Blue-Green Deployment
SkillCloud & infraImplement blue-green deployment strategies for zero-downtime releases with instant rollback capability and traffic switching between environments.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Blue-Green Deployment skill
What this skill tells your AI
The instructions your AI receives, as published by aj-geddes/useful-ai-prompts in skills/blue-green-deployment/SKILL.md and read by ahel’s review.
Table of Contents
- Overview
- When to Use
- Quick Start
- Reference Guides
- Best Practices
Overview
Deploy applications using blue-green deployment patterns to maintain two identical production environments, enabling instant traffic switching and rapid rollback capabilities.
When to Use
- Zero-downtime releases
- High-risk deployments
- Complex application migrations
- Database schema changes
- Rapid rollback requirements
- A/B testing with environment separation
- Staged rollout strategies
Quick Start
Minimal working example:
# blue-green-setup.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: blue-green-config
namespace: production
data:
switch-traffic.sh: |
#!/bin/bash
set -euo pipefail
CURRENT_ACTIVE="${1:-blue}"
TARGET="${2:-green}"
ALB_ARN="arn:aws:elasticloadbalancing:us-east-1:123456789012:loadbalancer/app/myapp-alb/1234567890abcdef"
echo "Switching traffic from $CURRENT_ACTIVE to $TARGET..."
# Get target group ARNs
BLUE_TG=$(aws elbv2 describe-target-groups \
--load-balancer-arn "$ALB_ARN" \
--query "TargetGroups[?Tags[?Key=='Name' && Value=='blue']].TargetGroupArn" \
--output text)
GREEN_TG=$(aws elbv2 describe-target-groups \
--load-balancer-arn "$ALB_ARN" \
// ... (see reference guides for full implementation)
Reference Guides
Detailed implementations in the references/ directory:
| Guide | Contents |
|---|---|
| Blue-Green with Load Balancer | Blue-Green with Load Balancer |
| Blue-Green Rollback Script | Blue-Green Rollback Script |
| Monitoring and Validation | Monitoring and Validation |
Best Practices
✅ DO
- Follow established patterns and conventions
- Write clean, maintainable code
- Add appropriate documentation
- Test thoroughly before deploying
❌ DON'T
- Skip testing or validation
- Ignore error handling
- Hard-code configuration values
Signals
- GitHub stars
- 336
- Forks
- 55
- Last commit
- Mar 2026
Advanced
- Catalog kind
- skill
- Gateway key
blue-green-deployment- Source
- github.com/aj-geddes/useful-ai-prompts