multi-stage-dockerfile
Create optimized multi-stage Dockerfiles for any language or framework
Best use case
multi-stage-dockerfile is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create optimized multi-stage Dockerfiles for any language or framework
Teams using multi-stage-dockerfile should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/multi-stage-dockerfile/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How multi-stage-dockerfile Compares
| Feature / Agent | multi-stage-dockerfile | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create optimized multi-stage Dockerfiles for any language or framework
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
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
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