docker-workflow
Comprehensive Docker containerization workflow covering multi-stage builds, docker-compose orchestration, image optimization, debugging, and production best practices. Use when containerizing applications, setting up development environments, or deploying with Docker.
Best use case
docker-workflow is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Comprehensive Docker containerization workflow covering multi-stage builds, docker-compose orchestration, image optimization, debugging, and production best practices. Use when containerizing applications, setting up development environments, or deploying with Docker.
Comprehensive Docker containerization workflow covering multi-stage builds, docker-compose orchestration, image optimization, debugging, and production best practices. Use when containerizing applications, setting up development environments, or deploying with Docker.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "docker-workflow" skill to help with this workflow task. Context: Comprehensive Docker containerization workflow covering multi-stage builds, docker-compose orchestration, image optimization, debugging, and production best practices. Use when containerizing applications, setting up development environments, or deploying with Docker.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/docker-workflow/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How docker-workflow Compares
| Feature / Agent | docker-workflow | 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?
Comprehensive Docker containerization workflow covering multi-stage builds, docker-compose orchestration, image optimization, debugging, and production best practices. Use when containerizing applications, setting up development environments, or deploying with Docker.
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.
Related Guides
SKILL.md Source
# Docker Workflow
## Overview
Docker containerization streamlines development, testing, and deployment by packaging applications with their dependencies into portable, reproducible containers. This skill guides you through professional Docker workflows from development to production.
## Core Capabilities
- **Multi-stage builds**: Separate build and runtime dependencies for optimal image size
- **Docker Compose orchestration**: Manage multi-container applications with networking and dependencies
- **Image optimization**: Reduce image size by 50-90% through best practices
- **Development workflows**: Hot-reload, volume mounting, and environment-specific configs
- **Debugging tools**: Container inspection, health checks, and troubleshooting utilities
- **Production readiness**: Security hardening, health checks, and deployment strategies
## When to Use This Skill
Activate when:
- Containerizing a new application
- Setting up development environments with Docker
- Creating production-ready Docker images
- Orchestrating multi-container applications
- Debugging container issues
- Optimizing Docker builds and images
## Workflow Phases
### Phase 1: Initial Setup
#### Create .dockerignore
Exclude unnecessary files from build context:
```dockerignore
node_modules/
__pycache__/
*.pyc
.git/
.env
*.log
dist/
build/
coverage/
```
See `examples/.dockerignore` for comprehensive template.
**Key principles**:
- Exclude build artifacts and dependencies
- Exclude sensitive files (.env, credentials)
- Exclude version control (.git)
- Smaller context = faster builds
#### Analyze Application Requirements
Determine:
- Runtime (Node.js, Python, Go, Java)
- Dependencies and package managers
- Build vs. runtime requirements
- Port exposure and volume needs
### Phase 2: Multi-Stage Dockerfile
#### Choose Strategy
Multi-stage builds reduce final image size by 50-90%:
```dockerfile
# Stage 1: Build
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build
# Stage 2: Production
FROM node:18-alpine
WORKDIR /app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
EXPOSE 3000
CMD ["node", "dist/index.js"]
```
See `examples/Dockerfile.multi-stage` for templates for Node.js, Python, Go, Java, and Rust.
#### Optimize Layer Caching
Order matters - place changing content last:
```dockerfile
# ✅ GOOD: Dependencies cached separately
COPY package.json package-lock.json ./
RUN npm ci
COPY . .
# ❌ BAD: Any file change invalidates cache
COPY . .
RUN npm ci
```
#### Apply Security Best Practices
```dockerfile
# Use specific versions
FROM node:18.17.1-alpine
# Run as non-root user
RUN addgroup -g 1001 -S nodejs && adduser -S nodejs -u 1001
USER nodejs
# Copy with ownership
COPY --chown=nodejs:nodejs . .
