A comprehensive tool for managing GPU-accelerated cloud infrastructure, serverless endpoints, and network volumes on RunPod.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install runpodctl
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install runpodctl using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is RunPod CLI Manager?
The RunPod CLI skill provides a powerful interface for developers to orchestrate high-performance computing resources directly through an AI agent. This integration within the Openclaw Skills ecosystem allows for the seamless lifecycle management of GPU pods, including provisioning, scaling, and monitoring. Whether you are running large-scale model training or deploying inference APIs, this skill bridges the gap between your local environment and RunPod's cloud infrastructure.
By leveraging the runpodctl binary, users can programmatically search for official templates, check real-time GPU pricing, and manage persistent network volumes. This automation capability ensures that technical teams can focus on development while the agent handles the heavy lifting of infrastructure configuration and resource cleanup.
RunPod CLI Manager Use Cases
- Deploying on-demand NVIDIA RTX 4090 or A100 GPU pods for machine learning tasks.
- Scaling serverless endpoints dynamically based on workload requirements.
- Monitoring account balances and resource usage to prevent unexpected billing.
- Provisioning and attaching persistent network volumes for data-heavy applications.
- Searching and deploying official or custom templates for PyTorch and other AI frameworks.
How RunPod CLI Manager Works
- The skill utilizes the runpodctl binary located in the local bin directory to communicate with RunPod APIs.
- Users must first configure their environment by setting a valid API key through the setup workflow.
- The agent queries RunPod for available hardware resources, templates, or existing pod statuses.
- Commands are executed to create, start, or stop resources, with the agent providing real-time feedback and SSH access details.
- Post-deployment, the skill can be used to manage serverless workers or delete idle resources to optimize costs.
RunPod CLI Manager Setup
To get started with this skill in Openclaw Skills, ensure your API key is configured:
~/.local/bin/runpodctl config set --apiKey YOUR_API_KEY
You can find your API key at https://runpod.io/console/user/settings. Verify your account connection using:
~/.local/bin/runpodctl user
RunPod CLI Manager Data Schema & Taxonomy
The skill organizes RunPod resources into several logical categories for clear management:
| Resource Type | Description | Key Metadata |
|---|---|---|
| Pods | Dedicated GPU instances | Pod ID, GPU Type, SSH Info |
| Serverless | Auto-scaling endpoints | Endpoint ID, Worker Limits, Template |
| Network Volumes | Persistent storage | Size, Data Center ID, Volume ID |
| Templates | Pre-configured environments | Template ID, Official vs Community |
| Models | Deployed AI models | Model Path, Name |
RunPod CLI Manager Advanced Features
- Multi-region network volume management for low-latency data access.
- Granular serverless worker configuration, including min/max worker scaling.
- Detailed GPU availability and pricing lists to optimize compute expenditure.
- Direct model management for uploading and listing storage-based AI assets.
- Integrated account management for monitoring balance and user status within Openclaw Skills workflows.
SKILL.md
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