent state
- Large dataset processing
- Multi-step work with SSH access between iterations
- Always stop or remove pods after experiments
- Check availability:
command -v runpodctl
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