Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Updated scripts/mount_pod.sh to address warning about stricthostchecking. SSH keys for known pods will now be stored in ~/.runpod/ssh/known_hosts on first connect and verified thereafter.
What This Skill Does
Manages RunPod GPU cloud instances from the command line. Handles pod creation, lifecycle control, SSH access, and remote filesystem mounting via SSHFS. Works through the runpodctl CLI and RunPod's proxy infrastructure.
Handles SSH key management and SSHFS mounting that RunPod's web console doesn't support, letting agents manage the full pod lifecycle without leaving the terminal.
When to use it
- Spinning up an RTX 4090 pod for a model training run
- Mounting a remote pod's filesystem locally to edit scripts
- Checking which GPU pods are active and their current status
- Accessing a Jupyter or ComfyUI web service running on a pod
- Sending a dataset file to a pod before starting a job
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Create an RTX 4090 pod with a 100GB workspace and connect to it via SSH
- 1Run runpodctl create pod with --gpuType 'NVIDIA GeForce RTX 4090', --volumeSize 100, and --volumePath '/workspace'
- 2Run runpodctl get pod to retrieve the new pod's ID
- 3Run runpodctl start pod <id> to start the pod
- 4Run runpodctl ssh connect <id> to get the SSH connection command
SSH command printed: ssh -p <port> root@<ip> -i ~/.ssh/runpod_key
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.