mcpbeat Sign in

Blue Green Deploy Agent Skill

Configure zero-downtime deployment strategies including blue-green, canary, and rolling deployments. Implement traffic shifting, health checks, and rollback procedures. Use when implementing production deployment strategies or zero-downtime releases.

3k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
511
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill blue-green-deploy

The instruction itself

26 sections, as written by the author

Blue-Green & Deployment Strategies

Implement zero-downtime deployment patterns for production systems.

When to Use This Skill

Use this skill when:

  • Implementing zero-downtime deployments
  • Reducing deployment risk
  • Enabling instant rollbacks
  • Running canary releases
  • Performing A/B testing in production

Prerequisites

  • Load balancer or ingress controller
  • Container orchestration (K8s) or cloud platform
  • CI/CD pipeline
  • Health check endpoints

Deployment Strategy Overview

┌─────────────────────────────────────────────────────────────┐
│                    DEPLOYMENT STRATEGIES                     │
├─────────────┬─────────────┬─────────────┬──────────────────┤
│  Blue-Green │   Canary    │   Rolling   │    Recreate      │
├─────────────┼─────────────┼─────────────┼──────────────────┤
│ Full env    │ Gradual %   │ Pod by pod  │ All at once      │
│ swap        │ rollout     │ replacement │                  │
├─────────────┼─────────────┼─────────────┼──────────────────┤
│ Instant     │ Slow, safe  │ Moderate    │ Fast, risky      │
│ rollback    │ rollback    │ rollback    │                  │
├─────────────┼─────────────┼─────────────┼──────────────────┤
│ 2x resources│ +10-25%     │ Same        │ Same             │
│ needed      │ resources   │ resources   │                  │
└─────────────┴─────────────┴─────────────┴──────────────────┘

Blue-Green Deployment

Concept

Before:
┌─────────┐     ┌───────────────┐
│  Users  │────▶│  Blue (v1)    │ ◀── Active
└─────────┘     └───────────────┘
                ┌───────────────┐
                │  Green (v2)   │ ◀── Staging
                └───────────────┘

After Switch:
┌─────────┐     ┌───────────────┐
│  Users  │     │  Blue (v1)    │ ◀── Standby
└─────────┘     └───────────────┘
      │         ┌───────────────┐
      └────────▶│  Green (v2)   │ ◀── Active
                └───────────────┘

Kubernetes Implementation

# blue-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp-blue
  labels:
    app: myapp
    version: blue
spec:
  replicas: 3
  selector:
    matchLabels:
      app: myapp
      version: blue
  template:
    metadata:
      labels:
        app: myapp
        version: blue
    spec:
      containers:
      - name: myapp
        image: myapp:v1.0.0
        ports:
        - containerPort: 8080
        readinessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
---
# green-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp-green
  labels:
    app: myapp
    version: green
spec:
  replicas: 3
  selector:
    matchLabels:
      app: myapp
      version: green
  template:
    metadata:
      labels:
        app: myapp
        version: green
    spec:
      containers:
      - name: myapp
        image: myapp:v2.0.0
        ports:
        - containerPort: 8080
        readinessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
---
# service.yaml - Switch by changing selector
apiVersion: v1
kind: Service
metadata:
  name: myapp
spec:
  selector:
    app: myapp
    version: blue  # Change to 'green' to switch
  ports:
  - port: 80
    targetPort: 8080

Switch Script

#!/bin/bash
# blue-green-switch.sh

CURRENT=$(kubectl get svc myapp -o jsonpath='{.spec.selector.version}')
NEW_VERSION=$1

echo "Current version: $CURRENT"
echo "Switching to: $NEW_VERSION"

# Verify new deployment is ready
kubectl rollout status deployment/myapp-$NEW_VERSION

# Check health
HEALTH=$(kubectl exec -it deployment/myapp-$NEW_VERSION -- curl -s localhost:8080/health)
if [ "$HEALTH" != "ok" ]; then
  echo "Health check failed"
  exit 1
fi

# Switch traffic
kubectl patch svc myapp -p "{\"spec\":{\"selector\":{\"version\":\"$NEW_VERSION\"}}}"

echo "Switched to $NEW_VERSION"

AWS ECS Blue-Green

# AWS CodeDeploy appspec.yml
version: 0.0
Resources:
  - TargetService:
      Type: AWS::ECS::Service
      Properties:
        TaskDefinition: "arn:aws:ecs:region:account:task-definition/myapp:2"
        LoadBalancerInfo:
          ContainerName: "myapp"
          ContainerPort: 8080
Hooks:
  - BeforeInstall: "LambdaFunctionToValidateBeforeTrafficShift"
  - AfterInstall: "LambdaFunctionToValidateAfterTrafficShift"
  - AfterAllowTestTraffic: "LambdaFunctionToValidateTestTraffic"
  - BeforeAllowTraffic: "LambdaFunctionToValidateBeforeAllowTraffic"
  - AfterAllowTraffic: "LambdaFunctionToValidateAfterAllowTraffic"

Canary Deployment

Kubernetes with Istio

# VirtualService for traffic splitting
apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: myapp
spec:
  hosts:
  - myapp
  http:
  - match:
    - headers:
        x-canary:
          exact: "true"
    route:
    - destination:
        host: myapp
        subset: canary
  - route:
    - destination:
        host: myapp
        subset: stable
      weight: 90
    - destination:
        host: myapp
        subset: canary
      weight: 10
---
apiVersion: networking.istio.io/v1beta1
kind: DestinationRule
metadata:
  name: myapp
spec:
  host: myapp
  subsets:
  - name: stable
    labels:
      version: stable
  - name: canary
    labels:
      version: canary

