docker-development

これはレビュー時に保存された抜粋です。完全で最新の内容は外部ソースを確認してください。

---
name: "docker-development"
description: "Docker and container development agent skill and plugin for Dockerfile optimization, docker-compose orchestration, multi-stage builds, and container security hardening. Use when: user wants to optimize a Dockerfile, create or improve docker-compose configurations, implement multi-stage builds, audit container security, reduce image size, or follow container best practices. Covers build performance, layer caching, secret management, and production-ready container patterns."
license: MIT
metadata:
  version: 1.0.0
  author: Alireza Rezvani
  category: engineering
  updated: 2026-03-16
---

# Docker Development

> Smaller images. Faster builds. Secure containers. No guesswork.

Opinionated Docker workflow that turns bloated Dockerfiles into production-grade containers. Covers optimization, multi-stage builds, compose orchestration, and security hardening.

Not a Docker tutorial — a set of concrete decisions about how to build containers that don't waste time, space, or attack surface.

---

## Slash Commands

| Command | What it does |
|---------|-------------|
| `/docker:optimize` | Analyze and optimize a Dockerfile for size, speed, and layer caching |
| `/docker:compose` | Generate or improve docker-compose.yml with best practices |
| `/docker:security` | Audit a Dockerfile or running container for security issues |

---

## When This Skill Activates

Recognize these patterns from the user:

- "Optimize this Dockerfile"
- "My Docker build is slow"
- "Create a docker-compose for this project"
- "Is this Dockerfile secure?"
- "Reduce my Docker image size"
- "Set up multi-stage builds"
- "Docker best practices for [language/framework]"
- Any request involving: Dockerfile, docker-compose, container, image size, build cache, Docker security

If the user has a Dockerfile or wants to containerize something → this skill applies.

---

## Workflow

### `/docker:optimize` — Dockerfile Optimization

1. **Analyze current state**
   - Read the Dockerfile
   - Identify base image and its size
   - Count layers (each RUN/COPY/ADD = 1 layer)
   - Check for common anti-patterns

2. **Apply optimization checklist**

   ```
   BASE IMAGE
   ├── Use specific tags, never :latest in production
   ├── Prefer slim/alpine variants (debian-slim > ubuntu > debian)
   ├── Pin digest for reproducibility in CI: image@sha256:...
   └── Match base to runtime needs (don't use python:3.12 for a compiled binary)

   LAYER OPTIMIZATION
   ├── Combine related RUN commands with && \
   ├── Order layers: least-changing first (deps before source code)
   ├── Clean package manager cache in the same RUN layer
   ├── Use .dockerignore to exclude unnecessary files
   └── Separate build deps from runtime deps

   BUILD CACHE
   ├── COPY dependency files before source code (package.json, requirements.txt, go.mod)
   ├── Install deps in a separate layer from code copy
   ├── Use BuildKit cache mounts: --mount=type=cache,target=/root/.cache
   └── Avoid COPY . . before dependency installation

   MULTI-STAGE BUILDS
   ├── Stage 1: build (full SDK, build tools, dev deps)
   ├── Stage 2: runtime (minimal base, only production artifacts)
   ├── COPY --from=builder only what's needed
   └── Final image should have NO build tools, NO source code, NO dev deps
   ```

3. **Generate optimized Dockerfile**
   - Apply all relevant optimizations
   - Add inline comments explaining each decision
   - Report estimated size reduction

4. **Validate**
   ```bash
   python3 scripts/dockerfile_analyzer.py Dockerfile
   ```

### `/docker:compose` — Docker Compose Configuration

1. **Identify services**
   - Application (web, API, worker)
   - Database (postgres, mysql, redis, mongo)
   - Cache (redis, memcached)
   - Queue (rabbitmq, kafka)
   - Reverse proxy (nginx, traefik, caddy)

2. **Apply compose best practices**

   ```
   SERVICES
   ├── Use depends_on with condition: service_healthy
   ├── Add healthchecks for every service
   ├── Set reso