Back to Skills

cicd-pipeline-setup

aj-geddes
Updated Today
15 views
7
7
View on GitHub
Metatestingautomationdesign

About

This skill helps developers design and implement CI/CD pipelines using tools like GitHub Actions, GitLab CI, Jenkins, or CircleCI. It automates testing, building, security checks, and deployment to multiple environments. Use it to create robust workflows for release management and artifact handling with minimal manual intervention.

Documentation

CI/CD Pipeline Setup

Overview

Build automated continuous integration and deployment pipelines that test code, build artifacts, run security checks, and deploy to multiple environments with minimal manual intervention.

When to Use

  • Automated code testing and quality checks
  • Containerized application builds
  • Multi-environment deployments
  • Release management and versioning
  • Automated security scanning
  • Performance testing integration
  • Artifact management and registry

Implementation Examples

1. GitHub Actions Workflow

# .github/workflows/deploy.yml
name: Build and Deploy

on:
  push:
    branches:
      - main
      - develop
  pull_request:
    branches:
      - main
  workflow_dispatch:

env:
  REGISTRY: ghcr.io
  IMAGE_NAME: ${{ github.repository }}

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        node-version: [18.x, 20.x]

    steps:
      - uses: actions/checkout@v4

      - name: Setup Node.js ${{ matrix.node-version }}
        uses: actions/setup-node@v4
        with:
          node-version: ${{ matrix.node-version }}
          cache: npm

      - name: Install dependencies
        run: npm ci

      - name: Run linting
        run: npm run lint

      - name: Run tests
        run: npm run test:coverage

      - name: Upload coverage to Codecov
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage/coverage-final.json
          flags: unittests
          name: codecov-umbrella

  security:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Run Snyk Security Scan
        uses: snyk/actions/node@master
        env:
          SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }}
        with:
          args: --severity-threshold=high

      - name: Run Trivy vulnerability scan
        uses: aquasecurity/trivy-action@master
        with:
          scan-type: 'fs'
          scan-ref: '.'

  build:
    needs: [test, security]
    runs-on: ubuntu-latest
    permissions:
      contents: read
      packages: write

    steps:
      - uses: actions/checkout@v4

      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3

      - name: Log in to Container Registry
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Extract metadata
        id: meta
        uses: docker/metadata-action@v5
        with:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
          tags: |
            type=ref,event=branch
            type=semver,pattern={{version}}
            type=sha

      - name: Build and push Docker image
        uses: docker/build-push-action@v5
        with:
          context: .
          push: ${{ github.event_name != 'pull_request' }}
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}
          cache-from: type=gha
          cache-to: type=gha,mode=max

  deploy:
    needs: build
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main' && github.event_name == 'push'

    steps:
      - uses: actions/checkout@v4

      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v4
        with:
          role-to-assume: arn:aws:iam::${{ secrets.AWS_ACCOUNT_ID }}:role/GithubActionsRole
          aws-region: us-east-1

      - name: Deploy to ECS
        run: |
          aws ecs update-service \
            --cluster production \
            --service myapp \
            --force-new-deployment

      - name: Verify deployment
        run: |
          aws ecs wait services-stable \
            --cluster production \
            --services myapp

2. GitLab CI Pipeline

# .gitlab-ci.yml
stages:
  - test
  - build
  - deploy

variables:
  DOCKER_DRIVER: overlay2
  DOCKER_TLS_CERTDIR: ""
  IMAGE_TAG: $CI_COMMIT_SHA

test:
  stage: test
  image: node:20
  cache:
    paths:
      - node_modules/
  script:
    - npm ci
    - npm run lint
    - npm run test:coverage
  artifacts:
    reports:
      coverage_report:
        coverage_format: cobertura
        path: coverage/cobertura-coverage.xml
  coverage: '/Lines\s*:\s*(\d+.\d+)%/'

security:
  stage: test
  image: aquasec/trivy:latest
  script:
    - trivy fs --exit-code 0 --severity HIGH,CRITICAL .

build:
  stage: build
  image: docker:latest
  services:
    - docker:dind
  before_script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
  script:
    - docker build -t $CI_REGISTRY_IMAGE:$IMAGE_TAG .
    - docker push $CI_REGISTRY_IMAGE:$IMAGE_TAG
    - docker tag $CI_REGISTRY_IMAGE:$IMAGE_TAG $CI_REGISTRY_IMAGE:latest
    - docker push $CI_REGISTRY_IMAGE:latest
  only:
    - main
    - develop

deploy_staging:
  stage: deploy
  image: alpine:latest
  before_script:
    - apk add --no-cache aws-cli
  script:
    - aws ecs update-service --cluster staging --service myapp --force-new-deployment
  environment:
    name: staging
    url: https://staging.myapp.com
  only:
    - develop

deploy_production:
  stage: deploy
  image: alpine:latest
  before_script:
    - apk add --no-cache aws-cli
  script:
    - aws ecs update-service --cluster production --service myapp --force-new-deployment
  environment:
    name: production
    url: https://myapp.com
  when: manual
  only:
    - main

