Agent Skills
› rmyndharis/antigravity-skills
› deployment-pipeline-design
deployment-pipeline-design
GitHub设计多阶段CI/CD流水线，涵盖构建、测试、审批门控及生产部署编排。支持GitOps、渐进式交付及蓝绿/滚动等策略，平衡发布速度与安全性。
Trigger Scenarios
设计CI/CD架构
实施部署门控
配置多环境流水线
建立部署最佳实践
实现渐进式交付
Install
npx skills add rmyndharis/antigravity-skills --skill deployment-pipeline-design -g -y
SKILL.md
Frontmatter
{
"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
# 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
# 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
# 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
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
# 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
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
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
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 ${{ secrets.SLACK_WEBHOOK }} \
-d '{"text":"Production deployment successful!"}'
Pipeline Best Practices
- Fail fast - Run quick tests first
- Parallel execution - Run independent jobs concurrently
- Caching - Cache dependencies between runs
- Artifact management - Store build artifacts
- Environment parity - Keep environments consistent
- Secrets management - Use secret stores (Vault, etc.)
- Deployment windows - Schedule deployments appropriately
- Monitoring integration - Track deployment metrics
- Rollback automation - Auto-rollback on failures
- Documentation - Document pipeline stages
Rollback Strategies
Automated Rollback
deploy-and-verify:
steps:
- name: Deploy new version
run: kubectl apply -f k8s/
- name: Wait for rollout
run: kubectl rollout status deployment/my-app
- name: Health check
id: health
run: |
for i in {1..10}; do
if curl -sf https://app.example.com/health; then
exit 0
fi
sleep 10
done
exit 1
- name: Rollback on failure
if: failure()
run: kubectl rollout undo deployment/my-app
Manual Rollback
# List revision history
kubectl rollout history deployment/my-app
# Rollback to previous version
kubectl rollout undo deployment/my-app
# Rollback to specific revision
kubectl rollout undo deployment/my-app --to-revision=3
Monitoring and Metrics
Key Pipeline Metrics
- Deployment Frequency - How often deployments occur
- Lead Time - Time from commit to production
- Change Failure Rate - Percentage of failed deployments
- Mean Time to Recovery (MTTR) - Time to recover from failure
- Pipeline Success Rate - Percentage of successful runs
- Average Pipeline Duration - Time to complete pipeline
Integration with Monitoring
- name: Post-deployment verification
run: |
# Wait for metrics stabilization
sleep 60
# Check error rate
ERROR_RATE=$(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 implementationgitlab-ci-patterns- For GitLab CI implementationsecrets-management- For secrets handling
Version History
- e63f7dd Current 2026-07-05 09:31
