🎯

gitlab-ci-patterns

🎯Skill

Gives ready-made examples for setting up GitLab CI/CD, GitLab's built-in system that automatically tests, builds and releases your code. It covers stages, caching to save time, security checks and deployments.

wshobson/agents10,047 Installs27,673Added Jan 31, 2026

Installation

$npx skills add wshobson/agents --skill gitlab-ci-patterns

Run it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).

Uses skills.sh Β· Node.js 22.20+

After installing

Ask for related work and the agent uses it on its own, or call it yourself.

Run it (Claude Code)/gitlab-ci-patterns
Install by hand

Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.

Open the skill folder β†’
  • This project .claude/skills/gitlab-ci-patterns/
  • All projects ~/.claude/skills/gitlab-ci-patterns/

These paths are for Claude Code. For other tools, the command above puts it in the right place.

What this skill does

Overview

This skill helps you write GitLab CI/CD pipelines. A pipeline is a list of automatic steps, like build, test and deploy, that run when you push code. The skill gives example settings files you can copy and adjust.

Key Features

  • Split the work into clear stages such as build, test and deploy.
  • Build Docker images, which are packaged copies of your app, and upload them to GitLab.
  • Deploy to staging and production, with a manual approval step before production.
  • Reuse downloaded files between runs with caching so pipelines finish faster.
  • Add security scans that look for weak spots in your code and images.

Who is this for?

This is for developers and DevOps teams who keep their code on GitLab. It helps both beginners setting up a first pipeline and teams trying to speed up a slow one.

Summarized by AI from this skill's SKILL.md. Suggest a better one

Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.

GitLab CI Patterns

Comprehensive GitLab CI/CD pipeline patterns for automated testing, building, and deployment.

Purpose

Create efficient GitLab CI pipelines with proper stage organization, caching, and deployment strategies.

When to Use

  • Automate GitLab-based CI/CD
  • Implement multi-stage pipelines
  • Configure GitLab Runners
  • Deploy to Kubernetes from GitLab
  • Implement GitOps workflows

Basic Pipeline Structure

stages:
  - build
  - test
  - deploy

variables:
  DOCKER_DRIVER: overlay2
  DOCKER_TLS_CERTDIR: "/certs"

build:
  stage: build
  image: node:20
  script:
    - npm ci
    - npm run build
  artifacts:
    paths:
      - dist/
    expire_in: 1 hour
  cache:
    key: ${CI_COMMIT_REF_SLUG}
    paths:
      - node_modules/

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

deploy:
  stage: deploy
  image: bitnami/kubectl:1.31
  script:
    - kubectl apply -f k8s/
    - kubectl rollout status deployment/my-app
  only:
    - main
  environment:
    name: production
    url: https://app.example.com

Docker Build and Push

build-docker:
  stage: build
  image: docker:24
  services:
    - docker:24-dind
  before_script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
  script:
    - docker build -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA .
    - docker build -t $CI_REGISTRY_IMAGE:latest .
    - docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
    - docker push $CI_REGISTRY_IMAGE:latest
  only:
    - main
    - tags

Multi-Environment Deployment

Set KUBE_CA_CERT_FILE as a GitLab file variable containing the cluster CA certificate, and provide KUBE_TOKEN through a protected, masked CI variable. The file variable contains a path that kubectl uses to verify the API server certificate. Protect both develop and main so the deployment jobs can read the protected token.

.deploy_template: &deploy_template
  image: bitnami/kubectl:1.31
  before_script:
    - kubectl config set-cluster k8s --server="$KUBE_URL" --certificate-authority="$KUBE_CA_CERT_FILE" --embed-certs=true
    - kubectl config set-credentials admin --token="$KUBE_TOKEN"
    - kubectl config set-context default --cluster=k8s --user=admin
    - kubectl config use-context default

deploy:staging:
  <<: *deploy_template
  stage: deploy
  script:
    - kubectl apply -f k8s/ -n staging
    - kubectl rollout status deployment/my-app -n staging
  environment:
    name: staging
    url: https://staging.example.com
  only:
    - develop

deploy:production:
  <<: *deploy_template
  stage: deploy
  script:
    - kubectl apply -f k8s/ -n production
    - kubectl rollout status deployment/my-app -n production
  environment:
    name: production
    url: https://app.example.com
  when: manual
  only:
    - main

Terraform Pipeline

stages:
  - validate
  - plan
  - apply

variables:
  TF_ROOT: ${CI_PROJECT_DIR}/terraform
  TF_VERSION: "1.6.0"

before_script:
  - cd ${TF_ROOT}
  - terraform --version

validate:
  stage: validate
  image: hashicorp/terraform:${TF_VERSION}
  script:
    - terraform init -backend=false
    - terraform validate
    - terraform fmt -check

plan:
  stage: plan
  image: hashicorp/terraform:${TF_VERSION}
  script:
    - terraform init
    - terraform plan -out=tfplan
  artifacts:
    paths:
      - ${TF_ROOT}/tfplan
    expire_in: 1 day

apply:
  stage: apply
  image: hashicorp/terraform:${TF_VERSION}
  script:
    - terraform init
    - terraform apply -auto-approve tfplan
  dependencies:
    - plan
  when: manual
  only:
    - main

Security Scanning

include:
  - template: Security/SAST.gitlab-ci.yml
  - template: Security/Dependency-Scanning.gitlab-ci.yml
  - template: Security/Container-Scanning.gitlab-ci.yml

trivy-scan:
  stage: test
  image: aquasec/trivy:0.58.0
  script:
    - trivy image --exit-code 1 --severity HIGH,CRITICAL $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
  allow_failure: true

Caching Strategies

# Cache node_modules
build:
  cache:
    key: ${CI_COMMIT_REF_SLUG}
    paths:
      - node_modules/
    policy: pull-push

# Global cache
cache:
  key: ${CI_COMMIT_REF_SLUG}
  paths:
    - .cache/
    - vendor/

# Separate cache per job
job1:
  cache:
    key: job1-cache
    paths:
      - build/

job2:
  cache:
    key: job2-cache
    paths:
      - dist/

Dynamic Child Pipelines

generate-pipeline:
  stage: build
  script:
    - python generate_pipeline.py > child-pipeline.yml
  artifacts:
    paths:
      - child-pipeline.yml

trigger-child:
  stage: deploy
  trigger:
    include:
      - artifact: child-pipeline.yml
        job: generate-pipeline
    strategy: depend

Best Practices

  1. Use specific image tags (node:20, not node:latest)
  2. Cache dependencies appropriately
  3. Use artifacts for build outputs
  4. Implement manual gates for production
  5. Use environments for deployment tracking
  6. Enable merge request pipelines
  7. Use pipeline schedules for recurring jobs
  8. Implement security scanning
  9. Use CI/CD variables for secrets
  10. Monitor pipeline performance

Related Skills

  • github-actions-templates - For GitHub Actions
  • deployment-pipeline-design - For architecture
  • secrets-management - For secrets handling

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