AGENT SKILL

runpod-companion-clis

Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS. Use runpod-build-template for complete template project authoring, and the ComfyUI model-repair guide in runpod-templates for imported workflows lacking model download metadata.

使用场景运维与部署
License 许可
Apache-2.0
最近更新
2026-11-12
Stars
★ 49
Forks
10

01 前言 Front Matter

name
runpod-companion-clis
license
Apache-2.0
metadata
{ "author": "runpod", "version": "2.1.0", "concepts": [ "container-image", "network-volume", "hub-repo" ] }
description
Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS. Use runpod-build-template for complete template project authoring, and the ComfyUI model-repair guide in runpod-templates for imported workflows lacking model download metadata.
compatibility
Linux, macOS, Windows

02 资源文件 8

03 SKILL.md 正文

Companion CLIs

For creating or improving a documented template project, start with runpod-build-template; return here for the selected CLI operation.

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.

Windows: choose the shell, then set up WSL2 if needed

Check existing native, WSL2 or remote tools before installing another copy. The Windows hf and gh setup recipes in this skill use WSL2 and their Linux installers. If choosing that route and WSL2 is missing, run this in PowerShell as Administrator, then restart if prompted:

wsl --install

Afterward open the Ubuntu app to finish setup, then follow the Linux instructions in each reference. Keep commands, credentials and paths in the selected environment. File authoring alone does not require installing these CLIs.

HuggingFace CLI

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).

  • Use the standalone hf CLI, not pip install huggingface_hub (that's the older huggingface-cli with different syntax).
  • Auth via hf auth login, or export HF_TOKEN=hf_... (env var wins over saved token).

GitHub CLI

Manage worker repositories and cut releases. Auth and commands: reference/github.md (install + SSH-key setup: reference/github-setup.md).

  • The Hub indexes releases, not commits — every Hub listing update needs a new gh release create.
  • One SSH key (ssh-keygen -t ed25519) registers with both GitHub (gh ssh-key add) and HuggingFace (paste in browser).

Docker

Build, validate, and push images to Docker Hub. Credentials and commands: reference/docker.md (install: reference/docker-setup.md).

  • Always build --platform=linux/amd64 — Runpod runs on x86 Linux.
  • Always use explicit semantic tags; never latest — latest doesn't track the newest push, so workers can silently pull the wrong image.
  • Docker Hub auth uses a personal access token, not your password. For private images, register the credential once in Console → Container Registry Settings.

AWS CLI

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).

  • Runpod's S3 API, not AWS: access key = Runpod user id (user_...), secret = S3 API key (rps_...).
  • S3 API keys are Console-only. No 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).
  • Every command needs --region DATACENTER --endpoint-url https://s3api-DATACENTER.runpod.io/ (datacenter = the volume's DC, not an AWS region).
  • For large/many-file transfers with resume, prefer runpodctl network-volume upload/download (v3.1.0+, same S3 API keys; see the runpodctl skill). On an older runpodctl, see reference/aws.md → optional resumable volume transfers.

04 同仓库更多 Skills

runpod
runpod/runpod-plugins-official

runpod-vllm

Serve a Hugging Face model with vLLM on Runpod (Hub worker-vllm queue endpoint, load-balancing endpoint on a vllm/vllm-openai image, or a Pod), sized, CUDA-pinned and verified with a real OpenAI client. Use for any "deploy/serve/test this LLM with vLLM" request or a vLLM deployment that will not boot; defer to runpod-serverless-deploy for non-vLLM engines and runpod-endpoint-ops for an endpoint that already works.

运维与部署图像与多媒体
★ 49
runpod
runpod/runpod-plugins-official

runpodctl

Runpod CLI for managing GPU/CPU workloads from the terminal — pods, serverless endpoints, templates, network volumes, Hub deploys, models, SSH, and file transfer (send/receive, network-volume upload/download). Use for terminal/CI/scripting, SSH setup, `doctor`, or when the Runpod MCP tools are not connected; prefer runpod-mcp for structured tool calls in an MCP-enabled session.

Skill与智能体开发
★ 49
runpod
runpod/runpod-plugins-official

runpod

Start here when the Runpod lane is unclear: picks the skill for a task (runpod-mcp and the journey playbooks it routes to, such as runpod-serverless-deploy, runpod-pod-deploy, runpod-pod-doctor and runpod-cost-audit; runpodctl, runpod-flash, runpod-companion-clis, runpod-usage, runpod-templates, runpod-build-template, runpod-migrate) and indexes two dozen end-to-end worked examples (golden paths). Use it before any multi-step or provisioning task, even when the lane is clear, to check for a matching golden path; for the Runpod MCP tools, runpod-mcp owns the answer contract, connection and tool rules.

Skill与智能体开发
★ 49
runpod
runpod/runpod-plugins-official

runpod-usage

Explain Runpod architecture and design choices: Pods versus Serverless, GPU/VRAM, image construction, storage, networking, and workload development. For creating or improving a complete template project use runpod-build-template; execute infrastructure with runpodctl/runpod-mcp and code-first work with runpod-flash.

Skill与智能体开发
★ 49
runpod
runpod/runpod-plugins-official

runpod-templates

Find, use, and troubleshoot official Runpod Pod templates, including ComfyUI workflow model metadata repair. For creating or optimizing Dockerfiles and application template projects use runpod-build-template; deploy through runpod-mcp or runpodctl.

Skill与智能体开发
★ 49
runpod
runpod/runpod-plugins-official

runpod-serverless-deploy

Bring any source (a HuggingFace repo, a ComfyUI workflow, a Hub release, a custom container image, a GGUF repo, a Civitai asset, or a community template) to a proven Runpod Serverless endpoint: pick the engine for the modality and model format, size the GPU from the model's real metadata, provision, warm, prove it with one real job, and hand back the URL with what it bills. Deploys an existing source through the Runpod MCP tools; code-first Python deploys (the Flash dev loop) are out of scope. Use when the user wants a model or worker served on Runpod Serverless, whatever the modality: text generation, speech-to-text or text-to-speech, image or video generation, embeddings, document processing, or their own handler image. For an LLM served with vLLM (Hub worker-vllm, a vllm/vllm-openai image, or a vLLM Pod), defer to runpod-vllm.

运维与部署图像与多媒体
★ 49