Agent skill · software engineering · github
multi-stage-dockerfile
Create optimized multi-stage Dockerfiles for any language or framework
Why this skill is useful
Provides specific multi-stage Dockerfile patterns and best practices that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
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 NODEENV=production to optimize runtime be …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/multi-stage-dockerfile