Create Docker Container

SkillCloud & infra

Add a Docker container to a Datagrok package with Dockerfile and config

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Create Docker Container skill

What this skill tells your AI

The instructions your AI receives, as published by datagrok-ai/public in .claude/skills/create-docker-container/SKILL.md and read by ahel’s review.

Help the user add a Docker container to their Datagrok package so it can be built, deployed, and accessed via the platform.

Usage

/create-docker-container [package-name]

Instructions

Follow these steps to create a Docker container for a Datagrok package:

1. Create the Dockerfile

Create a dockerfiles/ folder inside the package root and add a Dockerfile there.

  • The container MUST expose exactly one port using EXPOSE $PORT (only one EXPOSE is allowed).
  • The application inside the container MUST embed an HTTP server listening on that port.
  • Follow Docker best practices: minimal base images, multi-stage builds, small layers.

Example structure:

packages/MyPackage/
  dockerfiles/
    Dockerfile
    container.json   (optional)
  src/
  package.json

2. Create container.json (optional)

Place container.json in the same directory as the Dockerfile. If omitted, defaults are used.

{
  "cpu": 1.5,
  "gpu": 1,
  "memory": 2048,
  "on_demand": true,
  "shutdown_timeout": 60,
  "storage": 25,
  "env": {
    "CONN": "#{x.Package:Entity}",
    "LOGIN": "login"
  }
}

Configuration properties and defaults:

PropertyTypeDefaultDescription
cpuDouble0.25CPU cores allocated
gpuInteger0GPU devices reserved
memoryInteger512RAM in megabytes
on_demandBooleanfalseStart container only on first request
shutdown_timeoutIntegernullIdle minutes before auto-shutdown
storageInteger21Disk storage in gigabytes
shm_sizeInteger64Shared memory in megabytes
envObjectEnvironment variables passed to the container
imageStringPublished image to run instead of building one
portIntegerPort the container listens on (EXPOSE when built here)

Set image when the image is already published — built by a CI pipeline, kept in another repository, or a stock third-party image. The folder then needs no Dockerfile at all, and grok publish builds and pushes nothing:

{
  "image": "datagrok/jkg_python:bleeding-edge",
  "port": 8888,
  "on_demand": true
}

Set port alongside it — with no Dockerfile there is no EXPOSE to read. A folder holding both a Dockerfile and an image is built from the Dockerfile.

For env values, use #{x.Package:Entity} to pass a JSON-serialized entity from a package namespace. Credentials are only passed for connections within the same package.

3. Implement HTTP request access

Get the container ID and use fetchProxy to call the container's HTTP server:

const containerId = (await grok.dapi.docker.dockerContainers.filter('my-container').first()).id;

const params = {
  method: 'POST',
  headers: {'Accept': 'application/json', 'Content-Type': 'application/json'},
  body: JSON.stringify(payload),
};
const response: Response = await grok.dapi.docker.dockerContainers.fetchProxy(containerId, '/endpoint', params);
const result = await response.json();

The params object follows the standard RequestInit interface.

4. Implement WebSocket access (if needed)

const ws: WebSocket = await grok.dapi.docker.dockerContainers.webSocketProxy(container.id, '/ws');
ws.send('Hello');

ws.addEventListener('message', (event: MessageEvent) => {
  console.log(event.data);
});

setTimeout(() => ws.close(), 3000);

5. Build and publish

webpack
grok publish dev

A return code of 0 indicates successful deployment. After publishing, Datagrok queues the image for building automatically.

6. Monitor status

In Datagrok, go to Platform -> Dockers to view containers and images. Status indicators:

  • Green dot: running/ready
  • Red dot: error
  • Blinking grey dot: pending (starting, stopping, or rebuilding)
  • No dot: stopped

Right-click a card to start/stop containers or rebuild images. Check logs via the Property pane.

Behavior

  • Ask the user for the package name and what service the container will run.
  • Create the dockerfiles/ directory, Dockerfile, and optionally container.json.
  • If the user needs HTTP access, generate a TypeScript helper function using fetchProxy.
  • If the user needs WebSocket access, generate a helper using webSocketProxy.
  • Remind the user that only one EXPOSE port is allowed in the Dockerfile.
  • Remind the user that grok-spawner must be running in the same environment.
  • Follow project coding conventions: no excessive comments, prefer for-in loops, catch/else if on new lines.

Signals

GitHub stars
72
Forks
32
Last commit
Sep 2026

ahel review

  • S4info
    community integration — published by datagrok-ai, not docker

Automated review, not a security audit. Ruleset v1.

Advanced
Catalog kind
skill
Gateway key
create-docker-container
Source
github.com/datagrok-ai/public