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Guide · Agent skill

How to use the GitLab CI Patterns agent skill

Updated · written with AI from the project's own documentation and checked against our catalogue data

The gitlab-ci-patterns skill gives an AI agent a library of ready-made GitLab CI/CD pipeline patterns to draw on. This guide explains what a skill is, how to install this one, how to ask for pipeline work so the agent uses it, and what to check before you run anything it writes. You do not need prior experience with agent skills to follow it, but you do need a GitLab project to use the output.

GitLab CI Patternswshobson/agents · MIT

Quick start

  1. 1Download and unzip the skill. Download the skill ZIP from this page and unzip it into ~/.claude/skills/ for personal use or into a project's .claude/skills/ folder.
  2. 2Or upload it in the Claude apps. In the Claude apps, open Settings, go to Capabilities, then Skills, and upload the skill.
  3. 3Prepare your GitLab project. Confirm the project has GitLab CI/CD enabled, runners available, and any Docker, Kubernetes or Terraform access the pipeline will need.
  4. 4Ask for a specific pipeline. Describe the stages you want, such as build, test and a manual production deploy, and name GitLab in your request.
  5. 5Review and replace placeholders. Read the drafted .gitlab-ci.yml, replace my-app, example.com and other placeholder values, and check any only: rules against your setup.
  6. 6Test before production. Commit the file on a non-production branch and watch the pipeline run inside GitLab before you use it for production deploys.

What the skill makes the agent do

An agent skill is a folder with a SKILL.md file that tells the agent when to use it and what reference material to follow. This skill covers GitLab CI/CD. When you ask for help with a GitLab pipeline, the agent can draft a .gitlab-ci.yml file based on its patterns.

The patterns cover multi-stage pipelines, caching, Docker image builds and pushes, security scanning, Kubernetes deployments with a manual production gate, Terraform validate, plan and apply flows, and dynamic child pipelines.

  • Multi-stage pipelines with build, test and deploy stages
  • Caching for dependencies such as node_modules
  • Docker build and push to the GitLab container registry
  • Security scanning templates and a Trivy image scan example
  • Kubernetes rollouts with a manual production job
  • Terraform pipelines with a manual apply step

How to install the skill

Download the ZIP file for the skill from this page. Unzip it into one of two locations. For a personal install, use ~/.claude/skills/. For a single project, put it in that project's .claude/skills/ folder.

If you use the Claude apps instead, open Settings, go to Capabilities, then Skills, and upload the skill there.

  • Personal install: unzip into ~/.claude/skills/
  • Project install: unzip into the project's .claude/skills/ folder
  • Claude apps: Settings → Capabilities → Skills, then upload

How to ask for pipeline help so the skill triggers

The skill is described as useful when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment. Name GitLab and say what you want the pipeline to do. Include the stages, the deploy target and any gates you need.

Mention your project's details where you can, such as the language, whether you build a Docker image and whether you deploy to Kubernetes. Expect placeholder names in the output, which you will need to replace.

  • Set up a GitLab CI pipeline that builds a Node app, runs lint and tests, and caches node_modules
  • Add a manual approval step before production deploys to Kubernetes
  • Write a .gitlab-ci.yml that builds and pushes a Docker image on main and tags

Example: a manual production deploy gate

The skill's Kubernetes examples use a job with when: manual so that production only deploys after someone starts it in GitLab. The job below is adapted from the pattern in the skill. It uses only: rules, which is older syntax, so check it against your GitLab version before use.

Replace my-app, the namespace and the environment URL with your own values.

deploy:production: 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

What you need before you start

The skill does not create infrastructure. It assumes the pieces below already exist. Missing any of them will make the examples fail when GitLab runs them.

  • A GitLab project with GitLab CI/CD enabled and runners available
  • Docker-capable runners for the Docker build examples
  • Kubernetes cluster access and CI variables KUBE_TOKEN and KUBE_CA_CERT_FILE for the deploy examples
  • A Terraform backend and cloud credentials for the Terraform example

Safety notes: scripts and network

This skill does not run scripts and does not use the network. It does not execute anything on its own. The pipeline runs later, inside GitLab, when you commit or trigger it.

Store tokens and credentials in protected, masked CI/CD variables rather than in the YAML file. The Kubernetes example expects KUBE_TOKEN to be protected and masked, and says to protect the develop and main branches so the deploy jobs can read it. Review every production job before you merge.

  • No scripts are bundled and none are run by the skill
  • No network use by the skill itself
  • Keep secrets in CI/CD variables, not in committed files
  • Review terraform apply and production deploy jobs before they run

Tips for better results

Ask the agent to adapt the pattern to your project rather than paste it in. Then read the output line by line. The examples use placeholder names such as my-app and example.com, and some use older syntax such as only:. Check each job against your own setup and the current GitLab documentation.

Pin image tags as the skill recommends, such as node:20 instead of node:latest. The skill also suggests using environments for deployment tracking, merge request pipelines and pipeline schedules for recurring jobs.

  • Replace all placeholder names and URLs
  • Check any only: rules against your GitLab version
  • Use specific image tags, not latest
  • Test on a non-production branch first

What people use it for

  • Drafting a first .gitlab-ci.yml for a Node project with build, test and cache stages
  • Adding a manual approval job before production Kubernetes deploys
  • Building and pushing a Docker image to the GitLab registry on main and tags
  • Starting a Terraform validate, plan and apply pipeline with a manual apply step

Frequently asked questions

How do I install the GitLab CI Patterns agent skill?

Download the skill ZIP from this page and unzip it into ~/.claude/skills/ for personal use, or into a project's .claude/skills/ folder. In the Claude apps, you can instead upload it under Settings, Capabilities, Skills.

Is the gitlab-ci-patterns skill free to use?

The skill is published under the MIT licence. This page does not state pricing for the wider Claude apps, so check those services directly for any costs.

What do I need to use the GitLab CI patterns?

You need a GitLab project with GitLab CI/CD and runners available. Docker examples need Docker-capable runners, deploy examples need Kubernetes access and the KUBE_TOKEN and KUBE_CA_CERT_FILE variables, and the Terraform example needs a backend and cloud credentials.

Does the skill run my pipeline or scripts?

No. The skill does not run scripts and does not use the network. It drafts YAML, and the pipeline only runs later inside GitLab.

Can I use this skill with GitHub Actions or Jenkins?

Not for those tools. The output is GitLab-specific YAML. The skill's related skills list points to github-actions-templates for GitHub Actions.

Can I copy the examples straight into production?

No. The examples use placeholder names such as my-app and example.com and some older syntax such as only:. Check them against your own setup and test on a non-production branch first.

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Our verdict

This skill suits developers and DevOps engineers who already use GitLab and want a quick, well-structured starting point for CI/CD, including a manual production gate. It is a poor fit for GitHub Actions or Jenkins users, or for anyone without a GitLab project, and its examples need checking before they go near production.

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