docker-expert▌
davila7/claude-code-templates · updated May 30, 2026
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Advanced Docker containerization expertise for optimization, security, and production deployment.
- ›Covers multi-stage builds, image size optimization, layer caching strategies, and base image selection (Alpine, distroless, scratch)
- ›Provides security hardening patterns including non-root user configuration, secrets management, capability restrictions, and vulnerability scanning
- ›Includes Docker Compose orchestration with service dependency management, health checks, networking, resource
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:
-
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."
-
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 -10After 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)
-
Identify the specific problem category and complexity level
-
Apply the appropriate solution strategy from my expertise
-
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 /app/node_modules ./node_modules
COPY /app/dist ./dist
COPY /app/package*.json ./
USER nextjs
EXPOSE 3000
HEALTHCHECK \
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 package*.json ./
RUN npm ci --only=production
COPY . .
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-expertHow to use docker-expert on Cursor
AI-first code editor with Composer
1Prerequisites
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
2Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill docker-expertThe skills CLI fetches docker-expert from GitHub repository davila7/claude-code-templates and configures it for Cursor.
3Select 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│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/docker-expertReload 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.
Additional Resources
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.7★★★★★62 reviews- ★★★★★Sakura Martin· Dec 28, 2024
I recommend docker-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Jin Brown· Dec 12, 2024
Keeps context tight: docker-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aanya Malhotra· Dec 8, 2024
Solid pick for teams standardizing on skills: docker-expert is focused, and the summary matches what you get after install.
- ★★★★★Jin Taylor· Dec 8, 2024
We added docker-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Carlos Rahman· Nov 27, 2024
docker-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kwame Rahman· Nov 27, 2024
Registry listing for docker-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Luis Reddy· Nov 27, 2024
docker-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Camila Abebe· Nov 3, 2024
docker-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Luis Bansal· Oct 22, 2024
Useful defaults in docker-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aditi Choi· Oct 18, 2024
I recommend docker-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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