runpod

name	runpod
description	Start here for any Runpod task — running GPU/CPU pods, deploying serverless endpoints, templates, network volumes, building images, or understanding how Runpod works. Routes the request to the right Runpod skill (runpod-mcp, runpodctl, flash, companion-clis, or runpod-usage). Use when it is unclear which Runpod skill applies.
metadata	{"author":"runpod","version":"1.1.0"}
license	Apache-2.0

Runpod (router)

The entrypoint for the Runpod skills. This skill does no work itself — it picks the right lane and hands off. Read the matching skill's SKILL.md next.

The lanes

Lane	Use it for
runpod-mcp	Manage infra (pods, endpoints, jobs, templates, volumes, registries, catalog, billing) via structured tool calls — when the Runpod MCP tools are connected in this session.
runpodctl	Manage the same infra from a terminal/CI/script, plus the things only the CLI does: Hub browse/deploy, send/receive file transfer, SSH keys, doctor setup, model cache.
flash	Write Python that runs on Runpod serverless — @remote/@Endpoint functions, flash dev hot-reload, flash deploy. Code-first, not infra management.
companion-clis	Prerequisite artifacts: download a model (hf), build/push an image (docker), repos/releases (gh), move data to a network volume over S3 (aws).
runpod-usage	Understand how Runpod works before acting — pods vs serverless, building a container, storage, GPU selection, gotchas. Knowledge only.

First run — check auth before the first infra action

Infra tasks (pods, endpoints, jobs, volumes) need a working control plane — the Runpod MCP or runpodctl. Don't start and discover mid-task that nothing's set up: check first, and if it isn't, help the user set up rather than limping on a partial fallback.

Check (credential resolution order: RUNPOD_API_KEY env → .env → ~/.runpod/config.toml):

runpodctl user            # succeeds ⇒ a key is set and valid

Plus, in Claude Code, /mcp should show runpod Connected.

Rule: get a key first — do not default to MCP OAuth. The reason: one RUNPOD_API_KEY unlocks every tool — it authenticates runpodctl + flash + the hosted MCP (as --header "Authorization: Bearer $RUNPOD_API_KEY"). The MCP's "Sign in with Runpod" OAuth auths the MCP alone — the CLIs stay blocked, so you hit a wall on any CLI-only task (Hub, send/receive, SSH, doctor, model cache/Model Repository, CPU endpoints). ⚠️ OAuth-only is a half-setup. If nothing's set up, stop and get a key, in order:

1. flash login — browser OAuth that saves a real key to ~/.runpod/config.toml (runpodctl
   • flash read it; reuse it for the MCP Bearer). One step, unlocks all. Human-only.
2. export RUNPOD_API_KEY=… (https://console.runpod.io/user/settings) — same full unlock; best for headless agents.
3. MCP OAuth only (/mcp → Sign in) — last resort, MCP-only work; CLIs stay unauthed.

Then: if a lane already works, use it — but if only the MCP is OAuth'd, still get a key before any CLI-only step. Missing a CLI? curl -sSL https://cli.runpod.net | bash (runpodctl) · uv tool install runpod-flash (flash) · npx @runpod/mcp-server@latest add (MCP). Full setup: runpod-usage/reference/getting-started.md.

How to route

1. Conceptual question, or an unmade design choice (serverless vs pod? which GPU? bake the model or mount a volume?) → read runpod-usage first, then continue with the answer.
2. Write/iterate/ship your own code on Runpod GPUs → flash.
3. Produce an artifact (download a model, build+push an image, create a repo release, sync data to a volume) → companion-clis.
4. Manage infrastructure (create/list/update/delete pods, endpoints, templates, volumes; list GPUs/data centers; run a serverless job; billing):
   • Capability only the CLI has — Hub, send/receive, SSH keys, doctor, model cache → runpodctl.
   • Otherwise, if the Runpod MCP tools are connected in this session (create-pod, list-endpoints, … are available) → runpod-mcp.
   • Otherwise (shell-only agent, no MCP) → runpodctl.

runpod-mcp vs runpodctl (the overlap)

Both drive the same Runpod API, so they overlap on infra CRUD. Choose by capability first, environment second:

• MCP wins on convenience for simple, structured operations — reads and basic CRUD — when its tools are connected (typed params, no shell quoting).
• runpodctl takes over when an operation needs a capability MCP lacks — even if MCP is connected — and is the only option for a shell-only agent or when the user wants a reproducible command.

