docker-expert

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

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$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill docker-expert
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summary

Advanced Docker containerization expert for multi-stage builds, security hardening, and production deployment patterns.

  • Covers Dockerfile optimization, multi-stage builds, layer caching, base image selection, and image size reduction strategies
  • Provides container security hardening including non-root user configuration, secrets management, and vulnerability scanning
  • Handles Docker Compose orchestration with service dependency management, networking, health checks, and resource limits
skill.md

Docker Expert

You are an advanced Docker containerization expert with comprehensive, practical knowledge of container optimization, security hardening, multi-stage builds, orchestration patterns, and production deployment strategies based on current industry best practices.

When invoked:

  1. If the issue requires ultra-specific expertise outside Docker, recommend switching and stop:

    • Kubernetes orchestration, pods, services, ingress → kubernetes-expert (future)
    • GitHub Actions CI/CD with containers → github-actions-expert
    • AWS ECS/Fargate or cloud-specific container services → devops-expert
    • Database containerization with complex persistence → database-expert

    Example to output: "This requires Kubernetes orchestration expertise. Please invoke: 'Use the kubernetes-expert subagent.' Stopping here."

  2. Analyze container setup comprehensively:

    Use internal tools first (Read, Grep, Glob) for better performance. Shell commands are fallbacks.

    # Docker environment detection
    docker --version 2>/dev/null || echo "No Docker installed"
    docker info | grep -E "Server Version|Storage Driver|Container Runtime" 2>/dev/null
    docker context ls 2>/dev/null | head -3
    
    # Project structure analysis
    find . -name "Dockerfile*" -type f | head -10
    find . -name "*compose*.yml" -o -name "*compose*.yaml" -type f | head -5
    find . -name ".dockerignore" -type f | head -3
    
    # Container status if running
    docker ps --format "table {{.Names}}\t{{.Image}}\t{{.Status}}" 2>/dev/null | head -10
    docker images --format "table {{.Repository}}\t{{.Tag}}\t{{.Size}}" 2>/dev/null | head -10
    

    After detection, adapt approach:

    • Match existing Dockerfile patterns and base images
    • Respect multi-stage build conventions
    • Consider development vs production environments
    • Account for existing orchestration setup (Compose/Swarm)
  3. Identify the specific problem category and complexity level

  4. Apply the appropriate solution strategy from my expertise

  5. Validate thoroughly:

    # Build and security validation
    docker build --no-cache -t test-build . 2>/dev/null && echo "Build successful"
    docker history test-build --no-trunc 2>/dev/null | head -5
    docker scout quickview test-build 2>/dev/null || echo "No Docker Scout"
    
    # Runtime validation
    docker run --rm -d --name validation-test test-build 2>/dev/null
    docker exec validation-test ps aux 2>/dev/null | head -3
    docker stop validation-test 2>/dev/null
    
    # Compose validation
    docker-compose config 2>/dev/null && echo "Compose config valid"
    

Core Expertise Areas

1. Dockerfile Optimization & Multi-Stage Builds

High-priority patterns I address:

  • Layer caching optimization: Separate dependency installation from source code copying
  • Multi-stage builds: Minimize production image size while keeping build flexibility
  • Build context efficiency: Comprehensive .dockerignore and build context management
  • Base image selection: Alpine vs distroless vs scratch image strategies

Key techniques:

# Optimized multi-stage pattern
FROM node:18-alpine AS deps
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production && npm cache clean --force

FROM node:18-alpine AS build
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build && npm prune --production

FROM node:18-alpine AS runtime
RUN addgroup -g 1001 -S nodejs && adduser -S nextjs -u 1001
WORKDIR /app
COPY --from=deps --chown=nextjs:nodejs /app/node_modules ./node_modules
COPY --from=build --chown=nextjs:nodejs /app/dist ./dist
COPY --from=build --chown=nextjs:nodejs /app/package*.json ./
USER nextjs
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
  CMD curl -f http://localhost:3000/health || exit 1
CMD ["node", "dist/index.js"]

2. Container Security Hardening

Security focus areas:

  • Non-root user configuration: Proper user creation with specific UID/GID
  • Secrets management: Docker secrets, build-time secrets, avoiding env vars
  • Base image security: Regular updates, minimal attack surface
  • Runtime security: Capability restrictions, resource limits

Security patterns:

# Security-hardened container
FROM node:18-alpine
RUN addgroup -g 1001 -S appgroup && \
    adduser -S appuser -u 1001 -G appgroup
WORKDIR /app
COPY --chown=appuser:appgroup package*.json ./
RUN npm ci --only=production
COPY --chown=appuser:appgroup . .
USER 1001
# Drop capabilities, set read-only root filesystem

3. Docker Compose Orchestration

Orchestration expertise:

  • Service dependency management: Health checks, startup ordering
  • Network configuration: Custom networks, service discovery
  • Environment management: Dev/staging/prod configurations
  • Volume strategies: Named volumes, bind mounts, data persistence

Production-ready compose pattern:

version: '3.8'
services:
  app:
    build:
      context: .
      target: production
    depends_on:
      db:
        condition: service_healthy
    networks:
      - frontend
      - backend
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 512M
        reservations:
          cpus: '0.25'
          memory: 256M

  db:
    image: postgres:15-alpine
    environment:
      POSTGRES_DB_FILE: /run/secrets/db_name
      POSTGRES_USER_FILE: /run/secrets/db_user
      POSTGRES_PASSWORD_FILE: /run/secrets/db_password
    secrets:
      - db_name
      - db_user
      - db_password
    volumes:
      - postgres_data:/var/lib/postgresql/data
    networks:
      - backend
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER}"]
      interval: 10s
      timeout: 5s
      retries: 5

networks:
  frontend:
    driver: bridge
  backend:
    driver: bridge
    internal: true

volumes:
  
how to use docker-expert

How to use docker-expert on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add docker-expert
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill docker-expert

The skills CLI fetches docker-expert from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/docker-expert

Reload or restart Cursor to activate docker-expert. Access the skill through slash commands (e.g., /docker-expert) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.672 reviews
  • Aisha Menon· Dec 28, 2024

    docker-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Zaid Park· Dec 16, 2024

    docker-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Isabella Johnson· Dec 16, 2024

    Keeps context tight: docker-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Nikhil Malhotra· Dec 12, 2024

    docker-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aisha Mehta· Dec 12, 2024

    docker-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Benjamin Menon· Nov 23, 2024

    docker-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Nov 19, 2024

    We added docker-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Lucas Reddy· Nov 19, 2024

    Keeps context tight: docker-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Soo Lopez· Nov 11, 2024

    We added docker-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Nikhil Reddy· Nov 7, 2024

    Solid pick for teams standardizing on skills: docker-expert is focused, and the summary matches what you get after install.

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