Automatically transform bloated Dockerfiles into production-ready, high-performance container images using industry best practices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install docker-optimizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install docker-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Docker Optimizer?
The Docker Optimizer is a specialized technical advisor designed to refine containerization workflows. It analyzes existing Dockerfiles to implement critical optimizations such as multi-stage builds, intelligent layer ordering for caching, and robust security hardening. By leveraging these Openclaw Skills, developers can move beyond slow and oversized images to create streamlined containers that are ready for production.
This skill supports a wide range of ecosystems including Node.js, Python, Go, and Java. It focuses on reducing the attack surface by minimizing package counts and switching to optimized base images like Alpine, Slim, or Distroless. Using these Openclaw Skills ensures that your containerized applications are not only faster to deploy but also significantly more cost-effective to host in cloud environments.
Docker Optimizer Use Cases
- Optimizing Docker images before production deployment to ensure maximum efficiency.
- Accelerating slow CI/CD pipelines by improving layer caching strategies and build parallelism.
- Reducing cloud storage and data transfer costs through massive image size reduction (often by 80% or more).
- Hardening container security by removing unnecessary packages and implementing non-root user execution.
- Preparing applications for Kubernetes or Serverless environments where small image sizes and fast cold starts are essential.
How Docker Optimizer Works
- The skill parses the provided Dockerfile to analyze instructions, stages, and base image selections.
- It identifies common anti-patterns such as using the latest tag, running as root, or missing .dockerignore files.
- It calculates the potential impact of optimizations, projecting size reduction and build time savings.
- Specific improvements are suggested, including multi-stage build structures and minimal base image alternatives.
- It generates a fully optimized Dockerfile alongside a recommended .dockerignore configuration.
- The tool validates the syntax to ensure the new Dockerfile is production-ready and follows official standards.
Docker Optimizer Setup
To use this skill, ensure that Docker is installed on your local machine or build environment. These Openclaw Skills require the Docker CLI to validate configurations.
# Verify your Docker installation
docker --version
# It is recommended to enable BuildKit for advanced features
export DOCKER_BUILDKIT=1
Docker Optimizer Data Schema & Taxonomy
The Docker Optimizer organizes its findings by comparing your current configuration against optimized industry standards. It generates the following data components:
| Component | Description |
|---|---|
| Analysis Report | Breakdown of critical issues like security vulnerabilities and cache inefficiencies. |
| Optimized Dockerfile | A complete, multi-stage Dockerfile ready for use in production. |
| .dockerignore | A tailored ignore list to reduce build context size. |
| Impact Metrics | Estimated percentage reduction in image size and build duration. |
| Security Profile | Summary of hardening steps taken, such as non-root user implementation. |
Docker Optimizer Advanced Features
- Support for Distroless and Scratch-based images to achieve the smallest possible security footprint.
- Integration with BuildKit for advanced cache mounts (npm, pip, go mod) and secret management.
- Multi-architecture build support (linux/amd64, linux/arm64) for modern cloud environments.
- Automated health check injection and graceful signal handling with tools like dumb-init.
- Language-specific tuning, including JVM container support and Node.js standalone build optimizations.
SKILL.md
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