Docker Development

Category: Engineering Domain: Container Development & Optimization

Overview

The Docker Development skill provides automated analysis of Dockerfiles and docker-compose configurations. It identifies layer optimization opportunities, security issues, best practice violations, and compose service misconfigurations. Use this skill to enforce container standards across your team and catch issues before they reach production.

Clarify First

Before running the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Target file — which Dockerfile or docker-compose.yml to analyze (sets --file and which tool runs)
• Focus — full analysis vs security-only, and whether to check port conflicts (sets --security-only/--check-ports)
• Output mode — human-readable vs JSON for CI (sets --format json and whether findings gate a pipeline)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

# Analyze a Dockerfile for best practices
python scripts/dockerfile_analyzer.py --file Dockerfile

# Analyze with JSON output
python scripts/dockerfile_analyzer.py --file Dockerfile --format json

# Validate a docker-compose file
python scripts/compose_validator.py --file docker-compose.yml

# Check for port conflicts across compose files
python scripts/compose_validator.py --file docker-compose.yml --check-ports

Tools Overview

dockerfile_analyzer.py

Analyzes Dockerfiles for best practices, security issues, and optimization opportunities.

Feature	Description
Layer optimization	Detects unnecessary layers, recommends combining RUN statements
Multi-stage analysis	Validates multi-stage build patterns and final image size
Security scanning	Flags running as root, use of latest tags, exposed secrets
Base image checks	Recommends smaller base images (alpine, distroless, slim)
Cache optimization	Identifies poor layer ordering that breaks Docker cache

# Full analysis
python scripts/dockerfile_analyzer.py --file Dockerfile

# Security-focused scan
python scripts/dockerfile_analyzer.py --file Dockerfile --security-only

# JSON output for CI integration
python scripts/dockerfile_analyzer.py --file Dockerfile --format json

compose_validator.py

Validates docker-compose files for correctness, dependency issues, and port conflicts.

Feature	Description
Schema validation	Checks compose file structure and syntax
Dependency graph	Validates depends_on chains for circular dependencies
Port conflict detection	Identifies duplicate host port bindings
Volume mount checks	Validates volume paths and mount configurations
Network analysis	Checks network definitions and service connectivity

# Full validation
python scripts/compose_validator.py --file docker-compose.yml

# Check port conflicts only
python scripts/compose_validator.py --file docker-compose.yml --check-ports

# JSON output
python scripts/compose_validator.py --file docker-compose.yml --format json

Workflows

Dockerfile Review Workflow

1. Analyze - Run dockerfile_analyzer.py against the target Dockerfile
2. Review findings - Address critical security issues first (root user, secrets)
3. Optimize layers - Combine RUN statements, reorder for cache efficiency
4. Validate base images - Switch to minimal base images where possible
5. Re-analyze - Confirm improvements and verify no regressions

Compose Validation Workflow

1. Validate structure - Run compose_validator.py for syntax and schema checks
2. Check dependencies - Review service dependency graph for circular refs
3. Audit ports - Ensure no host port conflicts across services
4. Review volumes - Confirm volume mounts are correct and necessary
5. Network review - Verify service isolation and connectivity

CI Integration Workflow

# Example GitHub Actions step
- name: Docker Lint
  run: |
    python scripts/dockerfile_analyzer.py --file Dockerfile --format json > results.json
    python scripts/compose_validator.py --file docker-compose.yml --format json >> results.json

Reference Documentation

• Docker Best Practices - Comprehensive guide to Dockerfile and Compose patterns

Common Patterns Quick Reference

Pattern	Good	Bad
Base image	FROM python:3.12-slim	FROM python:latest
User	USER appuser	Running as root
Layer combining	RUN apt-get update && apt-get install -y pkg	Separate RUN for update and install
COPY ordering	Copy requirements first, then code	Copy everything at once
Multi-stage	Use builder stage + minimal runtime	Single stage with build tools
Secrets	Use build secrets or env at runtime	COPY .env . or ENV SECRET=value
Health checks	HEALTHCHECK CMD curl -f http://localhost/	No health check defined
.dockerignore	Include node_modules, .git, etc.	No .dockerignore file

Compose Patterns

Pattern	Good	Bad
Restart policy	restart: unless-stopped	No restart policy
Resource limits	deploy.resources.limits set	Unlimited resources
Named volumes	volumes: [db-data:/var/lib/postgresql]	Anonymous volumes
Networks	Explicit network definitions	Default bridge only
Environment	env_file: .env	Inline secrets in compose