gitlab-ci-patterns
Teaches your AI agent to ship projects and manage infrastructure and automate repetitive tasks.
What it does
This set of instructions helps create automated workflows for GitLab CI/CD. It organizes tasks like building, testing, and deploying your code efficiently.
When to use it
- ✓Automating repetitive tasks in GitLab.
- ✓Organizing the continuous integration/continuous deployment process.
- ✓Setting up automatic testing and deployments.
Example of what to ask your AI
“I want to set up a CI/CD pipeline in GitLab to build, test, and deploy my web application.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It defines clear steps (stages) for each part of the process, such as 'build', 'test', and 'deploy'. It uses configurations to speed up the process (caching) and ensure deployments are secure.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill gitlab-ci-patternsOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/cicd-automation/skills/gitlab-ci-patterns/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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/
…
FAQ
What are 'stages'?
These are the different phases of your pipeline, like building the code, running tests, and then deploying the application. Each stage happens in a specific order.
What is 'caching'?
It's a way to temporarily store files that are used frequently, like code dependencies. This speeds up the pipeline in subsequent runs.
What are 'runners'?
These are the 'workers' of GitLab CI/CD. They execute the tasks defined in your pipeline on machines or containers.
Details
Original description by the author
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.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4822
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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