RunPod Cloud GPU
Skill Verified ActiveCloud GPU processing via RunPod serverless. Use when setting up RunPod endpoints, deploying Docker images, managing GPU resources, troubleshooting endpoint issues, or understanding costs. Covers all 5 toolkit images (qwen-edit, realesrgan, propainter, sadtalker, qwen3-tts).
To enable users to leverage cloud GPU processing through RunPod serverless for deploying and managing AI models and Docker images efficiently.
Features
- Setup and deployment of RunPod endpoints
- Management of GPU resources and Docker images
- Troubleshooting endpoint issues
- Detailed RunPod API reference (GraphQL and REST)
- Cost understanding and optimization guidance
Use Cases
- When setting up RunPod endpoints for AI model deployment
- Deploying custom Docker images to cloud GPUs
- Managing GPU resources and scaling configurations
- Troubleshooting issues with RunPod endpoints
- Understanding and optimizing cloud GPU costs
Non-Goals
- Directly running AI models locally
- Managing local machine hardware
- Providing a general-purpose cloud management tool
- Replacing the RunPod web console for all tasks
Workflow
- Add RunPod API key to .env
- Run `--setup` for specific tools (image_edit, upscale, etc.)
- Configure endpoint workers (min/max, idleTimeout)
- Manage endpoints via RunPod dashboard or API reference
- Troubleshoot common issues (cold start, OOM, worker availability)
Prerequisites
- RunPod account and API key
- Cloudflare R2 credentials (optional, for file transfer fallback)
- Python 3.9+ recommended
Installation
npx skills add digitalsamba/claude-code-video-toolkitRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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