```
**Security checklist**:
- Pin base image versions
- Use minimal base images (alpine, slim)
- Run as non-root user
- Scan for vulnerabilities
- Minimize installed packages
### Phase 3: Docker Compose Setup
#### Define Services
Create `docker-compose.yml`:
```yaml
version: '3.8'
services:
app:
build:
context: .
dockerfile: Dockerfile
ports:
- "3000:3000"
environment:
- DATABASE_URL=postgresql://db:5432/myapp
depends_on:
db:
condition: service_healthy
volumes:
- ./src:/app/src # Development hot-reload
networks:
- app-network
db:
image: postgres:15-alpine
environment:
POSTGRES_DB: myapp
volumes:
- postgres-data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U user"]
interval: 5s
networks:
- app-network
volumes:
postgres-data:
networks:
app-network:
```
See `examples/docker-compose.yml` for full-featured setup with monitoring, queues, and caching.
#### Environment Configuration
Use override files for different environments:
**Development (docker-compose.override.yml)**:
```yaml
services:
app:
build:
target: development
volumes:
- ./src:/app/src
environment:
- NODE_ENV=development
command: npm run dev
```
**Production (docker-compose.prod.yml)**:
```yaml
services:
app:
build:
target: production
restart: always
environment:
- NODE_ENV=production
```
**Usage**:
```bash
# Development (uses override automatically)
docker-compose up
# Production
docker-compose -f docker-compose.yml -f docker-compose.prod.yml up -d
```
### Phase 4: Build and Run
#### Build Commands
```bash
# Basic build
docker build -t myapp:latest .
# Build specific stage
docker build --target production -t myapp:prod .
# Build with BuildKit (faster)
DOCKER_BUILDKIT=1 docker build -t myapp:latest .
```
#### Run Commands
```bash
# Single container
docker run -d -p 3000:3000 -e NODE_ENV=production myapp:latest
# Docker Compose
docker-compose up -d
# View logs
docker-compose logs -f app
# Execute in container
docker-compose exec app sh
# Stop and remove
docker-compose down -v
```
### Phase 5: Debugging and Troubleshooting
#### Use Helper Script
The `scripts/docker_helper.sh` utility provides common debugging operations:
```bash
# Check container health
./scripts/docker_helper.sh health myapp
# Inspect details
./scripts/docker_helper.sh inspect myapp
# View logs
./scripts/docker_helper.sh logs myapp 200
# Open shell
./scripts/docker_helper.sh shell myapp
# Analyze image size
./scripts/docker_helper.sh size myapp:latest
# Cleanup resources
./scripts/docker_helper.sh cleanup
```
#### Common Issues
**Container exits immediately**:
```bash
docker logs myapp
docker run -it --entrypoint sh myapp:latest
```
**Network connectivity**:
```bash
docker network inspect myapp_default
docker exec myapp ping db
```
**Volume permissions**:
```bash
# Fix in Dockerfile
RUN chown -R nodejs:nodejs /app/data
```
### Phase 6: Optimization
#### Reduce Image Size
**Strategies**:
1. Use smaller base images (alpine > slim > debian)
2. Multi-stage builds to exclude build tools
3. Combine RUN commands for fewer layers
4. Clean up in same layer
5. Use .dockerignore
**Example**:
```dockerfile
# ✅ GOOD: Combined, cleaned up
RUN apt-get update && \
apt-get install -y --no-install-recommends package1 && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
```
#### Build Performance
```bash
# Enable BuildKit
export DOCKER_BUILDKIT=1
# Use cache mounts
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# Parallel builds
docker-compose build --parallel
```
### Phase 7: Production Deployment
#### Production Dockerfile
```dockerfile
FROM node:18-alpine AS production
# Security: non-root user
RUN addgroup -g 1001 -S nodejs && adduser -S nodejs -u 1001
WORKDIR /app
COPY --from=builder --chown=nodejs:nodejs /app/dist ./dist
USER nodejs
# Health check
HEALTHCHECK --interval=30s --timeout=3s \
