Create and manage Jenkins CI/CD pipelines, configure agents, manage plugins, and automate builds. Use when working with Jenkins servers, creating Jenkinsfiles, or setting up build automation for enterprise environments.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill jenkins
Build, test, and deploy applications using Jenkins, the leading open-source automation server.
Use this skill when:
Create Jenkinsfile in repository root:
pipeline {
agent any
environment {
DOCKER_REGISTRY = 'registry.example.com'
APP_NAME = 'myapp'
}
stages {
stage('Build') {
steps {
sh 'npm ci'
sh 'npm run build'
}
}
stage('Test') {
steps {
sh 'npm test'
}
post {
always {
junit 'test-results/*.xml'
}
}
}
stage('Deploy') {
when {
branch 'main'
}
steps {
sh './deploy.sh'
}
}
}
post {
failure {
mail to: '[email protected]',
subject: "Pipeline Failed: ${env.JOB_NAME}",
body: "Check console output at ${env.BUILD_URL}"
}
}
}
pipeline {
agent {
docker {
image 'node:20'
args '-v /tmp:/tmp'
}
}
stages {
stage('Build') {
steps {
sh 'npm ci && npm run build'
}
}
}
}
pipeline {
agent {
kubernetes {
yaml '''
apiVersion: v1
kind: Pod
spec:
containers:
- name: node
image: node:20
command:
- sleep
args:
- infinity
- name: docker
image: docker:24-dind
securityContext:
privileged: true
'''
}
}
stages {
stage('Build') {
steps {
container('node') {
sh 'npm ci && npm run build'
}
}
}
}
}
pipeline {
agent { label 'linux && docker' }
stages {
stage('Build') {
steps {
sh 'make build'
}
}
}
}
pipeline {
agent any
parameters {
string(name: 'BRANCH', defaultValue: 'main', description: 'Branch to build')
choice(name: 'ENVIRONMENT', choices: ['dev', 'staging', 'prod'], description: 'Target environment')
booleanParam(name: 'RUN_TESTS', defaultValue: true, description: 'Run tests?')
}
stages {
stage('Deploy') {
when {
expression { params.ENVIRONMENT == 'prod' }
}
steps {
sh "deploy.sh ${params.ENVIRONMENT}"
}
}
}
}
pipeline {
agent any
environment {
AWS_CREDS = credentials('aws-credentials')
DOCKER_CREDS = credentials('docker-hub')
}
stages {
stage('Deploy') {
steps {
withCredentials([
usernamePassword(
credentialsId: 'github-token',
usernameVariable: 'GH_USER',
passwordVariable: 'GH_TOKEN'
)
]) {
sh 'git push https://${GH_USER}:${GH_TOKEN}@github.com/repo.git'
}
}
}
}
}
pipeline {
agent any
stages {
stage('Tests') {
parallel {
stage('Unit Tests') {
steps {
sh 'npm run test:unit'
}
}
stage('Integration Tests') {
steps {
sh 'npm run test:integration'
}
}
stage('E2E Tests') {
steps {
sh 'npm run test:e2e'
}
}
}
}
}
}
vars/
├── buildApp.groovy
├── deployApp.groovy
└── notifySlack.groovy
src/
└── com/example/
└── Pipeline.groovy
resources/
└── templates/
└── deployment.yaml
// vars/buildApp.groovy
def call(Map config = [:]) {
def nodeVersion = config.nodeVersion ?: '20'
docker.image("node:${nodeVersion}").inside {
sh 'npm ci'
sh 'npm run build'
}
}
@Library('my-shared-library') _
pipeline {
agent any
stages {
stage('Build') {
steps {
buildApp(nodeVersion: '20')
}
}
stage('Deploy') {
steps {
deployApp(environment: 'staging')
}
}
}
post {
failure {
notifySlack(channel: '#builds', status: 'FAILED')
}
}
}
node('linux') {
try {
stage('Checkout') {
checkout scm
}
stage('Build') {
docker.image('node:20').inside {
sh 'npm ci'
sh 'npm run build'
}
}
stage('Test') {
sh 'npm test'
}
if (env.BRANCH_NAME == 'main') {
stage('Deploy') {
sh './deploy.sh'
}
}
} catch (e) {
currentBuild.result = 'FAILURE'
throw e
} finally {
cleanWs()
}
}
// Install via Jenkins CLI or init.groovy.d
def plugins = [
'workflow-aggregator', // Pipeline
'git', // Git integration
'docker-workflow', // Docker Pipeline
'kubernetes', // Kubernetes agent
'credentials-binding', // Credentials
'blueocean', // Blue Ocean UI
'job-dsl', // Job DSL
'configuration-as-code' // JCasC
]
# jenkins.yaml
jenkins:
systemMessage: "Jenkins configured via JCasC"
numExecutors: 2
securityRealm:
local:
users:
- id: admin
password: ${ADMIN_PASSWORD}
authorizationStrategy:
globalMatrix:
permissions:
- "Overall/Administer:admin"
- "Overall/Read:authenticated"
credentials:
system:
domainCredentials:
- credentials:
- usernamePassword:
id: "docker-hub"
username: "user"
password: ${DOCKER_PASSWORD}
// Automatically discovers branches with Jenkinsfile
// Configure in Jenkins UI: New Item > Multibranch Pipeline
// Branch-specific behavior in Jenkinsfile
pipeline {
agent any
stages {
stage('Deploy') {
when {
anyOf {
branch 'main'
branch 'release/*'
}
}
steps {
sh './deploy.sh'
}
}
}
}
Problem: Jenkinsfile fails to parse
Solution: Use Pipeline Syntax generator in Jenkins UI, validate with jenkins-cli
Problem: Build agents disconnect
Solution: Check agent logs, verify network connectivity, increase timeout settings
Problem: Jenkins crashes or builds fail with OOM
Solution: Increase heap size in JAVA_OPTS, clean up old builds
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/jenkins from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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same name cannot sit side by side — one of them will be ignored.