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Docker image optimization

Skill Amey-Thakur/AI-SKILLS/skills/devops/docker-image-optimization

Plug-and-play skills and prompts for every AI coding agent

Install
npx -y skills add Amey-Thakur/AI-SKILLS --skill docker-image-optimization

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  • 19 days oldThe repository was created 19 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Build small, fast, secure container images with layer caching, multi-stage builds, and a minimal base policy. Use when images are bloated, slow to build, or failing security scans.

SKILL.md

3.4 KB, as published. Nobody here has run it

Docker image optimization

A container image has three costs: build time (developer iteration), size (registry, pull, cold-start), and attack surface (every byte is something to scan and exploit). Multi-stage builds and disciplined layering cut all three at once.

Method

  1. Separate build from runtime with multi-stage. A build stage with the full toolchain compiles/bundles; the final stage copies only the artifact into a minimal runtime (see artifact-versioning): compilers, dev dependencies, and build caches never ship. This single change often halves image size and removes most of the CVEs a scanner finds.
  2. Order layers by change frequency for cache hits. Dependencies (rarely change) before application code (changes every commit): COPY package.json && install then COPY . ., so a code edit does not reinstall every dependency. Cache misses on the slow layer are the main build-time waste (see deployment-pipelines' speed budget); a correctly-ordered Dockerfile rebuilds in seconds.
  3. Choose the smallest sufficient base, by policy. Distroless or minimal (alpine, slim, scratch for static binaries) over full OS images: less to pull, less to patch, less to exploit. Pin base images by digest (not latest: reproducibility and supply-chain: see supply-chain-defense), and standardize the allowed bases org-wide so patching is one bump, not fifty (see container-security).
  4. Keep each layer clean. Combine related RUN commands and clean up within the same layer (package caches, temp files: a separate rm layer does not shrink the image, it adds one); use .dockerignore so build context excludes node_modules, .git, and secrets (which must never enter an image: see secrets-management). Every megabyte multiplies across every pull on every node.
  5. Harden the runtime image. Run as non-root (see kubernetes-workloads' pod security), read-only filesystem where possible, no shell/package manager in the final image if the app does not need them (smaller and drastically reduces post-exploit options), and only the ports the app uses. The minimal image is also the hardened image: the disciplines converge (see least-privilege).
  6. Gate size and scan in CI. Scan every image for known vulnerabilities (see dependency-auditing, sast-integration) and track image size with a budget and per-PR diff (see bundle-size's twin for containers): a base bump that adds 200MB or a new critical CVE fails the gate, caught at the commit, not at deploy.

Boundaries

  • Extreme minimization (scratch, distroless) removes debugging tools; keep a debug variant or ephemeral debug-container workflow so production incidents are still investigable (see production-debugging).
  • Build-cache optimization interacts with reproducibility and multi-arch builds; verify the cache does not mask stale dependencies (lockfiles pinned: see dependency-management).
  • Image optimization reduces attack surface but is not container security entire: runtime policy, network policy, and secrets handling are separate layers (see container-security, cloud-networking).

Keep looking

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