Does it provide linting for Dockerfiles?

Yes, it integrates Hadolint standards to identify common mistakes, such as using the 'latest' tag or failing to clean up package manager caches.

Does it support different programming languages?

Yes, the skill includes specific optimization patterns and examples for Go, Node.js, and Python projects.

How does it handle container security?

The skill mandates the use of non-root users (UID 65532) and recommends distroless base images to minimize the attack surface of your containers.

Can it help with slow Docker builds?

Absolutely. It provides guidance on layer ordering, placing frequently changed files like source code after rarely changed files like dependency manifests to maximize cache hits.

How does this skill help reduce Docker image size?

It enforces multi-stage builds, which separate the build-time dependencies from the runtime environment, ensuring only the necessary production binaries are included in the final image.

Dockerfile Best Practices

Name: Dockerfile Best Practices
Author: sumik5

bysumik5

Despliegue y DevOps

Optimizes Docker container images for minimal size, maximum security, and peak build performance using modern industry standards.

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This Claude Code skill provides a comprehensive framework for creating production-ready Dockerfiles. It guides the AI to implement multi-stage builds to drastically reduce image size, optimize layer caching for faster CI/CD pipelines, and enhance security by enforcing non-root execution and utilizing distroless base images. By integrating static analysis tools like Hadolint and vulnerability scanning recommendations, it ensures your containerization workflow is efficient, secure, and follows current DevOps best practices for Go, Node.js, and Python environments.

Características Principales

Security hardening via non-root users and distroless base images
Standardized .dockerignore templates to prevent sensitive data leaks
Static analysis and linting guidance using Hadolint standards
Layer sequencing optimization for efficient build caching
Automated multi-stage build implementation to reduce image size
0 GitHub stars

Casos de Uso

Migrating monolithic Dockerfiles to efficient multi-stage builds
Hardening container security for production-grade deployments
Optimizing build speeds in CI/CD pipelines through better cache management

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Características Principales

Security hardening via non-root users and distroless base images
Standardized .dockerignore templates to prevent sensitive data leaks
Static analysis and linting guidance using Hadolint standards
Layer sequencing optimization for efficient build caching
Automated multi-stage build implementation to reduce image size
0 GitHub stars

Casos de Uso

Migrating monolithic Dockerfiles to efficient multi-stage builds
Hardening container security for production-grade deployments
Optimizing build speeds in CI/CD pipelines through better cache management