By runpod
Official Runpod agent skills and MCP server — run Runpod tasks (pods, serverless, templates, volumes) with runpodctl and flash, plus conceptual guidance and worked golden paths.
Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS.
runpod-flash — code-first serverless: write Python locally, run it on remote Runpod GPUs/CPUs with `flash dev` (hot-reload + live worker logs), then `flash deploy`. Use for @Endpoint/@remote functions, resource config, and debugging flash deployments. For CLI-only infra management use runpodctl or runpod-mcp.
Manage Runpod infrastructure — pods, serverless endpoints, jobs, templates, network volumes, container-registry auth, GPU/CPU catalog, and billing — via the Runpod MCP server's structured tool calls. Use when the Runpod MCP tools (create-pod, list-endpoints, …) are connected in this session, or to connect them (hosted OAuth or local npx). Prefer this over runpodctl for plain infra CRUD when MCP is available; use runpodctl for the terminal, Hub deploys, file transfer, or SSH setup.
How Runpod works and how to work it — pods vs serverless, GPU/VRAM selection, storage, building a container, networking, plus the agentic pod development loop (provision → ssh-exec → set up → poll readiness) and on-pod install hygiene (uv/apt). Use to answer "how does X work", "which GPU", "how do I build a container", or "how do I stand up a workload on a pod". Guidance, not a tool — execute with runpodctl, runpod-mcp, or flash.
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.
External network access
Connects to servers outside your machine
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The official plugin marketplace of skills for AI agents to manage GPU workloads on Runpod —
pods, serverless endpoints, jobs, templates, and volumes — via the Runpod MCP
server, runpodctl, and flash.
This repo ships one plugin, runpod, that bundles a router
plus six skills, the hosted Runpod MCP server config, and worked golden paths.
Compatibility: installs as a native plugin in Claude Code, Codex, Gemini, and opencode (with auto-update), and as skills via skills.sh for Cursor, Copilot, Windsurf, Cline, and 17+ other agents — all from the same manifest.
Install — works with every agent (Claude Code, Codex, Cursor, Copilot, Windsurf, Cline, Gemini, opencode, +more). The first line is all you need; drop any others you won't use:
npx skills add runpod/runpod-plugins-official # the skills — any agent (essential)
curl -sSL https://cli.runpod.net | bash # runpodctl — infra CLI (or: brew install runpod/runpodctl/runpodctl)
uv tool install runpod-flash # flash — deploy your own code (optional; or: pip install runpod-flash)
npx @runpod/mcp-server@latest add # hosted MCP server — guided setup, OAuth (optional)
Then authenticate — one API key covers everything (from the console):
export RUNPOD_API_KEY=<key> # current shell only — add to ~/.zshrc (or ~/.bashrc) to persist
# or, to save it permanently:
runpodctl doctor # prompts for the key and writes ~/.runpod/config.toml
Now just ask in plain English — the skill drives the tools for you:
"list my Runpod endpoints" · "spin up an A100 pod" · "deploy this handler to serverless"
That's it. Notes:
docker/gh/hf/aws) install separately, per task — see
companion-clis.Same repo, one manifest — pick your agent below. Every route installs the same router + six skills, plus a hosted Runpod MCP server for control-plane tools. Then authenticate.
/plugin marketplace add runpod/runpod-plugins-official
/plugin install runpod@runpod
/reload-plugins
Installing also wires up the hosted Runpod MCP server (via the bundled
.mcp.json) — no separate MCP setup. Authenticate it:
/mcp → runpod → Sign in with Runpod (OAuth; no key on disk).
Verify: /plugin shows Runpod under Installed; then ask "list my Runpod
endpoints" — it should call the MCP list-endpoints tool.
codex plugin marketplace add https://github.com/runpod/runpod-plugins-official.git
codex /plugins # → open the "Runpod" marketplace tab → Runpod → Install (reload if prompted)
Manage the source: codex plugin marketplace list · … upgrade runpod · … remove runpod.
Codex may not auto-wire the bundled MCP — if the runpod MCP tools don't appear
after install, add the hosted server manually:
codex mcp add runpod --transport http https://mcp.getrunpod.io/
Verify: ask a Runpod task (the skill/router should answer); once the MCP is added, "list my Runpod endpoints" should call a tool.
Install the skills via skills.sh (reads the same
.claude-plugin/marketplace.json):
npx skills add runpod/runpod-plugins-official
# just one skill:
npx skills add https://github.com/runpod/runpod-plugins-official/tree/main/plugins/runpod/skills/runpodctl
Gemini can also install natively via the bundled
gemini-extension.json (see your client's
extension docs). For these clients, add the MCP server yourself — the guided
installer configures most agents:
npx @runpod/mcp-server@latest add # detects your agent + sets up the hosted MCP (OAuth)
See runpod-mcp/SKILL.md for hosted vs
local (stdio) MCP setup and all client options.
Auth is needed in two places, and one Runpod API key covers both:
| What | What it needs | How |
|---|---|---|
runpodctl + flash (the CLIs) | RUNPOD_API_KEY (env or saved) | export RUNPOD_API_KEY=<key>, or runpodctl doctor to save it |
| Hosted Runpod MCP server (infra tools) | OAuth or the same key | /mcp sign-in, or pass the key as a Bearer header |
Fastest path — one key, everything:
npx claudepluginhub runpod/runpod-plugins-official --plugin runpodSkyPilot agent skill for launching cloud VMs, Kubernetes pods, and Slurm jobs across 25+ clouds
Claude Code skill pack for CoreWeave (23 skills). Community-contributed; not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
Machine learning training and inference pipeline using cloud GPUs (Modal, Lambda Labs, RunPod) with HuggingFace ecosystem - no local GPU required
Cloudflare Workers AI for serverless GPU inference. Use for LLMs, text/image generation, embeddings, or encountering AI_ERROR, rate limits, token exceeded errors.
Expert Modal.com serverless cloud platform system with comprehensive Modal 1.0 SDK (May 2025) features, GPU functions (T4/L4/A10G/L40S/A100/H100/H200/B200), autoscaler configuration, @modal.concurrent/@modal.batched decorators, Sandboxes for isolated code execution, CloudBucketMount for S3/GCS, and production deployment patterns. PROACTIVELY activate for: (1) ANY Modal.com task, (2) GPU configuration with fallbacks and multi-GPU, (3) Autoscaler settings (min/max/buffer containers, scaledown_window), (4) Web endpoints (FastAPI, ASGI, WSGI, custom servers), (5) @modal.concurrent for request concurrency, (6) @modal.batched for dynamic batching, (7) Sandboxes for untrusted code execution, (8) Scheduling (Cron with timezone, Period), (9) Storage (Volumes with commit(), Dict with TTL, Queue, CloudBucketMount), (10) Parallel processing (.map(), .starmap(), .spawn(), .for_each()), (11) Container lifecycle (@modal.enter, @modal.method, @modal.exit), (12) Image building (uv_pip_install, run_function for model downloads), (13) Secrets and environment management, (14) Deployment and CI/CD with GitHub Actions, (15) Cost optimization and 2025 pricing. Provides: Modal 1.0 stable API patterns, GPU selection guide with per-second pricing, autoscaler tuning strategies, concurrency and batching for ML inference, Sandbox security patterns, CloudBucketMount for external data, complete CLI reference, debugging workflows, and production-ready configurations.
Agent Skills for Together AI platform — inference, training, embeddings, audio, video, images, function calling, and infrastructure