Skip to content
All Skills

GitLab CI Patterns

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.

DevOps & Cloud|v1|Updated 7/14/2026|GitHub source
MCP get_skill({ skillId: "gitlab-ci-patterns-85ea3470" })

Use this skill with your agent

Create a free account and connect via MCP

Get Started Free
# 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

```yaml
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

```yaml
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

```yaml
.deploy_template: &deploy_template
  image: bitnami/kubectl:1.31
  before_script:
    - kubectl config set-cluster k8s --server="$KUBE_URL" --insecure-skip-tls-verify=true
    - kubectl config set-credentials admin --token="$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

```yaml
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

```yaml
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

```yaml
# 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

```yaml
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
#broad-capability#engineering#agent-skills#ci#cd#pipelinesgitlab

Related Skills

More skills in DevOps & Cloud

1password Skill

1password Skill linked from Juliano Barbosa Claude Code Skills, with the upstream skill instructions available on GitHub.

#github#broad-capabilityMIT

Actions Manager

GitHub Actions command center -- view workflow runs, read logs, re-run failed jobs, manage workflows, and debug CI failures entirely from the editor. Bypasses the deeply nested, visually-dependent Actions UI that is largely inaccessible to screen readers.

#broad-capability#accessibilityMIT

Airunway Aks Setup

Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".

#broad-capability#developmentMIT

Alz Accelerator

Deploy Azure Landing Zones using the ALZ Accelerator with AVM (Azure Verified Modules). Use this skill whenever the user mentions Azure Landing Zones, ALZ, Azure landing zone accelerator, AVM modules for landing zones, deploying management groups, hub-and-spoke networking, Virtual WAN, platform landing zones, or asks about Bicep vs Terraform for Azure infrastructure. Also trigger when the user wants to bootstrap CI/CD for Azure platform deployment, set up management groups hierarchy, or deploy connectivity/identity/management platform subscriptions.

#broad-capability#devopsMIT

Alz Accelerator Skill

Alz Accelerator Skill linked from Juliano Barbosa Claude Code Skills, with the upstream skill instructions available on GitHub.

#github#broad-capabilityMIT

Ansible Conventions and Best Practices

Ansible conventions and best practices

#github-copilot#devopsMIT