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rmyndharis/antigravity-skills/deployment-pipeline-design

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version: "1.0.2" name: deployment-pipeline-design description: Design multi-stage CI/CD pipelines with approval gates, security checks, and deployment orchestration. Use when architecting deployment workflows, setting up continuous delivery, or implementing GitOps practices.

Deployment Pipeline Design

Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies.

Do not use this skill when

The task is unrelated to deployment pipeline design
You need a different domain or tool outside this scope

Instructions

Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.

Purpose

Design robust, secure deployment pipelines that balance speed with safety through proper stage organization and approval workflows.

Use this skill when

Design CI/CD architecture
Implement deployment gates
Configure multi-environment pipelines
Establish deployment best practices
Implement progressive delivery

Pipeline Stages

Standard Pipeline Flow

┌─────────┐   ┌──────┐   ┌─────────┐   ┌────────┐   ┌──────────┐
│  Build  │ → │ Test │ → │ Staging │ → │ Approve│ → │Production│
└─────────┘   └──────┘   └─────────┘   └────────┘   └──────────┘

Detailed Stage Breakdown

Source - Code checkout
Build - Compile, package, containerize
Test - Unit, integration, security scans
Staging Deploy - Deploy to staging environment
Integration Tests - E2E, smoke tests
Approval Gate - Manual approval required
Production Deploy - Canary, blue-green, rolling
Verification - Health checks, monitoring
Rollback - Automated rollback on failure

Approval Gate Patterns

Pattern 1: Manual Approval

yaml

# GitHub Actions
production-deploy:
  needs: staging-deploy
  environment:
    name: production
    url: https://app.example.com
  runs-on: ubuntu-latest
  steps:
    - name: Deploy to production
      run: |
        # Deployment commands

Pattern 2: Time-Based Approval

yaml

# GitLab CI
deploy:production:
  stage: deploy
  script:
    - deploy.sh production
  environment:
    name: production
  when: delayed
  start_in: 30 minutes
  only:
    - main

Pattern 3: Multi-Approver

yaml

# Azure Pipelines
stages:
- stage: Production
  dependsOn: Staging
  jobs:
  - deployment: Deploy
    environment:
      name: production
      resourceType: Kubernetes
    strategy:
      runOnce:
        preDeploy:
          steps:
          - task: ManualValidation@0
            inputs:
              notifyUsers: 'team-leads@example.com'
              instructions: 'Review staging metrics before approving'

Deployment Strategies

1. Rolling Deployment

yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 2
      maxUnavailable: 1

Characteristics:

Gradual rollout
Zero downtime
Easy rollback
Best for most applications

2. Blue-Green Deployment

yaml

# Blue (current)
kubectl apply -f blue-deployment.yaml
kubectl label service my-app version=blue
 
# Green (new)
kubectl apply -f green-deployment.yaml
# Test green environment
kubectl label service my-app version=green
 
# Rollback if needed
kubectl label service my-app version=blue

Characteristics:

Instant switchover
Easy rollback
Doubles infrastructure cost temporarily
Good for high-risk deployments

3. Canary Deployment

yaml

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    canary:
      steps:
      - setWeight: 10
      - pause: {duration: 5m}
      - setWeight: 25
      - pause: {duration: 5m}
      - setWeight: 50
      - pause: {duration: 5m}
      - setWeight: 100

Characteristics:

Gradual traffic shift
Risk mitigation
Real user testing
Requires service mesh or similar

4. Feature Flags

python

from flagsmith import Flagsmith
flagsmith = Flagsmith(environment_key="API_KEY")
if flagsmith.has_feature("new_checkout_flow"):
    # New code path
    process_checkout_v2()
else:
    # Existing code path
    process_checkout_v1()

Characteristics:

Deploy without releasing
A/B testing
Instant rollback
Granular control

Pipeline Orchestration

Multi-Stage Pipeline Example

yaml

name: Production Pipeline
 
on:
  push:
    branches: [ main ]
 
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build application
        run: make build
      - name: Build Docker image
        run: docker build -t myapp:${{ github.sha }} .
      - name: Push to registry
        run: docker push myapp:${{ github.sha }}
 
  test:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Unit tests
        run: make test
      - name: Security scan
        run: trivy image myapp:${{ github.sha }}
 
  deploy-staging:
    needs: test
    runs-on: ubuntu-latest
    environment:
      name: staging
    steps:
      - name: Deploy to staging
        run: kubectl apply -f k8s/staging/
 
  integration-test:
    needs: deploy-staging
    runs-on: ubuntu-latest
    steps:
      - name: Run E2E tests
        run: npm run test:e2e
 
  deploy-production:
    needs: integration-test
    runs-on: ubuntu-latest
    environment:
      name: production
    steps:
      - name: Canary deployment
        run: |
          kubectl apply -f k8s/production/
          kubectl argo rollouts promote my-app
 
  verify:
    needs: deploy-production
    runs-on: ubuntu-latest
    steps:
      - name: Health check
        run: curl -f https://app.example.com/health
      - name: Notify team
        run: |
          curl -X POST ${{ secrets.SLACK_WEBHOOK }} \
            -d '{"text":"Production deployment successful!"}'

Pipeline Best Practices

Fail fast - Run quick tests first
Parallel execution - Run independent jobs concurrently
Caching - Cache dependencies between runs
Artifact management - Store build artifacts
Environment parity - Keep environments consistent
Secrets management - Use secret stores (Vault, etc.)
Deployment windows - Schedule deployments appropriately
Monitoring integration - Track deployment metrics
Rollback automation - Auto-rollback on failures
Documentation - Document pipeline stages

Rollback Strategies

Automated Rollback

yaml

deploy-and-verify:
  steps:
    - name: Deploy new version
      run: kubectl apply -f k8s/
 
    - name: Wait for rollout
      run: kubectl rollout status deployment/my-app
 
    - name: Health check
      id: health
      run: |
        for i in {1..10}; do
          if curl -sf https://app.example.com/health; then
            exit 0
          fi
          sleep 10
        done
        exit 1
 
    - name: Rollback on failure
      if: failure()
      run: kubectl rollout undo deployment/my-app

Manual Rollback

bash

# List revision history
kubectl rollout history deployment/my-app
 
# Rollback to previous version
kubectl rollout undo deployment/my-app
 
# Rollback to specific revision
kubectl rollout undo deployment/my-app --to-revision=3

Monitoring and Metrics

Key Pipeline Metrics

Deployment Frequency - How often deployments occur
Lead Time - Time from commit to production
Change Failure Rate - Percentage of failed deployments
Mean Time to Recovery (MTTR) - Time to recover from failure
Pipeline Success Rate - Percentage of successful runs
Average Pipeline Duration - Time to complete pipeline

Integration with Monitoring

yaml

- name: Post-deployment verification
  run: |
    # Wait for metrics stabilization
    sleep 60
 
    # Check error rate
    ERROR_RATE=$(curl -s "$PROMETHEUS_URL/api/v1/query?query=rate(http_errors_total[5m])" | jq '.data.result[0].value[1]')
 
    if (( $(echo "$ERROR_RATE > 0.01" | bc -l) )); then
      echo "Error rate too high: $ERROR_RATE"
      exit 1
    fi

Related Skills

github-actions-templates - For GitHub Actions implementation
gitlab-ci-patterns - For GitLab CI implementation
secrets-management - For secrets handling

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