Use when working with KubeSphere DevOps extension, CI/CD pipelines, Jenkins integration, or pipeline troubleshooting
npx skills add https://github.com/kubesphere/kubesphere --skill kubesphere-devops-overview
KubeSphere DevOps provides CI/CD capabilities through Jenkins integration, supporting both graphical pipeline editing and Jenkinsfile-based pipelines. It enables automated builds, testing, and deployments across multi-cluster environments with ArgoCD integration for GitOps continuous deployment.
KubeSphere DevOps maps resources across three layers:
KubeSphere Kubernetes Jenkins
─────────────────────────────────────────────────────────────
Workspace Workspace CR (authorization)
└── DevOpsProject ├── DevOpsProject CR └── Folder
(Namespace) └── Namespace (with label)
└── Pipeline ├── Pipeline CR └── WorkflowJob
└── Run ├── PipelineRun CR └── Build #N
Key Concept: A "DevOps Project" in KubeSphere is fundamentally a Kubernetes namespace with the devops.kubesphere.io/managed=true label. The DevOpsProject CR exists as a wrapper resource, but when querying for accessible DevOps projects, you interact with namespaces, not the DevOpsProject CRs directly.
For tenants: Use the /kapis/devops.kubesphere.io/v1alpha3/workspaces/{workspace}/namespaces endpoint to list accessible DevOps projects (returns namespace resources). The /apis/devops.kubesphere.io/v1alpha3/devopsprojects endpoint requires cluster-scoped permissions and returns 403 for tenants.
DevOps projects have two forms of names:
| Name Type | Example | Source | Usage |
|-----------|---------|--------|-------|
| Shortname | devopstest | .metadata.generateName in DevOpsProject CR | Display/user-friendly name |
| Fullname | devopstestc2nj7 | .metadata.name in DevOpsProject CR and Namespace | Actual resource identifier |
Important:
devopstestc2nj7)devopstest, KubeSphere generates a unique fullname by appending random charactersDevOpsProjects belong to a Workspace via label:
apiVersion: devops.kubesphere.io/v1alpha3
kind: DevOpsProject
metadata:
name: my-project
labels:
kubesphere.io/workspace: demo # Associates with Workspace 'demo'
To create and associate:
# 1. Create Workspace
kubectl apply -f - <<EOF
apiVersion: tenant.kubesphere.io/v1beta1
kind: Workspace
metadata:
name: demo
EOF
# 2. Create DevOpsProject with label
kubectl apply -f - <<EOF
apiVersion: devops.kubesphere.io/v1alpha3
kind: DevOpsProject
metadata:
name: my-project
labels:
kubesphere.io/workspace: demo
EOF
┌──────────────────────────────────────────────────────────────┐
│ DevOps Project │
│ (Namespace with devops.kubesphere.io/managed=true label) │
└──────────────────────┬───────────────────────────────────────┘
│
┌──────────────┼──────────────┐
│ │ │
┌───────▼──────┐ ┌─────▼─────┐ ┌──────▼──────┐
│ Pipelines │ │Credentials│ │ Webhooks │
│ │ │ │ │ │
│ - Graphical │ │ - SSH │ │ - GitHub │
│ - Jenkinsfile│ │ - Basic │ │ - GitLab │
│ - Multi-branch│ │ - Token │ │ - Generic │
└──────────────┘ └───────────┘ └─────────────┘
Minimal Installation (Default Config) - RECOMMENDED:
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
name: devops
namespace: kubesphere-system
spec:
extension:
name: devops
version: 1.2.4
enabled: true
upgradeStrategy: Manual # Required for production
# Note: spec.config is omitted to use extension default values
When to use minimal installation:
Custom Configuration (Only When Needed):
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
name: devops
namespace: kubesphere-system
spec:
extension:
name: devops
version: 1.2.4
enabled: true
upgradeStrategy: Manual # Required for production
# config: leave empty to use default values
Custom Configuration (Override Defaults):
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
name: devops
namespace: kubesphere-system
spec:
extension:
name: devops
version: 1.2.4
enabled: true
upgradeStrategy: Manual # Required for production
config: |
# Overrides values from DevOps chart's values.yaml
agent:
jenkins:
Master:
NodeSelector: {}
resources:
requests:
cpu: "500m"
memory: "4Gi"
limits:
cpu: "2000m"
memory: "8Gi"
Agent:
Image: "jenkins/inbound-agent"
Tag: "3309.v27b_9314fd1a_4-1-jdk21"
Privileged: false
Important:
upgradeStrategy: Manual for productionconfig is optional - omit or leave empty to use extension defaultsvalues.yaml settingsTo install DevOps agent on member clusters, add clusterScheduling:
apiVersion: kubesphere.io/v1alpha1
kind: InstallPlan
metadata:
name: devops
namespace: kubesphere-system
spec:
extension:
name: devops
version: 1.2.4
enabled: true
upgradeStrategy: Manual
config: |
# Base config for all clusters
agent:
jenkins:
Master:
resources:
requests:
cpu: "500m"
memory: "4Gi"
clusterScheduling:
placement:
clusters:
- host # Install on host cluster
- member1 # Install on member1
