Docker Project Foundations logo

Docker Project Foundations

Organization
docker
docker-project-foundations

Use this skill when setting up, initializing, or Dockerizing a project, even if the user doesn't explicitly mention Docker but describes a need for containerized local development, adding a database or cache dependency, or running services without host-level installs. Covers Dockerfile, compose.yaml, and .dockerignore creation with Docker best practices.

Overview

Publisherdocker
Repositoryskills
Skill namedocker-project-foundations
Stars
557
Forks
29
Bundled files
8
LicenseApache-2.0
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.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Docker Project Foundations 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/docker/skills/tree/main/skills/docker-project-foundations
  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/docker/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/docker-project-foundations .claude/skills/docker-project-foundations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Docker Project Foundations 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 Docker Project Foundations 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 Docker Project Foundations 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.

Docker Project Foundations

Overview

This skill guides you in Dockerizing a project from scratch. It focuses on creating the initial Docker file set, choosing a sane layout, and preferring containerized dependencies over host-level installs.

When to use this skill

Activate this skill when:

  • A user asks you to set up, initialize, or Dockerize a project
  • A project needs an initial Dockerfile, compose.yaml, or .dockerignore and does not have one
  • A user wants to add a service dependency (database, cache, message queue) to a project
  • A user asks how to run or develop a project locally and Docker is available

Do not use this skill when

Do not use this skill when:

  • The user explicitly wants to avoid Docker
  • The project already has a mature Docker setup and only needs minor edits
  • The main task is optimizing an existing Dockerfile
  • The main task is editing or debugging an existing Compose stack

Core guidance

Always create these three files

When Dockerizing a project, always produce all three:

  1. .dockerignore — Create this first so the initial build context is small and safe. See assets/dockerignore-example for a reference.
  2. Dockerfile — Create a working starter image definition that the project can build and run with. See assets/Dockerfile.simple.
  3. compose.yaml — Create a local development stack that includes the application service and any required dependencies. See assets/compose-dev.yaml.

npm registry credentials

  • Exclude .npmrc at every depth with **/.npmrc in .dockerignore; otherwise a broad source copy can persist credentials in image layers.
  • The Node.js starter mounts npmrc as a BuildKit secret for both npm ci steps. Public-package builds need no secret. For private registries, pass the config explicitly:
    bash
    DOCKER_BUILDKIT=1 docker build --secret id=npmrc,src="$HOME/.npmrc" .
    Use the actual config path if the project keeps it elsewhere. Never copy the credential file or pass its values through ARG or ENV. Dependency scripts run during installation can access the mounted secret; use trusted dependencies and a least-privilege registry token.
  • For private-registry builds through Compose, add this optional override as compose.npm.yaml alongside the starter's compose.yaml:
    yaml
    services:
      app:
        build:
          secrets:
            - npmrc
    secrets:
      npmrc:
        file: ${NPMRC_PATH:?Set NPMRC_PATH to your npm config file}
    Build with NPMRC_PATH="$HOME/.npmrc" docker compose -f compose.yaml -f compose.npm.yaml build. This grants build-time access only, not a runtime secret. Public-package builds should omit the override so no credential file is required.

Prefer Dockerized dependencies over host installs

When a project needs a database (Postgres, MySQL, MongoDB), cache (Redis, Memcached), queue (RabbitMQ, Kafka), or any other infrastructure service:

  • Always define it as a service in compose.yaml instead of telling the user to install it on the host.
  • Never suggest brew install postgres, apt install redis, or similar host-level installs for development dependencies.
  • Use official Docker images from Docker Hub for these services.
  • Configure services with environment variables, not config files baked into images.

Bootstrap checklist

  • Name the file compose.yaml rather than legacy Compose filenames.
  • Put all three files at the project root unless there is a clear multi-service layout that justifies a docker/ subdirectory.
  • Ensure the initial setup can build and start locally with one command path.
  • Bind published application ports to loopback by default. Widen the host address only when another device must reach the development service.
  • Keep unauthenticated datastores on the Compose network instead of publishing their ports. If local host tools require database access, publish only to loopback.
  • If a development-only credential fallback enables one-command startup, label it clearly and document a .env override.
  • Use Compose services for local databases, caches, and queues instead of host installs.
  • Keep the first scaffold simple; defer detailed image optimization and advanced Compose tuning to the owning skills.

Development vs production

  • Development: Use bind mounts for live reload, publish application ports on loopback by default, and enable verbose logging. Keep unauthenticated datastores on the Compose network; publish a datastore port only on loopback when local host tools require it.
  • Production: Use multi-stage builds, copy only built artifacts, do not mount source code, minimize image layers, set appropriate resource limits.
  • Keep a single Dockerfile that supports both via build stages and build arguments when possible.

File placement

  • Place Dockerfile at the project root (or in a docker/ subdirectory if the project has multiple services).
  • Place compose.yaml at the project root.
  • Place .dockerignore at the project root, next to the Dockerfile.

Related skills

  • For Dockerfile optimization, cache strategy, non-root execution, and image hardening, use docker-build-strategies.
  • For service dependencies, health checks, overrides, volumes, networks, and Compose debugging, use docker-compose-patterns.
  • For destructive Docker CLI commands (docker system prune, docker rm -f, image/network/builder pruning) and a cross-product index of destructive-command guardrails, use docker-destructive-guardrails.

References

  • references/project-structure.md — Detailed guidance on Docker project file organization, naming conventions, and multi-service layouts.

Assets

  • assets/dockerignore-example — A comprehensive .dockerignore for a typical project.
  • assets/compose-dev.yaml — A development-oriented Compose file with Dockerized dependencies.
  • assets/Dockerfile.simple — A basic multi-stage Dockerfile following best practices.

Scripts

  • scripts/verify-setup.sh — Checks that required files exist in the current directory and validates its compose.yaml. Run it from the project root, with the script path resolved under this skill's directory:
    bash
    bash "<skill-dir>/scripts/verify-setup.sh" [--help]
    Replace <skill-dir> with the absolute path of the folder that contains this SKILL.md; the scripts/ path is relative to that folder, not to the project. Do not change into the skill directory first: the script checks the current directory. If the skill directory cannot be resolved, check that .dockerignore, Dockerfile, and compose.yaml exist in the project root, then run docker compose config --quiet. Exit status is 0 when verification succeeds or help is requested, 1 when required files are missing or the Compose configuration is invalid, and 2 for invalid arguments.

Checks

  • checks/verification.md — Detailed verification checklist for manual review.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Docker Project Foundations AI skill do?

Use this skill when setting up, initializing, or Dockerizing a project, even if the user doesn't explicitly mention Docker but describes a need for containerized local development, adding a database or cache dependency, or running services without host-level installs. Covers Dockerfile, compose.yaml, and .dockerignore creation with Docker best practices.

Why use Docker Project Foundations on TypingMind?

Because you install it once and use it with any model. Docker Project Foundations 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 Docker Project Foundations in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/docker/skills/tree/main/skills/docker-project-foundations. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Docker Project Foundations?

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 Docker Project Foundations?

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

Is the Docker Project Foundations AI skill free?

Yes. It is published on GitHub by docker under the Apache-2.0 license. 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.

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