npx skills add ...
npx skills add runpod/runpod-plugins-official --skill companion-clis
Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS. Use the ComfyUI model-repair guide in runpod-templates instead when an imported ComfyUI workflow lacks model download metadata.
npx skills add runpod/runpod-plugins-official --skill companion-clis
Four CLIs commonly needed alongside Runpod. Each has its own credentials + command reference in reference/ — plus a one-time <cli>-setup.md for install (only opened if the CLI isn't installed). Load only the one the task needs, not all four.
If the request starts with an imported ComfyUI workflow whose model filenames lack verified URLs or hashes, route to the ComfyUI model-repair guide in runpod-templates. Return here when the exact Hugging Face repository/file is already known and the task is simply to download, cache, or bake that artifact.
| CLI | Use it to | Full reference |
|---|---|---|
hf (HuggingFace) | Download models from the Hub to cache/bake into images | reference/huggingface.md |
gh (GitHub) | Manage worker repos + cut releases (Hub indexes releases) | reference/github.md |
docker | Build/validate/push images to Docker Hub for Runpod to pull | reference/docker.md |
aws (S3) | Read/write network-volume storage over Runpod's S3 API | reference/aws.md |
Each requires credentials before use. Read the per-tool reference for auth steps and commands; install is a separate one-time <cli>-setup.md.
These CLIs are usually one step inside a larger job. For the whole job the verified example is in runpod/golden-paths/README.md — baking vs mounting a model (25), building a minimal image (22), or moving data to a network volume (07).
These are third-party CLIs on their own release trains, so <cli> --help is authoritative
for flags and subcommands — the references here cover the Runpod-specific usage and the
traps, not the tool's full surface. Check --help before reporting that one of them cannot do
something.
If you are on Windows, install WSL2 before proceeding — it gives you the native Linux environment all these CLIs target. In PowerShell as Administrator, then restart:
Afterward open the Ubuntu app to finish setup, then follow the Linux instructions in each reference.
Download models locally so they're cached for a Docker build/run. Auth and hf download recipes: reference/huggingface.md (install: reference/huggingface-setup.md).
hf CLI, not pip install huggingface_hub (that's the older huggingface-cli with different syntax).hf auth login, or export HF_TOKEN=hf_... (env var wins over saved token).Manage worker repositories and cut releases. Auth and commands: reference/github.md (install + SSH-key setup: reference/github-setup.md).
gh release create.ssh-keygen -t ed25519) registers with both GitHub (gh ssh-key add) and HuggingFace (paste in browser).Build, validate, and push images to Docker Hub. Credentials and commands: reference/docker.md (install: reference/docker-setup.md).
--platform=linux/amd64 — Runpod runs on x86 Linux.latest — latest doesn't track the newest push, so workers can silently pull the wrong image.Access network-volume storage over Runpod's S3-compatible API (bucket name = network volume ID). Credentials, region rules, and commands: reference/aws.md (install: reference/aws-setup.md).
user_...), secret = S3 API key (rps_...).runpodctl/REST/GraphQL creates them — if they're not already in ~/.aws/credentials/env and S3 access is needed, stop and ask the user to generate them (Settings > S3 API Keys).--region DATACENTER --endpoint-url https://s3api-DATACENTER.runpod.io/ (datacenter = the volume's DC, not an AWS region).