multi-stage-dockerfile

SkillCloud & infra

Your AI can write multi-stage Dockerfiles, the recipes that package an app into a container image, so the images it creates come out smaller, faster, and more secure. It works for any language or framework, keeping packaging optimized no matter what your project is built with.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add the skill, then tell your AI which language or framework your project uses and ask it to create a multi-stage Dockerfile for you.

Then ask your AI: use the multi-stage-dockerfile skill

What your AI can do with it

  • Write multi-stage Dockerfiles for any language or framework
  • Produce smaller container images
  • Produce faster container images
  • Improve the security of container images
  • Create optimized Dockerfiles tailored to your project

What this skill tells your AI

The instructions your AI receives, as published by github/awesome-copilot in skills/multi-stage-dockerfile/SKILL.md and read by ahel’s review.

Your goal is to help me create efficient multi-stage Dockerfiles that follow best practices, resulting in smaller, more secure container images.

Multi-Stage Structure

  • Use a builder stage for compilation, dependency installation, and other build-time operations
  • Use a separate runtime stage that only includes what's needed to run the application
  • Copy only the necessary artifacts from the builder stage to the runtime stage
  • Use meaningful stage names with the AS keyword (e.g., FROM node:18 AS builder)
  • Place stages in logical order: dependencies → build → test → runtime

Base Images

  • Start with official, minimal base images when possible
  • Specify exact version tags to ensure reproducible builds (e.g., python:3.11-slim not just python)
  • Consider distroless images for runtime stages where appropriate
  • Use Alpine-based images for smaller footprints when compatible with your application
  • Ensure the runtime image has the minimal necessary dependencies

Layer Optimization

  • Organize commands to maximize layer caching
  • Place commands that change frequently (like code changes) after commands that change less frequently (like dependency installation)
  • Use .dockerignore to prevent unnecessary files from being included in the build context
  • Combine related RUN commands with && to reduce layer count
  • Consider using COPY --chown to set permissions in one step

Security Practices

  • Avoid running containers as root - use USER instruction to specify a non-root user
  • Remove build tools and unnecessary packages from the final image
  • Scan the final image for vulnerabilities
  • Set restrictive file permissions
  • Use multi-stage builds to avoid including build secrets in the final image

Performance Considerations

  • Use build arguments for configuration that might change between environments
  • Leverage build cache efficiently by ordering layers from least to most frequently changing
  • Consider parallelization in build steps when possible
  • Set appropriate environment variables like NODE_ENV=production to optimize runtime behavior
  • Use appropriate healthchecks for the application type with the HEALTHCHECK instruction

Signals

GitHub stars
40k
Forks
5k
Last commit
Oct 2026
Advanced
Item type
skill
Key
multi-stage-dockerfile
Source
github.com/github/awesome-copilot