3. Jenkins Pipeline

// Jenkinsfile
pipeline {
    agent any

    options {
        buildDiscarder(logRotator(numToKeepStr: '10'))
        timeout(time: 1, unit: 'HOURS')
        timestamps()
    }

    environment {
        REGISTRY = 'gcr.io'
        PROJECT_ID = 'my-project'
        IMAGE_NAME = 'myapp'
        IMAGE_TAG = "${BUILD_NUMBER}-${GIT_COMMIT.take(7)}"
    }

    stages {
        stage('Checkout') {
            steps {
                checkout scm
                script {
                    GIT_COMMIT_MSG = sh(
                        script: "git log -1 --pretty=%B",
                        returnStdout: true
                    ).trim()
                }
            }
        }

        stage('Install') {
            steps {
                sh 'npm ci'
            }
        }

        stage('Lint') {
            steps {
                sh 'npm run lint'
            }
        }

        stage('Test') {
            steps {
                sh 'npm run test:coverage'
                publishHTML([
                    reportDir: 'coverage',
                    reportFiles: 'index.html',
                    reportName: 'Coverage Report'
                ])
            }
        }

        stage('Build Image') {
            when {
                branch 'main'
            }
            steps {
                script {
                    sh '''
                        docker build -t ${REGISTRY}/${PROJECT_ID}/${IMAGE_NAME}:${IMAGE_TAG} .
                        docker tag ${REGISTRY}/${PROJECT_ID}/${IMAGE_NAME}:${IMAGE_TAG} \
                                   ${REGISTRY}/${PROJECT_ID}/${IMAGE_NAME}:latest
                    '''
                }
            }
        }

        stage('Push Image') {
            when {
                branch 'main'
            }
            steps {
                sh '''
                    docker push ${REGISTRY}/${PROJECT_ID}/${IMAGE_NAME}:${IMAGE_TAG}
                    docker push ${REGISTRY}/${PROJECT_ID}/${IMAGE_NAME}:latest
                '''
            }
        }

        stage('Deploy Staging') {
            when {
                branch 'develop'
            }
            steps {
                sh '''
                    kubectl set image deployment/myapp myapp=${REGISTRY}/${PROJECT_ID}/${IMAGE_NAME}:${IMAGE_TAG} \
                        -n staging --record
                    kubectl rollout status deployment/myapp -n staging
                '''
            }
        }

        stage('Deploy Production') {
            when {
                branch 'main'
            }
            input {
                message "Deploy to production?"
                ok "Deploy"
            }
            steps {
                sh '''
                    kubectl set image deployment/myapp myapp=${REGISTRY}/${PROJECT_ID}/${IMAGE_NAME}:${IMAGE_TAG} \
                        -n production --record
                    kubectl rollout status deployment/myapp -n production
                '''
            }
        }
    }

    post {
        always {
            cleanWs()
        }
        success {
            slackSend(
                channel: '#deployments',
                message: "Build ${BUILD_NUMBER} succeeded on ${BRANCH_NAME}"
            )
        }
        failure {
            slackSend(
                channel: '#deployments',
                message: "Build ${BUILD_NUMBER} failed on ${BRANCH_NAME}"
            )
        }
    }
}

4. CI/CD Script

#!/bin/bash
# ci-pipeline.sh - Local pipeline validation

set -euo pipefail

echo "Starting CI/CD pipeline..."

# Code quality
echo "Running code quality checks..."
npm run lint
npm run type-check

# Testing
echo "Running tests..."
npm run test:coverage

# Build
echo "Building application..."
npm run build

# Docker build
echo "Building Docker image..."
docker build -t myapp:latest .

# Security scanning
echo "Running security scans..."
trivy image myapp:latest --exit-code 0 --severity HIGH

echo "All pipeline stages completed successfully!"

Best Practices

✅ DO

  • Fail fast with early validation
  • Run tests in parallel when possible
  • Use caching for dependencies
  • Implement proper secret management
  • Gate production deployments with approval
  • Monitor and alert on pipeline failures
  • Use consistent environment configuration
  • Implement infrastructure as code

❌ DON'T

  • Store credentials in pipeline configuration
  • Deploy without automated tests
  • Skip security scanning
  • Allow long-running pipelines
  • Mix staging and production pipelines
  • Ignore test failures
  • Deploy directly to main branch
  • Skip health checks after deployment

Resources

Quick Install

/plugin add https://github.com/aj-geddes/useful-ai-prompts/tree/main/cicd-pipeline-setup

Copy and paste this command in Claude Code to install this skill

GitHub 仓库

aj-geddes/useful-ai-prompts
Path: skills/cicd-pipeline-setup

Related Skills

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill

Algorithmic Art Generation

Meta

This skill helps developers create algorithmic art using p5.js, focusing on generative art, computational aesthetics, and interactive visualizations. It automatically activates for topics like "generative art" or "p5.js visualization" and guides you through creating unique algorithms with features like seeded randomness, flow fields, and particle systems. Use it when you need to build reproducible, code-driven artistic patterns.

View skill