Complete AI TrainingYourJobSkills for your job

Skills / workflow

gitlab-ci-patterns

Comprehensive GitLab CI/CD pipeline patterns for automated testing, building, and deployment.

IT & Software DevelopmentProject & Program ManagementOperations & Supply ChainAI & Automation

GitLab CI Patterns

Comprehensive GitLab CI/CD pipeline patterns for automated testing, building, and deployment.

Do not use this skill when

  • The task is unrelated to gitlab ci patterns
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Purpose

Create efficient GitLab CI pipelines with proper stage organization, caching, and deployment strategies.

Use this skill when

  • 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: cover

Subscribers only

The full skill, its 1 bundled files and every download is included with every paid Complete AI plan.

Details

Sourcecommunity
License
Risk labelcritical ("critical" means the skill may run commands or touch files — read before use)
FilesSKILL.md
Added2026-02-27

Related skills

acceptance-orchestrator

Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

address-github-comments

Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.

ai-loop

Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.

airflow-dag-patterns

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

antigravity-workflows

Use when asked to ship a SaaS MVP, audit application security, build an AI agent, run browser QA, or design a domain model with multiple skills and verified checkpoints.

ask-questions-if-underspecified

Clarify requirements before implementing. Use when serious doubts arise.