gitlab-ci-patterns
Automates the creation of scalable multi-stage GitLab CI/CD pipelines with caching.
Install
mkdir -p .claude/skills/gitlab-ci-patterns && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1626" && unzip -o skill.zip -d .claude/skills/gitlab-ci-patterns && rm skill.zipInstalls to .claude/skills/gitlab-ci-patterns
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Configure multi-stage CI/CD pipelines
- →Implement Docker build and push workflows
- →Manage multi-environment deployments
- →Execute Terraform infrastructure pipelines
- →Integrate security scanning tools
How it works
The skill provides modular YAML patterns for stage orchestration, caching strategies, and deployment automation within GitLab CI.
Inputs & outputs
When to use gitlab-ci-patterns
- →Implementing multi-stage CI/CD pipelines
- →Optimizing build performance with caching
- →Setting up automated testing and deployment
- →Configuring GitLab runners
About this skill
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 $CI_REGISTRY_IMAGE:latest
only:
- main
- tags
Multi-Environment Deployment
.deploy_template: &deploy_template
image: bitnami/kubectl:1.31
before_script:
- kubectl config set-cluster k8s --server="$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
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
When not to use it
- →When using GitHub Actions instead of GitLab
Prerequisites
Limitations
- →Requires GitLab runner configuration
- →Pipeline performance depends on runner resources
How it compares
It provides reusable, production-tested pipeline patterns that replace manual, error-prone configuration of CI/CD stages.
Compared to similar skills
gitlab-ci-patterns side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| gitlab-ci-patterns (this skill) | 10 | 3mo | No flags | Intermediate |
| bazel-build-optimization | 14 | 2mo | No flags | Advanced |
| deployment-pipeline-design | 6 | 2mo | Review | Advanced |
| github-actions-templates | 7 | 3mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by wshobson
View all by wshobson →You might also like
bazel-build-optimization
wshobson
Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
deployment-pipeline-design
wshobson
Design multi-stage CI/CD pipelines with approval gates, security checks, and deployment orchestration. Use when architecting deployment workflows, setting up continuous delivery, or implementing GitOps practices.
github-actions-templates
wshobson
Create production-ready GitHub Actions workflows for automated testing, building, and deploying applications. Use when setting up CI/CD with GitHub Actions, automating development workflows, or creating reusable workflow templates.
k8s-helm
rohitg00
Manage Helm charts, releases, and repositories. Use for Helm installations, upgrades, rollbacks, chart development, and release management.
cloudflare-deploy
davila7
Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.
mlops-engineer
sickn33
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.