Advanced Docker containerization expert for multi-stage builds, security hardening, and production deployment patterns.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondocker-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches docker-expert from sickn33/antigravity-awesome-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate docker-expert. Access via /docker-expert in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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.
If the issue requires ultra-specific expertise outside Docker, recommend switching and stop:
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 -10
After detection, adapt approach:
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"
High-priority patterns I address:
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"]
Security focus areas:
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
Orchestration expertise:
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:
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
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate 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
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AI/MLsame repoReviews
4.6★★★★★72 reviews- AAisha Menon★★★★★Dec 28, 2024
docker-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ZZaid Park★★★★★Dec 16, 2024
docker-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- IIsabella 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.
- NNikhil Malhotra★★★★★Dec 12, 2024
docker-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAisha Mehta★★★★★Dec 12, 2024
docker-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- BBenjamin Menon★★★★★Nov 23, 2024
docker-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- YYash Thakker★★★★★Nov 19, 2024
We added docker-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- LLucas 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.
- SSoo Lopez★★★★★Nov 11, 2024
We added docker-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- NNikhil 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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