Deploy App logo

Deploy App

Organization
serversathome
deploy-app

Stand up a NEW containerized app or compose stack on a docker host. Use whenever the user wants to deploy, install, add, spin up, or "set up" a new container or stack (Jellyfin, Radarr, Sonarr, qBittorrent, Immich, a new arr app, a dashboard, etc.), or add a service to an existing stack. For fixing or operating an app that already exists, use docker-stack-ops instead; for updating images, use patch-management.

Overview

Publisherserversathome
Repositoryhomelabhero
Skill namedeploy-app
Stars
104
Forks
4
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by serversathome on GitHub. Read the source before you install it.

Installation

Install the Deploy App AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/serversathome/homelabhero/tree/main/ops/.claude/skills/deploy-app
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/serversathome/homelabhero.git /tmp/homelabhero
mkdir -p .claude/skills
cp -r /tmp/homelabhero/ops/.claude/skills/deploy-app .claude/skills/deploy-app
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deploy App in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deploy App on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deploy App is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Deploy a new app / stack

Adding or starting containers is state-changing: propose the compose, confirm, then bring it up.

First, learn THIS homelab's layout - do not assume

Read infra/docker-stacks.md for the host alias, which compose model is in use, the pool name, and the config/media paths. There are a few common structures and this homelab may use any of them:

  • Single compose file - one /mnt/<pool>/docker/docker-compose.yml holding every service; per-app config at /mnt/<pool>/configs/<app>; a single /mnt/<pool>/media mount.
  • Per-stack (Dockge) - /opt/stacks/<stack>/compose.yaml, with the app's volumes kept inside the stack directory.
  • Portainer - stacks pasted/edited in the Portainer UI.

If infra/docker-stacks.md does not say, ask which one before writing paths.

A common convention worth knowing (the "servers@home" / TrueNAS *arr layout)

This is one widely used structure, not the only correct one - match what the box actually uses:

  • One media dataset with subdirectories movies, tv, downloads (a single mount so the *arr apps can hardlink instead of copying); one configs dataset with a sub-dataset per app.
  • Volumes: /mnt/<pool>/configs/<app>:/config and /mnt/<pool>/media:/media.
  • PUID/PGID: 568/568 on TrueNAS (the apps group); commonly 1000/1000 on Ubuntu/Proxmox hosts. Always set TZ. Dataset perms 770, no spaces or capitals in names.
  • Images: lscr.io/linuxserver/<app> or ghcr.io/hotio/<app>; restart: unless-stopped; ports as HOST:CONTAINER.

Service template:

services:
  radarr:
    image: lscr.io/linuxserver/radarr:latest
    container_name: radarr
    environment:
      - PUID=568
      - PGID=568
      - TZ=America/New_York
    volumes:
      - /mnt/tank/configs/radarr:/config
      - /mnt/tank/media:/media
    ports:
      - 7878:7878
    restart: unless-stopped

Download clients behind a VPN (killswitch)

Route a download client through a VPN container so it cannot leak the home IP. The client defines no ports of its own - the ports go on the VPN container, and the client joins its network. No depends_on is needed.

services:
  gluetun:                       # or a linuxserver/wireguard container
    image: qmcgaw/gluetun
    cap_add: [NET_ADMIN]
    environment:
      - VPN_SERVICE_PROVIDER=...
      - VPN_TYPE=wireguard
      - WIREGUARD_PRIVATE_KEY=...
      - FIREWALL_VPN_INPUT_PORTS=6881
    ports:
      - 8080:8080               # the qBittorrent WebUI port lives HERE
      - 6881:6881/tcp
  qbittorrent:
    image: lscr.io/linuxserver/qbittorrent:latest
    network_mode: "service:gluetun"   # shares the VPN's network stack
    # NO ports: block here

After it's up, confirm the egress IP is the VPN's, not home (see the gluetun rule in docker-stack-ops).

Workflow

  1. Confirm app, image, ports, and the layout/paths from infra/docker-stacks.md.

  2. Create the config dir/dataset with the right owner and 770 (on TrueNAS, create the dataset in the UI or mkdir under the configs dataset).

  3. Add the service block (single file) or create the stack dir (Dockge/Portainer). Show the user the compose and confirm before applying.

  4. Bring it up (state-changing):

    hh run <dockerhost> "cd <composedir> && docker compose up -d <service>"
  5. Verify: docker ps, docker logs --tail 80 <name>, and the WebUI on its port.

  6. External access wanted? Add it to the Cloudflare Tunnel (Zero Trust -> the tunnel -> add public hostname) or the reverse proxy - see network-diag and infra/. Do not publish anything externally without an explicit go-ahead.

Record the new app in infra/docker-stacks.md so the picture stays current.

Frequently asked questions

What does the Deploy App AI skill do?

Stand up a NEW containerized app or compose stack on a docker host. Use whenever the user wants to deploy, install, add, spin up, or "set up" a new container or stack (Jellyfin, Radarr, Sonarr, qBittorrent, Immich, a new arr app, a dashboard, etc.), or add a service to an existing stack. For fixing or operating an app that already exists, use docker-stack-ops instead; for updating images, use patch-management.

Why use Deploy App on TypingMind?

Because you install it once and use it with any model. Deploy App is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deploy App in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/serversathome/homelabhero/tree/main/ops/.claude/skills/deploy-app. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deploy App?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deploy App?

As many as you like. As long as a model supports skills, you can use Deploy App with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deploy App AI skill free?

It is published on GitHub by serversathome. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