CMD node healthcheck.js
EXPOSE 3000
CMD ["node", "dist/index.js"]
```
#### Deployment Commands
```bash
# Tag for registry
docker tag myapp:latest registry.example.com/myapp:v1.0.0
# Push to registry
docker push registry.example.com/myapp:v1.0.0
# Deploy
docker-compose pull && docker-compose up -d
# Rolling update
docker-compose up -d --no-deps --build app
```
## Common Patterns
### Full-Stack Application
- Frontend + Backend + Database + Redis
- See `examples/docker-compose.yml`
### Microservices
- API Gateway + Multiple Services + Message Queue
- Network isolation and service discovery
### Development with Hot Reload
- Volume mounting for source code
- Override files for dev configuration
## Best Practices Summary
### Security
✅ Use specific image versions, not `latest`
✅ Run as non-root user
✅ Use secrets management for sensitive data
✅ Scan images for vulnerabilities
✅ Use minimal base images
### Performance
✅ Use multi-stage builds
✅ Optimize layer caching
✅ Use .dockerignore
✅ Combine RUN commands
✅ Use BuildKit
### Development
✅ Use docker-compose for multi-container apps
✅ Use volumes for hot-reload
✅ Implement health checks
✅ Use proper dependency ordering
### Production
✅ Set restart policies
✅ Use orchestration (Swarm, Kubernetes)
✅ Monitor with health checks
✅ Use reverse proxy
✅ Implement rolling updates
## Helper Resources
- **scripts/docker_helper.sh**: Container inspection, health checks, automation
- **examples/Dockerfile.multi-stage**: Templates for Node.js, Python, Go, Java, Rust
- **examples/docker-compose.yml**: Full-featured multi-service setup
- **examples/.dockerignore**: Comprehensive ignore patterns
## Quick Reference
### Essential Commands
```bash
# Build
docker build -t myapp .
docker-compose build
# Run
docker run -d -p 3000:3000 myapp
docker-compose up -d
# Logs
docker logs -f myapp
docker-compose logs -f
# Execute
docker exec -it myapp sh
docker-compose exec app sh
# Stop
docker-compose down
# Clean
docker system prune -a
```
### Debugging
```bash
# Inspect
docker inspect myapp
# Stats
docker stats myapp
# Networks
docker network inspect bridge
# Volumes
docker volume ls
```Related Skills
req-change-workflow
Standardize requirement/feature changes in an existing codebase (especially Chrome extensions) by turning "改需求/需求变更/调整交互/改功能/重构流程" into a repeatable loop: clarify acceptance criteria, confirm current behavior from code, assess impact/risk, design the new logic, implement with small diffs, run a fixed regression checklist, and update docs/decision log. Use when the user feels the change process is chaotic, when edits tend to sprawl across files, or when changes touch manifest/service worker/OAuth/storage/UI and need reliable verification + rollback planning.
defou-workflow
将原始想法转化为结构清晰、判断明确、具有长期价值的“得否”风格内容报告。
defou-stanley-workflow
Defou x Stanley 融合工作流:结合深度结构化思考与人性弱点洞察,生成极简、犀利且具有长期价值的爆款内容。
agentic-workflow
Practical AI agent workflows and productivity techniques. Provides optimized patterns for daily development tasks such as commands, shortcuts, Git integration, MCP usage, and session management.
workflow-patterns
Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the verification protocol.
workflow-orchestration-patterns
Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.
workflow-automation
Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility
tdd-workflows-tdd-refactor
Use when working with tdd workflows tdd refactor
tdd-workflows-tdd-red
Generate failing tests for the TDD red phase to define expected behavior and edge cases.
tdd-workflows-tdd-green
Implement the minimal code needed to make failing tests pass in the TDD green phase.
tdd-workflows-tdd-cycle
Use when working with tdd workflows tdd cycle
ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.