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/gitlab-cicd-pipeline

Design and implement GitLab CI/CD pipelines with stages, jobs, artifacts, and caching. Configure runners, Docker integration, and deployment strategies.

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useful-ai-prompts
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Install
$ npx -y skills add aj-geddes/useful-ai-prompts --skill gitlab-cicd-pipeline --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/gitlab-cicd-pipeline

Context preview

The summary Claude sees to decide when to auto-load this skill.

Design and implement GitLab CI/CD pipelines with stages, jobs, artifacts, and caching. Configure runners, Docker integration, and deployment strategies.

SKILL.md

gitlab-cicd-pipeline.SKILL.md
name: gitlab-cicd-pipeline
description: >
  Design and implement GitLab CI/CD pipelines with stages, jobs, artifacts, and
  caching. Configure runners, Docker integration, and deployment strategies.

GitLab CI/CD Pipeline

Table of Contents

  • [Overview](#overview)
  • [When to Use](#when-to-use)
  • [Quick Start](#quick-start)
  • [Reference Guides](#reference-guides)
  • [Best Practices](#best-practices)

Overview

Create comprehensive GitLab CI/CD pipelines that automate building, testing, and deployment using GitLab Runner infrastructure and container execution.

When to Use

  • GitLab repository CI/CD setup
  • Multi-stage build pipelines
  • Docker registry integration
  • Kubernetes deployment
  • Review app deployment
  • Cache optimization
  • Dependency management

Quick Start

Minimal working example:

# .gitlab-ci.yml
image: node:18-alpine

variables:
  DOCKER_DRIVER: overlay2
  FF_USE_FASTZIP: "true"

stages:
  - lint
  - test
  - build
  - security
  - deploy-review
  - deploy-prod

cache:
  key: ${CI_COMMIT_REF_SLUG}
  paths:
    - node_modules/
    - .npm/

lint:
  stage: lint
  script:
    - npm install
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the `references/` directory:

| Guide | Contents | |---|---| | [Complete Pipeline Configuration](references/complete-pipeline-configuration.md) | Complete Pipeline Configuration | | [GitLab Runner Configuration](references/gitlab-runner-configuration.md) | GitLab Runner Configuration | | [Docker Layer Caching Optimization](references/docker-layer-caching-optimization.md) | Docker Layer Caching Optimization | | [Multi-Project Pipeline](references/multi-project-pipeline.md) | Multi-Project Pipeline | | [Kubernetes Deployment](references/kubernetes-deployment.md) | Kubernetes Deployment, Performance Testing Stage, Release Pipeline with Semantic Versioning |

Best Practices

✅ DO

  • Use stages to organize pipeline flow
  • Implement caching for dependencies
  • Use artifacts for test reports
  • Set appropriate cache keys
  • Implement conditional execution with `only` and `except`
  • Use `needs:` for job dependencies
  • Clean up artifacts with `expire_in`
  • Use Docker for consistent environments
  • Implement security scanning stages
  • Set resource limits for jobs
  • Use merge request pipelines

❌ DON'T

  • Run tests serially when parallelizable
  • Cache everything unnecessarily
  • Leave large artifacts indefinitely
  • Store secrets in configuration files
  • Run privileged Docker without necessity
  • Skip security scanning
  • Ignore pipeline failures
  • Use `only: [main]` without proper controls
Read more
Ships withuseful-ai-prompts

488 production-ready AI prompts, all following a standardized template with validated quality gates. Transform ChatGPT, Claude, and other AI assistants into expert consultants.

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