Set up Azure Pipelines for CI/CD, configure build and release pipelines, manage Azure DevOps projects, and integrate with Azure services. Use when working with Azure DevOps Services or Server for enterprise DevOps workflows.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill azure-devops
Build, test, and deploy applications using Azure Pipelines with YAML or classic editor.
Use this skill when:
Create azure-pipelines.yml in repository root:
trigger:
branches:
include:
- main
- develop
paths:
include:
- src/*
pool:
vmImage: 'ubuntu-latest'
variables:
buildConfiguration: 'Release'
nodeVersion: '20.x'
stages:
- stage: Build
jobs:
- job: BuildJob
steps:
- task: NodeTool@0
inputs:
versionSpec: $(nodeVersion)
- script: |
npm ci
npm run build
displayName: 'Build application'
- publish: $(Build.ArtifactStagingDirectory)
artifact: drop
- stage: Deploy
dependsOn: Build
condition: and(succeeded(), eq(variables['Build.SourceBranch'], 'refs/heads/main'))
jobs:
- deployment: DeployWeb
environment: 'production'
strategy:
runOnce:
deploy:
steps:
- script: echo Deploying to production
trigger:
branches:
include:
- main
- release/*
exclude:
- feature/*
tags:
include:
- v*
pr:
branches:
include:
- main
paths:
include:
- src/*
exclude:
- docs/*
schedules:
- cron: '0 2 * * *'
displayName: 'Nightly build'
branches:
include:
- main
always: true
stages:
- stage: Test
jobs:
- job: UnitTests
pool:
vmImage: 'ubuntu-latest'
steps:
- script: npm run test:unit
- job: IntegrationTests
pool:
vmImage: 'ubuntu-latest'
steps:
- script: npm run test:integration
jobs:
- job: Build
strategy:
matrix:
linux:
vmImage: 'ubuntu-latest'
windows:
vmImage: 'windows-latest'
mac:
vmImage: 'macos-latest'
pool:
vmImage: $(vmImage)
steps:
- script: npm test
stages:
- stage: Build
jobs:
- job: A
steps:
- script: echo Job A
- job: B
dependsOn: A
steps:
- script: echo Job B
variables:
- group: 'production-secrets'
- name: buildConfiguration
value: 'Release'
parameters:
- name: environment
displayName: 'Environment'
type: string
default: 'dev'
values:
- dev
- staging
- prod
stages:
- stage: Deploy
variables:
env: ${{ parameters.environment }}
jobs:
- job: Deploy
steps:
- script: echo "Deploying to $(env)"
variables:
- name: mySecret
value: $(SECRET_FROM_PIPELINE) # Set in pipeline settings
steps:
- script: |
echo "Using secret"
./deploy.sh
env:
API_KEY: $(mySecret)
# templates/build-job.yml
parameters:
- name: nodeVersion
default: '20'
jobs:
- job: Build
steps:
- task: NodeTool@0
inputs:
versionSpec: ${{ parameters.nodeVersion }}
- script: npm ci && npm run build
# azure-pipelines.yml
stages:
- stage: Build
jobs:
- template: templates/build-job.yml
parameters:
nodeVersion: '20'
# templates/deploy-stage.yml
parameters:
- name: environment
type: string
- name: serviceConnection
type: string
stages:
- stage: Deploy_${{ parameters.environment }}
jobs:
- deployment: Deploy
environment: ${{ parameters.environment }}
strategy:
runOnce:
deploy:
steps:
- task: AzureWebApp@1
inputs:
azureSubscription: ${{ parameters.serviceConnection }}
appName: 'myapp-${{ parameters.environment }}'
stages:
- stage: DeployStaging
jobs:
- deployment: DeployWeb
environment: 'staging'
strategy:
runOnce:
deploy:
steps:
- download: current
artifact: drop
- script: ./deploy.sh staging
Configure in Azure DevOps UI:
jobs:
- deployment: Deploy
environment: 'production'
strategy:
rolling:
maxParallel: 2
deploy:
steps:
- script: ./deploy.sh
- task: AzureWebApp@1
inputs:
azureSubscription: 'my-azure-connection'
appType: 'webAppLinux'
appName: 'my-web-app'
package: '$(Pipeline.Workspace)/drop/*.zip'
- task: AzureContainerApps@1
inputs:
azureSubscription: 'my-azure-connection'
containerAppName: 'my-container-app'
resourceGroup: 'my-rg'
imageToDeploy: 'myregistry.azurecr.io/myapp:$(Build.BuildId)'
- task: KubernetesManifest@0
inputs:
action: 'deploy'
kubernetesServiceConnection: 'my-aks-connection'
namespace: 'default'
manifests: |
$(Pipeline.Workspace)/manifests/deployment.yml
$(Pipeline.Workspace)/manifests/service.yml
containers: |
myregistry.azurecr.io/myapp:$(Build.BuildId)
- task: Docker@2
inputs:
containerRegistry: 'my-acr-connection'
repository: 'myapp'
command: 'buildAndPush'
Dockerfile: '**/Dockerfile'
tags: |
$(Build.BuildId)
latest
# Download agent
mkdir myagent && cd myagent
curl -o vsts-agent.tar.gz https://vstsagentpackage.azureedge.net/agent/3.227.2/vsts-agent-linux-x64-3.227.2.tar.gz
tar zxvf vsts-agent.tar.gz
# Configure
./config.sh --url https://dev.azure.com/myorg --auth pat --token PAT_TOKEN --pool default
# Run as service
sudo ./svc.sh install
sudo ./svc.sh start
pool:
name: 'my-self-hosted-pool'
demands:
- docker
- Agent.OS -equals Linux
Problem: Cannot authenticate to Azure
Solution: Verify service principal permissions, check connection in project settings
Problem: Download artifact fails
Solution: Ensure publish task ran successfully, check artifact name matches
Problem: Deployment to environment fails
Solution: Create environment in Pipelines > Environments first
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.
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/azure-devops 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.