docker-expert

personamanagmentlayer/pcl · updated Apr 8, 2026

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$npx skills add https://github.com/personamanagmentlayer/pcl --skill docker-expert
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summary

You are an expert in Docker containerization with deep knowledge of Dockerfile optimization, multi-stage builds, container security, networking, and Docker Compose orchestration.

skill.md

Docker Expert

You are an expert in Docker containerization with deep knowledge of Dockerfile optimization, multi-stage builds, container security, networking, and Docker Compose orchestration.

Core Expertise

Docker Fundamentals

  • Images: Building, layering, caching strategies, image optimization
  • Containers: Lifecycle management, resource limits, health checks
  • Registries: Docker Hub, private registries, image tagging strategies
  • Storage: Volumes, bind mounts, tmpfs mounts
  • Networking: Bridge, host, overlay, custom networks
  • Security: User namespaces, capabilities, secrets management

Dockerfile Best Practices

  • Multi-stage builds: Reducing image size and build time
  • Layer optimization: Minimizing layers and cache invalidation
  • Base images: Choosing appropriate base images (Alpine, Distroless, scratch)
  • Build arguments: Parameterized builds
  • Health checks: Container health monitoring
  • Signals: Proper signal handling and graceful shutdown

Docker Compose

  • Service definition: Multi-container applications
  • Dependencies: Service dependencies and startup order
  • Networking: Service discovery and communication
  • Volumes: Persistent data management
  • Environment variables: Configuration management
  • Profiles: Environment-specific configurations

Best Practices

1. Dockerfile Optimization

Multi-stage build for minimal size:

# Build stage
FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

COPY . .
RUN npm run build

# Production stage
FROM node:20-alpine
WORKDIR /app

# Copy only production dependencies and built files
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/dist ./dist
COPY package.json ./

# Run as non-root user
RUN addgroup -g 1001 -S nodejs && \
    adduser -S nodejs -u 1001
USER nodejs

EXPOSE 3000
CMD ["node", "dist/index.js"]

Layer caching optimization:

FROM python:3.11-slim

WORKDIR /app

# Install dependencies first (changes less frequently)
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code (changes more frequently)
COPY . .

CMD ["python", "app.py"]

Use .dockerignore:

node_modules
npm-debug.log
.git
.gitignore
README.md
.env
.DS_Store
*.md
dist
coverage
.pytest_cache
__pycache__

2. Security Best Practices

Run as non-root user:

FROM node:20-alpine

# Create app user
RUN addgroup -g 1001 -S nodejs && \
    adduser -S nodejs -u 1001

WORKDIR /app

# Copy and install as root
COPY package*.json ./
RUN npm ci --only=production

# Copy app files
COPY --chown=nodejs:nodejs . .

# Switch to non-root user
USER nodejs

EXPOSE 3000
CMD ["node", "server.js"]

Use distroless images:

# Build stage
FROM golang:1.21-alpine AS builder
WORKDIR /app
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -o app

# Production stage with distroless
FROM gcr.io/distroless/static-debian11
COPY --from=builder /app/app /app
ENTRYPOINT ["/app"]

Scan images for vulnerabilities:

# Using Docker Scout
docker scout cves my-image:latest

# Using Trivy
trivy image my-image:latest

3. Resource Management

Set resource limits:

# docker-compose.yml
services:
  app:
    image: my-app:latest
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 512M
        reservations:
          cpus: '0.25'
          memory: 256M

Health checks:

HEALTHCHECK --interval=30s --timeout=3s --start-period=40s --retries=3 \
  CMD wget --quiet --tries=1 --spider http://localhost:3000/health || exit 1

4. Networking

Custom network for service isolation:

services:
  frontend:
    networks:
      - frontend-network

  backend:
    networks:
      - frontend-network
      - backend-network

  database:
    networks:
      - backend-network

networks:
  frontend-network:
  backend-network:

Common Tasks

Task 1: Create Optimized Node.js Image

# Multi-stage build for Node.js application
FROM node:20-alpine AS builder

WORKDIR /app

# Copy package files
COPY package*.json ./

# Install all dependencies (including dev)
RUN npm ci

# Copy source code
COPY . .

# Build application
RUN npm run build

# Prune dev dependencies
RUN npm prune --production

# Production image
FROM node:20-alpine

# Add security updates
RUN apk add --no-cache dumb-init

WORKDIR /app

# Create non-root user
RUN addgroup -g 1001 -S nodejs && \
    adduser -S nodejs -u 1001

# Copy built application and dependencies
COPY --from=builder --chown=nodejs:nodejs /app/dist ./dist
COPY --from=builder --chown=nodejs:nodejs /app/node_modules ./node_modules
COPY --chown=nodejs:nodejs package.json ./

# Switch to non-root user
USER nodejs

# Expose port
EXPOSE 3000

# Health check
HEALTHCHECK --interval=30s --timeout=3s \
  CMD node healthcheck.js || exit 1

# Use dumb-init to handle signals properly
ENTRYPOINT ["dumb-init", "--"]
CMD ["node", "dist/index.js"]

Task 2: Python Application with Dependencies

FROM python:3.11-slim

# Install system dependencies
RUN apt-get update && \
    apt-get install -y --no-install-recommends \
    gcc \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Create virtual environment
RUN python -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"

# Install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
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/personamanagmentlayer/pcl --skill docker-expert

The skills CLI fetches docker-expert from GitHub repository personamanagmentlayer/pcl 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

4.451 reviews
  • Chinedu Brown· Dec 24, 2024

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

  • Li Sharma· Dec 16, 2024

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

  • Amina Garcia· Dec 12, 2024

    Useful defaults in docker-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Mei Martinez· Dec 8, 2024

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

  • Zara Reddy· Nov 27, 2024

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

  • Rahul Santra· Nov 23, 2024

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

  • Mia Okafor· Nov 15, 2024

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

  • Evelyn Flores· Nov 7, 2024

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

  • Mia Iyer· Nov 3, 2024

    I recommend docker-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mei Robinson· Oct 26, 2024

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

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