- member2 # Install on member2
# Optional: per-cluster overrides
overrides:
member1: |
agent:
jenkins:
Master:
resources:
limits:
memory: "8Gi" # Larger master for member1
member2: |
agent:
jenkins:
Agent:
NodeSelector:
zone: west
Key Points:
clusterScheduling.placement.clusters: List clusters where DevOps agent runsclusterScheduling.overrides: Cluster-specific config overridesclusterScheduling, DevOps only runs on the host clusterhelm upgrade --install devops kse-extensions/devops \
-n kubesphere-devops-system \
--create-namespace
Verify the DevOps installation:
# Check DevOps pods
kubectl get pods -n kubesphere-devops-system
# Check InstallPlan status
kubectl get installplan devops -n kubesphere-system
# For multi-cluster: check agent status on each cluster
kubectl get installplan devops -n kubesphere-system -o jsonpath='{.status.clusterSchedulingStatuses}'
| Component | Purpose | Namespace |
|-----------|---------|-----------|
| devops-jenkins | Jenkins master | kubesphere-devops-system |
| devops-apiserver | DevOps API service | kubesphere-devops-system |
| devops-controller | Resource controllers | kubesphere-devops-system |
| devops-argocd-* | ArgoCD (GitOps) | argocd |
| Jenkins Agent | Pipeline executors | Dynamic (per pipeline) |
|-----------|---------|-----------|
| devops-jenkins | Jenkins master | kubesphere-devops-system |
| devops-apiserver | DevOps API service | kubesphere-devops-system |
| devops-controller | Resource controllers | kubesphere-devops-system |
| Jenkins Agent | Pipeline executors | Dynamic (per pipeline) |
KubeSphere DevOps integrates with Jenkins for CI/CD execution. The secret devops-jenkins contains the admin token for direct Jenkins access:
# Get Jenkins admin token
TOKEN=$(kubectl -n kubesphere-devops-system get secret devops-jenkins -o jsonpath='{.data.jenkins-admin-token}' | base64 -d)
# Access Jenkins API
kubectl run curl-jenkins --rm -i --restart=Never --image=curlimages/curl \
-- "http://admin:${TOKEN}@devops-jenkins.kubesphere-devops-system:80/api/json"
Jenkins NodePort:
kubectl get svc devops-jenkins -n kubesphere-devops-system
# Default: 30180
Access Jenkins Console:
http://<node-ip>:30180adminKubeSphere DevOps includes ArgoCD v2.11.7 for GitOps continuous deployment:
ArgoCD Components:
| Component | Purpose |
|-----------|---------|
| application-controller | Manages Application state |
| applicationset-controller | Manages ApplicationSet resources |
| dex-server | SSO authentication |
| notifications-controller | Event notifications |
| repo-server | Repository operations |
| argocd-server | API/UI server |
| redis | Cache layer |
Access ArgoCD:
# Get ArgoCD server URL
kubectl get svc devops-agent-argocd-server -n argocd
# Get admin password
kubectl -n argocd get secret argocd-initial-admin-secret -o jsonpath='{.data.password}' | base64 -d
Key Features:
| Resource | API Version | Purpose |
|----------|-------------|---------|
| Pipeline | devops.kubesphere.io/v1alpha3 | CI/CD pipeline definition |
| DevOpsProject | devops.kubesphere.io/v1alpha3 | DevOps project (namespace wrapper) |
| Credential | v1/Secret | Repository and deployment credentials |
# List DevOps projects
kubectl get devopsprojects
# List pipelines in a project
kubectl get pipelines -n <devops-project-namespace>
# Get pipeline runs
kubectl get pipelineruns -n <devops-project-namespace>
# Check Jenkins status
kubectl -n kubesphere-devops-system get pods -l app=devops-jenkins
# View Jenkins logs
kubectl -n kubesphere-devops-system logs -l app=devops-jenkins
# Get Jenkins admin password
kubectl -n kubesphere-devops-system get secret devops-jenkins -o jsonpath='{.data.jenkins-admin-password}' | base64 -d
| Type | Description | Use Case |
|------|-------------|----------|
| Graphical | Visual pipeline editor | Simple pipelines, no code |
| Jenkinsfile (SCM) | Pipeline defined in repository | Version-controlled pipelines |
| Jenkinsfile (Inline) | Pipeline defined in KubeSphere | Quick testing |
| Multi-branch | Auto-discovers branches | GitFlow, feature branches |
| Mistake | Fix |
|---------|-----|
| Pipeline fails with "No agent" | Check Jenkins agent configuration |
| Cannot access Git repository | Verify credentials and webhook setup |
| kubeconfig credentials fail | Use string type instead of kubeconfigContent (v1.2+) |
| Jenkins out of memory | Increase Jenkins master resources |
| Pipeline hangs | Check agent pod status and resource limits |
| DevOps | Jenkins | Notes |
|--------|---------|-------|
| v1.2.x | 2.504.1 LTS | kubernetes-cd plugin removed |
| v1.1.x | 2.346.3 LTS | Legacy kubeconfigContent supported |
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 kubesphere/kubesphere-devops-overview 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.