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.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill gitlab-ci
Automate your software delivery pipeline with GitLab's integrated CI/CD system.
Use this skill when:
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
deploy:
script: ./deploy.sh
rules:
- if: $CI_COMMIT_BRANCH == "main"
when: manual
- if: $CI_PIPELINE_SOURCE == "merge_request_event"
when: never
- when: on_success
test:
stage: test
parallel: 3
script:
- npm test -- --shard=$CI_NODE_INDEX/$CI_NODE_TOTAL
test:
stage: test
parallel:
matrix:
- NODE_VERSION: ["18", "20", "22"]
OS: ["alpine", "slim"]
image: node:${NODE_VERSION}-${OS}
script:
- npm test
cache:
key:
files:
- package-lock.json
paths:
- node_modules/
policy: pull-push
build:
cache:
key: build-cache
paths:
- .cache/
policy: pull
build:
artifacts:
paths:
- dist/
- coverage/
reports:
junit: junit.xml
coverage_report:
coverage_format: cobertura
path: coverage/cobertura.xml
expire_in: 1 week
when: always
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
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
# 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
# /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"]
build:
tags:
- docker
- linux
script:
- make build
Define in Settings > CI/CD > Variables:
AWS_ACCESS_KEY_ID (protected, masked)AWS_SECRET_ACCESS_KEY (protected, masked)deploy:
script:
- aws s3 sync dist/ s3://$S3_BUCKET
variables:
AWS_DEFAULT_REGION: us-east-1
include:
- template: Security/SAST.gitlab-ci.yml
- project: 'group/shared-ci'
file: '/templates/deploy.yml'
- local: '/ci/jobs.yml'
.base_job:
image: node:20
before_script:
- npm ci
build:
extends: .base_job
script:
- npm run build
test:
extends: .base_job
script:
- npm test
trigger_downstream:
stage: deploy
trigger:
project: group/downstream-project
branch: main
strategy: depend
Problem: Jobs stay pending
Solution: Check runner availability and tags matching
Problem: Cannot connect to Docker daemon
Solution: Use docker:dind service with proper TLS configuration
Problem: Cache misses between jobs
Solution: Verify cache key and ensure runners share distributed cache
rules instead of only/except for complex conditionsAssess 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.
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.
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).
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).
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).
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.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
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.
Take bagelhole/gitlab-ci from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.