Argo Rollouts

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: myapp
spec:
  replicas: 5
  strategy:
    canary:
      steps:
      - setWeight: 10
      - pause: {duration: 5m}
      - setWeight: 25
      - pause: {duration: 5m}
      - setWeight: 50
      - pause: {duration: 5m}
      - setWeight: 75
      - pause: {duration: 5m}
      analysis:
        templates:
        - templateName: success-rate
        startingStep: 2
        args:
        - name: service-name
          value: myapp
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
      - name: myapp
        image: myapp:v2.0.0
        ports:
        - containerPort: 8080
---
apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: success-rate
spec:
  args:
  - name: service-name
  metrics:
  - name: success-rate
    interval: 1m
    successCondition: result[0] >= 0.95
    failureLimit: 3
    provider:
      prometheus:
        address: http://prometheus:9090
        query: |
          sum(rate(http_requests_total{service="{{args.service-name}}",status=~"2.*"}[5m]))
          /
          sum(rate(http_requests_total{service="{{args.service-name}}"}[5m]))

Rolling Deployment

Kubernetes Default

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 5
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1        # Max pods above desired
      maxUnavailable: 0   # Max pods unavailable
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
      - name: myapp
        image: myapp:v2.0.0
        ports:
        - containerPort: 8080
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 10
          periodSeconds: 10

Rolling Update Commands

# Update image
kubectl set image deployment/myapp myapp=myapp:v2.0.0

# Watch rollout
kubectl rollout status deployment/myapp

# Pause rollout
kubectl rollout pause deployment/myapp

# Resume rollout
kubectl rollout resume deployment/myapp

# Rollback
kubectl rollout undo deployment/myapp

# Rollback to specific revision
kubectl rollout undo deployment/myapp --to-revision=2

# View history
kubectl rollout history deployment/myapp

Health Checks

Comprehensive Health Endpoint

# Flask health endpoint
from flask import Flask, jsonify
import psycopg2
import redis

app = Flask(__name__)

@app.route('/health')
def health():
    """Liveness probe - is the app running?"""
    return jsonify({'status': 'healthy'}), 200

@app.route('/ready')
def ready():
    """Readiness probe - can the app serve traffic?"""
    checks = {}
    
    # Database check
    try:
        conn = psycopg2.connect(DATABASE_URL)
        conn.close()
        checks['database'] = 'ok'
    except Exception as e:
        checks['database'] = str(e)
        return jsonify({'status': 'unhealthy', 'checks': checks}), 503
    
    # Redis check
    try:
        r = redis.from_url(REDIS_URL)
        r.ping()
        checks['redis'] = 'ok'
    except Exception as e:
        checks['redis'] = str(e)
        return jsonify({'status': 'unhealthy', 'checks': checks}), 503
    
    return jsonify({'status': 'healthy', 'checks': checks}), 200

Rollback Procedures

Automated Rollback

#!/bin/bash
# auto-rollback.sh

DEPLOYMENT=$1
THRESHOLD=0.95
INTERVAL=60

echo "Monitoring deployment $DEPLOYMENT"

while true; do
  # Get success rate from Prometheus
  SUCCESS_RATE=$(curl -s "http://prometheus:9090/api/v1/query?query=sum(rate(http_requests_total{status=~\"2.*\"}[5m]))/sum(rate(http_requests_total[5m]))" | jq -r '.data.result[0].value[1]')
  
  echo "Current success rate: $SUCCESS_RATE"
  
  if (( $(echo "$SUCCESS_RATE < $THRESHOLD" | bc -l) )); then
    echo "Success rate below threshold! Rolling back..."
    kubectl rollout undo deployment/$DEPLOYMENT
    exit 1
  fi
  
  sleep $INTERVAL
done

Manual Rollback Checklist

## Rollback Checklist

### Before Rollback
- [ ] Confirm issue is deployment-related
- [ ] Document current error rates
- [ ] Notify team in #deployments channel

### During Rollback
- [ ] Execute rollback command
- [ ] Monitor rollback progress
- [ ] Verify old version is serving traffic

### After Rollback
- [ ] Confirm error rates normalized
- [ ] Update incident ticket
- [ ] Schedule post-mortem

Common Issues

Issue: Slow Deployments

Problem: Rollout takes too long

Solution: Increase maxSurge, decrease minReadySeconds

Issue: Failed Health Checks

Problem: Pods not becoming ready

Solution: Check probe endpoints, increase timeouts

Issue: Traffic During Rollback

Problem: Errors during switch

Solution: Use connection draining, implement graceful shutdown

Best Practices

  • Always implement health checks
  • Use connection draining
  • Test rollback procedures regularly
  • Monitor key metrics during deployment
  • Implement circuit breakers
  • Use deployment slots/environments
  • Automate deployment verification
  • Document rollback procedures
  • kubernetes-ops - K8s deployment basics
  • argocd-gitops - GitOps deployments
  • feature-flags - Progressive rollout

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

13k tokens
Capacity
by microsoft
vendor ×3

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

6k tokens scripts
Customize
by microsoft
vendor ×3

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

8k tokens
Deploy Model
by microsoft
vendor ×3

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

26k tokens scripts
Preset
by microsoft
vendor ×3

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take bagelhole/blue-green-deploy from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.