Capability matrix (pick the preferred lane per operation):

Operation	Preferred lane	Why
List/get anything; start/stop/restart/delete a pod; simple CRUD on endpoints, templates, volumes, registries; catalog; billing	runpod-mcp if connected, else runpodctl	Simple structured ops — MCP is typed and convenient
Create a simple pod (one image + one GPU)	runpod-mcp if connected, else runpodctl	Both handle it
Create a pod from a template, a CPU pod, or with a multi-GPU priority list	runpodctl	MCP's v2 create-pod has no templateId, requires an image, and narrows to a single GPU type
Deploy from the Hub	runpodctl	MCP has no Hub tools
File transfer (send/receive), SSH keys/info, doctor setup, model cache	runpodctl	MCP has no tool for these
Invoke a serverless job (run/runsync/status/stream)	runpod-mcp if connected, else runpodctl	MCP has first-class job tools

Rule of thumb: default to MCP for the easy stuff, hand off to runpodctl the moment an op needs a flag/feature MCP doesn't expose.

Deploying a workload (the golden loop)

For any "get running on Runpod" task, follow the development loop in runpod-usage/reference/development-loop.md: decide pod vs serverless → provision → set up (only if from-scratch) → verify → deliver → cost-guard + teardown. Two rules bind within it:

• Prefer a prebuilt template / Hub worker over building an image from scratch.
• Before delivering, verify the workload with a real request from outside the pod/endpoint — a "Running"/"ready" status does not mean it is serving.

It branches to two sub-loops:

• Service you open at a URL (Ollama, ComfyUI, dev box) → runpod-usage/reference/pod-workflows.md (ports + env + volume at creation, SSH-exec install, bind 0.0.0.0, poll the proxy URL). Execute in the runpodctl lane.
• Request/response API that scales to zero (Whisper, inference) → runpod-usage/reference/endpoint-workflows.md (Hub worker vs flash vs custom image; invoke /run//runsync; poll job status).

Worked examples (golden paths)

Two dozen end-to-end scenarios (nearly all live-verified) live in ./golden-paths/README.md — the yardstick for "can an agent finish the job", with real commands + observed output to copy from. When a task matches one, open its golden path first instead of re-deriving it:

Want to…	Golden path
Run a server (Ollama/ComfyUI) on a pod at a URL	01, 02
Deploy a serverless model endpoint (Hub / flash / custom image)	03, 05
Serve a HuggingFace model without baking it in or a volume (host-cached)	20 — model caching (--model-reference)
Call a ready hosted model (no deploy)	11 — Public Endpoints
Fine-tune, then serve the result	04, 08
Interactive dev box (SSH / VS Code)	06
Move data pod → volume → serverless	07
Custom serverless when flash isn't enough (dual-mode image dev loop)	09
Build a minimal image for a target (pod vs serverless queue)	22 (pod), 23 (queue); concepts in building-images
Decide what to bake into the image vs mount on a network volume	25 — bake vs mount
High availability / multi-region serverless (multi-volume + data sync)	10, 19 (3-region)
Stream output incrementally (/stream)	12
Tune autoscaling / raise per-worker throughput	13 (autoscaling), 18 (concurrency)
Load-balancing / HTTP-server or WebSocket worker	14 (LB), 17 (WebSocket)
Get notified on job completion (push, not poll)	16 — webhooks
Check health / debug a failing endpoint	15 — monitor & debug

Multi-lane tasks

Sequence is always understand → produce artifacts → manage infra → verify, because infra can only reference artifacts that already exist. Keep each step in one lane, and switch lanes at credential boundaries.

Example — "deploy openai/gpt-oss-20b to a serverless endpoint":

1. runpod-usage — serverless vs pod, GPU tier for 20B, bake vs mount vs cache.
2. companion-clis — hf download …, docker build --platform=linux/amd64 …, docker push.
3. runpod-mcp or runpodctl — create the endpoint referencing the image + GPU pool.
4. Same infra lane — invoke the endpoint / check status to verify.

Auth

Everything is one key: RUNPOD_API_KEY (https://console.runpod.io/user/settings). Each lane just makes that key resolvable — runpodctl doctor, flash login, MCP stdio env var, or MCP hosted "Sign in with Runpod" (OAuth, no key on disk). Companion CLIs use their own credentials (HuggingFace token, GitHub auth, Docker Hub PAT, Runpod S3 keys for aws) — do not reuse RUNPOD_API_KEY for those.