runpod-skill

A Claude Code skill for deploying and managing GPU/CPU infrastructure on RunPod.

Built for any AI agent that needs to programmatically launch pods, manage storage, and control compute resources — not tied to a specific workflow or use case.

What it covers

• Pod lifecycle — create, list, start, stop, restart, reset, update, terminate
• GPU selection — full GPU table with IDs, VRAM, and fallback chains by use case
• Spot instances — podRentInterruptable via GraphQL with bid pricing
• Network volumes — persistent storage independent of pods (CRUD + attach)
• Templates — reusable pod configurations via REST API
• Connectivity — SSH, HTTP proxy, TCP ports with timeout warnings
• Billing — cost queries for pods, endpoints, and volumes
• CLI — runpodctl reference for common operations
• Error handling — retry patterns for GPU stock-outs, zero-GPU restarts, volume mismatches

What it gets right that others get wrong

Common mistake	This skill
Wrong REST URL (api.runpod.io)	Correct: rest.runpod.io/v1
region: "us-west-1" (doesn't exist)	Correct: dataCenterIds: ["US-TX-3"]
env format same for both APIs	Documents the difference: REST = object, GraphQL = array of {key, value}
gpuTypeId vs gpuTypeIds confusion	Flags singular (GraphQL) vs plural array (REST)
Container disk is persistent (it isn't)	Explicit ephemeral/persistent/network storage model

Install

Copy the SKILL.md file into your Claude Code skills directory:

# If you have a skills directory configured
cp SKILL.md ~/.claude/skills/runpod/SKILL.md

# Or place it alongside your project
cp SKILL.md .claude/skills/runpod/SKILL.md

The skill activates when prompts mention RunPod, GPU cloud, launching pods, or related terms.

Prerequisites

• A RunPod account with API access
• API key set as RUNPOD_API_KEY environment variable

API coverage

The skill standardizes on the REST API (rest.runpod.io/v1) for all CRUD operations and documents GraphQL (api.runpod.io/graphql) for three things REST can't do:

1. GPU availability queries with stockStatus filtering
2. Runtime metrics (GPU utilization, container CPU/memory)
3. Spot instance deployment (podRentInterruptable)

License

MIT