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/companion-clis

Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS.

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socket
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$ npx -y skills add gaelic-ghost/socket --skill companion-clis --agent claude-code

SKILL.md

companion-clis.SKILL.md
name: companion-clis
description: Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS.
allowed-tools: Bash(hf:*), Bash(gh:*), Bash(docker:*), Bash(aws:*), Bash(ssh-keygen:*), Bash(ssh-add:*), Bash(ssh-agent:*)
compatibility: Linux, macOS, Windows
metadata:
  author: runpod
  version: "1.0"
license: Apache-2.0

Companion CLIs

Four CLIs commonly needed alongside Runpod: HuggingFace (`hf`), GitHub (`gh`), Docker (`docker`), and AWS (`aws`). Each requires credentials before use.

Windows: Install WSL2 First

If you are on Windows, install WSL2 (Windows Subsystem for Linux) before proceeding. WSL2 gives you a native Linux environment on Windows, which all these CLIs are designed for. Run in PowerShell as Administrator, then restart:

wsl --install

This installs WSL2 with Ubuntu by default. After restarting, open the Ubuntu app to complete setup (create a Linux username and password). From that point on, follow the **Linux** instructions throughout this skill.

HuggingFace CLI

The HuggingFace CLI (`hf`) is used to download models from the Hub to your local machine so they are cached and available when you build and run the Docker container. For example, to deploy `openai/gpt-oss-20b` to a Runpod serverless endpoint: download the model locally first, build a Docker image that includes or mounts it, validate the container locally, then push the image to Docker Hub for Runpod to pull.

Install

# macOS / Linux (standalone installer — recommended)
curl -LsSf https://hf.co/cli/install.sh | bash

# macOS (Homebrew)
brew install hf

# Windows (WSL2): use the Linux standalone installer above

> **Note:** `pip install huggingface_hub` installs the older Python CLI (`huggingface-cli`), which uses different command syntax. The commands below are for the standalone `hf` CLI.

Credentials

Get a token at https://huggingface.co/settings/tokens. Use **write** access for uploading; **read** access is sufficient for downloading public or gated models.

# Option 1: interactive login (saves token to ~/.cache/huggingface/token, optionally to git credential store)
hf auth login

# Option 2: non-interactive (pass token directly, useful in scripts and pod start commands)
hf auth login --token $HF_TOKEN --add-to-git-credential

# Option 3: environment variable (takes precedence over saved token; to revert, unset the variable)
export HF_TOKEN=hf_...
hf auth whoami      # confirm auth and org memberships
hf auth logout      # delete all locally stored tokens

Key Commands

# Download a model to a local directory (use --local-dir to control where it lands)
hf download openai/gpt-oss-20b --local-dir ./models/gpt-oss-20b
hf download meta-llama/Llama-3.1-8B --local-dir ./models/llama-3.1-8b

# Download a single file from a model repo
hf download openai/gpt-oss-20b config.json --local-dir ./models/gpt-oss-20b

# Download with glob filters (e.g. only safetensors weights, skip fp16 variants)
hf download stabilityai/stable-diffusion-xl-base-1.0 \
  --include "*.safetensors" --exclude "*.fp16.*" \
  --local-dir ./models/sdxl

# Download a specific revision (commit hash, branch, or tag — append --revision REF)
hf download openai/gpt-oss-20b --revision v1.0 --local-dir ./models/gpt-oss-20b

Troubleshooting

# Increase download timeout on slow connections (default: 10s)
export HF_HUB_DOWNLOAD_TIMEOUT=30

---

GitHub CLI

The GitHub CLI (`gh`) is used to manage repositories for Runpod serverless workers. This includes cloning repos into local Docker containers for testing, versioning source code so changes can be tracked and shared with teammates or collaborators, and creating GitHub releases that publish listings to the Runpod Hub. The Hub indexes releases — not commits — so every deployment update requires a new release.

Install

# macOS
brew install gh

# Linux (Debian/Ubuntu)
(type -p wget >/dev/null || (sudo apt update && sudo apt install wget -y)) \
  && sudo mkdir -p -m 755 /etc/apt/keyrings \
  && out=$(mktemp) && wget -nv -O$out https://cli.github.com/packages/githubcli-archive-keyring.gpg \
  && cat $out | sudo tee /etc/apt/keyrings/githubcli-archive-keyring.gpg > /dev/null \
  && sudo chmod go+r /etc/apt/keyrings/githubcli-archive-keyring.gpg \
  && echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" \
     | sudo tee /etc/apt/sources.list.d/github-cli.list > /dev/null \
  && sudo apt update && sudo apt install gh -y

# Linux (Alpine)
apk add github-cli

# Windows (WSL2): use the Linux (Debian/Ubuntu) installer above

SSH Keys

An SSH key identifies your machine as authentic to remote services. Generate one key and register the public key with each service that requires it — GitHub (via `gh`) and HuggingFace (via browser).

**Generate a key**

ssh-keygen -t ed25519 -C "your_email@example.com"
# Saves to ~/.ssh/id_ed25519 (private) and ~/.ssh/id_ed25519.pub (public)
# Press Enter to accept the default path; set a passphrase or leave blank

**Add the key to the SSH agent**

# macOS
eval "$(ssh-agent -s)"
ssh-add --apple-use-keychain ~/.ssh/id_ed25519

# macOS — also add to ~/.ssh/config so the key loads automatically on login.
# Create the file if it doesn't exist, and add these lines:
#
#   Host *
#     AddKeysToAgent yes
#     UseKeychain yes
#     IdentityFile ~/.ssh/id_ed25519

# Linux
eval "$(ssh-agent -s)"
ssh-add ~/.ssh/id_ed25519

# Windows (WSL2): use the Linux instructions above

Credentials

# Interactive login — when prompted, select SSH as the git protocol
gh auth login

# Verify auth
gh auth status

**Register the public key with each service**

# GitHub — upload via gh CLI (requires auth above to be completed first)
gh ssh-key add ~/.ssh/id_ed25519.pub --title "my-machine"

# HuggingFace — paste contents of
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Questions about companion-clis.

Which coding agents can use companion-clis?
companion-clis installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.