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.
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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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