mcpbeat Sign in

Gitlab CI Agent Skill

Configure GitLab CI/CD pipelines and runners for automated building, testing, and deployment. Create .gitlab-ci.yml configurations, manage runners, and implement DevOps workflows. Use when working with GitLab repositories or self-hosted GitLab instances.

2k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
511
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill gitlab-ci

What comes with it

1 685 bytes besides the instruction
references/pipeline-patterns.md

The instruction itself

29 sections, as written by the author

GitLab CI/CD

Automate your software delivery pipeline with GitLab's integrated CI/CD system.

When to Use This Skill

Use this skill when:

  • Setting up CI/CD pipelines in GitLab
  • Configuring GitLab runners (shared or self-hosted)
  • Creating multi-stage deployment pipelines
  • Implementing GitLab Auto DevOps
  • Managing CI/CD variables and secrets

Prerequisites

  • GitLab repository (gitlab.com or self-hosted)
  • Basic understanding of YAML
  • For self-hosted runners: Linux server or Kubernetes cluster

Pipeline Configuration

Create .gitlab-ci.yml in repository root:

stages:
  - build
  - test
  - deploy

variables:
  NODE_VERSION: "20"

build:
  stage: build
  image: node:${NODE_VERSION}
  script:
    - npm ci
    - npm run build
  artifacts:
    paths:
      - dist/
    expire_in: 1 hour

test:
  stage: test
  image: node:${NODE_VERSION}
  script:
    - npm ci
    - npm test
  coverage: '/Coverage: \d+\.\d+%/'

deploy:
  stage: deploy
  script:
    - ./deploy.sh
  environment:
    name: production
    url: https://example.com
  only:
    - main

Job Configuration

Rules-Based Execution

deploy:
  script: ./deploy.sh
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
      when: manual
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"
      when: never
    - when: on_success

Parallel Jobs

test:
  stage: test
  parallel: 3
  script:
    - npm test -- --shard=$CI_NODE_INDEX/$CI_NODE_TOTAL

Matrix Builds

test:
  stage: test
  parallel:
    matrix:
      - NODE_VERSION: ["18", "20", "22"]
        OS: ["alpine", "slim"]
  image: node:${NODE_VERSION}-${OS}
  script:
    - npm test

Caching

cache:
  key:
    files:
      - package-lock.json
  paths:
    - node_modules/
  policy: pull-push

build:
  cache:
    key: build-cache
    paths:
      - .cache/
    policy: pull

Artifacts

build:
  artifacts:
    paths:
      - dist/
      - coverage/
    reports:
      junit: junit.xml
      coverage_report:
        coverage_format: cobertura
        path: coverage/cobertura.xml
    expire_in: 1 week
    when: always

Environments and Deployments

deploy_staging:
  stage: deploy
  script:
    - deploy --env staging
  environment:
    name: staging
    url: https://staging.example.com
    on_stop: stop_staging

stop_staging:
  stage: deploy
  script:
    - undeploy --env staging
  environment:
    name: staging
    action: stop
  when: manual

Docker Builds

build_image:
  stage: build
  image: docker:24
  services:
    - docker:24-dind
  variables:
    DOCKER_TLS_CERTDIR: "/certs"
  script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
    - docker build -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA .
    - docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA

GitLab Runners

Install Runner

# Download and install
curl -L https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh | sudo bash
sudo apt install gitlab-runner

# Register runner
sudo gitlab-runner register \
  --url https://gitlab.com/ \
  --registration-token TOKEN \
  --executor docker \
  --docker-image alpine:latest

Runner Configuration

# /etc/gitlab-runner/config.toml
[[runners]]
  name = "docker-runner"
  url = "https://gitlab.com/"
  token = "TOKEN"
  executor = "docker"
  [runners.docker]
    image = "alpine:latest"
    privileged = true
    volumes = ["/cache", "/var/run/docker.sock:/var/run/docker.sock"]

Runner Tags

build:
  tags:
    - docker
    - linux
  script:
    - make build

CI/CD Variables

Protected Variables

Define in Settings > CI/CD > Variables:

  • AWS_ACCESS_KEY_ID (protected, masked)
  • AWS_SECRET_ACCESS_KEY (protected, masked)

Using Variables

deploy:
  script:
    - aws s3 sync dist/ s3://$S3_BUCKET
  variables:
    AWS_DEFAULT_REGION: us-east-1

Include and Extend

Include Templates

include:
  - template: Security/SAST.gitlab-ci.yml
  - project: 'group/shared-ci'
    file: '/templates/deploy.yml'
  - local: '/ci/jobs.yml'

Extend Jobs

.base_job:
  image: node:20
  before_script:
    - npm ci

build:
  extends: .base_job
  script:
    - npm run build

test:
  extends: .base_job
  script:
    - npm test

Multi-Project Pipelines

trigger_downstream:
  stage: deploy
  trigger:
    project: group/downstream-project
    branch: main
    strategy: depend

Common Issues

Issue: Pipeline Stuck

Problem: Jobs stay pending

Solution: Check runner availability and tags matching

Issue: Docker-in-Docker Fails

Problem: Cannot connect to Docker daemon

Solution: Use docker:dind service with proper TLS configuration

Issue: Cache Not Working

Problem: Cache misses between jobs

Solution: Verify cache key and ensure runners share distributed cache

Best Practices

  • Use rules instead of only/except for complex conditions
  • Leverage GitLab's built-in security scanning templates
  • Use job dependencies to optimize pipeline speed
  • Implement review apps for merge requests
  • Cache dependencies aggressively
  • Use artifacts for passing data between stages
  • github-actions - GitHub CI/CD alternative
  • argocd-gitops - GitOps deployments
  • container-registries - Registry management

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

13k tokens
Capacity
by microsoft
vendor ×3

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

6k tokens scripts
Customize
by microsoft
vendor ×3

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

8k tokens
Deploy Model
by microsoft
vendor ×3

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

26k tokens scripts
Preset
by microsoft
vendor ×3

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take bagelhole/gitlab-ci from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.