gitlab-ci-patterns
π―SkillGives 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.
Installation
npx skills add wshobson/agents --skill gitlab-ci-patternsRun 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.
/gitlab-ci-patternsInstall 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
SKILL.md
View on GitHub βBuild 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
- Use specific image tags (node:20, not node:latest)
- Cache dependencies appropriately
- Use artifacts for build outputs
- Implement manual gates for production
- Use environments for deployment tracking
- Enable merge request pipelines
- Use pipeline schedules for recurring jobs
- Implement security scanning
- Use CI/CD variables for secrets
- Monitor pipeline performance
Related Skills
github-actions-templates- For GitHub Actionsdeployment-pipeline-design- For architecturesecrets-management- For secrets handling
More from this repository10
Helps you build a consistent look for your app with Tailwind CSS v4. It sets up shared colors, sizes, dark mode and reusable components so every screen matches.
A guide to TypeScript's advanced type features, which describe the shape of your data so mistakes are caught before the code runs. It covers reusable types, types that change based on rules, and built-in helper types, with short examples.
Guidance for building reliable server-side apps with Node.js, using Express or Fastify. It covers login, error handling, databases, API design and a list of proven best practices.
Intelligent automation and multi-agent orchestration for Claude Code
Claude Code plugin for AI-powered framework migration workflows, coordinating specialized agents to plan and execute transitions between technology stacks as part of the wshobson/agents marketplace.
A Claude Code plugin for payment system integration covering Stripe, PayPal, and billing platforms with checkout flows, subscription management, and webhook handling in the wshobson/agents ecosystem.
A Claude Code plugin bundling core development tools and agents for everyday tasks like debugging and code generation, part of the wshobson/agents marketplace with 73 focused plugins.
Claude Code plugin for observability and monitoring including logging, metrics, distributed tracing, and alerting, part of the wshobson/agents marketplace with 73 focused plugins.
A Claude Code plugin providing specialized AI agents for deployment strategy planning and execution, covering blue-green deployments, canary releases, rolling updates, and zero-downtime patterns.
Claude Code plugin for JVM language development including Java, Kotlin, and Scala with specialized agents for idiomatic patterns, build tools, and runtime optimization in the wshobson/agents marketplace.