Docker Production
Run a structured Docker production readiness checklist before you ship containers to real users.
Overview
docker-production is an agent skill most often used in Ship (also Operate) that guides solo builders through a Docker production checklist before and after release.
Install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-docker --skill docker-productionWhat is this skill?
- Pre-deployment checklist: image scan, secrets, resource limits, health checks, logging
- Production monitoring block: CPU/memory metrics, centralized logs, alerts, dashboard
- High availability: replicas, load balancer, graceful shutdown, rolling updates
- Backup & recovery: volume backups, tested recovery, documented rollback
- Environment overrides with strict_mode enabled in production
- 4 checklist sections: pre-deployment, monitoring, high availability, backup & recovery
Adoption & trust: 1 installs on skills.sh; 2 GitHub stars; 1/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
What problem does it solve?
You are about to deploy a container and do not have a consistent list of security, HA, monitoring, and backup checks.
Who is it for?
Indie devs containerizing a backend or SaaS who want agent-guided release hardening without hiring a dedicated platform team.
Skip if: Local-only dev containers with no production target, or teams already standardized on a managed PaaS that owns scanning and HA end-to-end.
When should I use this skill?
You are preparing or operating Docker workloads in production and need a consistent checklist-driven review.
What do I get? / Deliverables
You get an ordered production checklist and environment-aware validation defaults so releases and ongoing ops follow the same Docker standards.
- Production readiness review against the checklist
- Environment-tuned validation settings (dev vs production strict_mode)
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Pre-deployment gates (image scan, secrets, health checks) belong on the Ship shelf because they block unsafe releases. Security is the canonical subphase: vulnerability scans, Docker secrets, and strict validation maps directly to release hardening.
Where it fits
Agent walks your Dockerfile and compose file against vulnerability scan and secrets placement before merge.
You finalize replicas, load balancer, and rolling update strategy for first production deploy.
You configure centralized logs, alerts, and a dashboard template for container metrics.
You schedule volume backups and document rollback after a bad release.
How it compares
Use as a structured checklist skill instead of asking the agent for generic “Docker best practices” in one-off prompts.
Common Questions / FAQ
Who is docker-production for?
Solo and indie builders who deploy their own Docker images and want a repeatable production readiness review with their AI coding agent.
When should I use docker-production?
Use it in Ship before go-live to verify scans, secrets, and health checks; in Operate when setting monitoring, backups, and rollback plans for running containers.
Is docker-production safe to install?
Treat it as guidance and config templates—review the Security Audits panel on this Prism page and inspect any generated compose or secret-handling advice before applying to prod.
SKILL.md
READMESKILL.md - Docker Production
# docker-production Configuration # Category: containers # Generated: 2025-12-30 skill: name: docker-production version: "1.0.0" category: containers settings: # Default settings for docker-production enabled: true log_level: info # Category-specific defaults validation: strict_mode: false auto_fix: false output: format: markdown include_examples: true # Environment-specific overrides environments: development: log_level: debug validation: strict_mode: false production: log_level: warn validation: strict_mode: true # Integration settings integrations: # Enable/disable integrations git: true linter: true formatter: true # Docker Production Checklist pre_deployment: - Image scanned for vulnerabilities - All secrets in Docker secrets - Resource limits configured - Health checks defined - Logging configured monitoring: - Container metrics (CPU, memory) - Application logs centralized - Alerts configured - Dashboard ready high_availability: - Multiple replicas - Load balancer configured - Graceful shutdown handling - Rolling update strategy backup_recovery: - Volume backup scheduled - Recovery tested - Rollback plan documented { "$schema": "http://json-schema.org/draft-07/schema#", "title": "docker-production Configuration Schema", "type": "object", "properties": { "skill": { "type": "object", "properties": { "name": { "type": "string" }, "version": { "type": "string", "pattern": "^\\d+\\.\\d+\\.\\d+$" }, "category": { "type": "string", "enum": [ "api", "testing", "devops", "security", "database", "frontend", "algorithms", "machine-learning", "cloud", "containers", "general" ] } }, "required": [ "name", "version" ] }, "settings": { "type": "object", "properties": { "enabled": { "type": "boolean", "default": true }, "log_level": { "type": "string", "enum": [ "debug", "info", "warn", "error" ] } } } }, "required": [ "skill" ] } # Docker Production Guide ## Overview This guide provides comprehensive documentation for the **docker-production** skill in the custom-plugin-docker plugin. ## Category: Containers ## Quick Start ### Prerequisites - Familiarity with containers concepts - Development environment set up - Plugin installed and configured ### Basic Usage ```bash # Invoke the skill claude "docker-production - [your task description]" # Example claude "docker-production - analyze the current implementation" ``` ## Core Concepts ### Key Principles 1. **Consistency** - Follow established patterns 2. **Clarity** - Write readable, maintainable code 3. **Quality** - Validate before deployment ### Best Practices - Always validate input data - Handle edge cases explicitly - Document your decisions - Write tests for critical paths ## Common Tasks ### Task 1: Basic Implementation ```python # Example implementation pattern def implement_docker_production(input_data): """ Implement docker-production functionality. Args: input_data: Input to process Returns: Processed result """ # Validate input if not input_data: raise ValueError("Input required") # Process result = process(input_data) # Return return result ``` ### Task 2: Advanced Usage For advanced scenarios, consider: - Configuration customization via `assets/config.yaml` - Validation using `scripts/validate.py` - Integration with other skills ## Troubleshooting ### Common Issues | Issue | Cause | Solution | |-------|-