NodeGroup operation Skill for the edgewize nodegroup project. Use this whenever the user wants to query, create, update, delete, bind, unbind, or troubleshoot NodeGroup resources, including node binding, namespace binding, workspace binding, and deployment/config inspection for nodegroup.
npx skills add https://github.com/kubesphere/kubesphere --skill nodegroup
Use this skill to perform real nodegroup-related operations in the edgewize-io/nodegroup environment.
This skill should help with:
NodeGroupNodeGroupconfig/nodegroupPrefer using the bundled script scripts/nodegroup_api.py for authenticated KAPI operations. Use kubectl only as a verification or fallback tool when the API path is unavailable.
Use the bundled script:
scripts/nodegroup_api.pyAuthentication model:
/oauth/token~/.kubesphere_tokenKUBESPHERE_HOST, KUBESPHERE_USERNAME, KUBESPHERE_PASSWORD, and KUBESPHERE_TOKENSetup:
cd scripts
pip install requests
export KUBESPHERE_HOST="http://<kubesphere-host>"
python nodegroup_api.py login --username admin --password <password>
Token helpers:
python nodegroup_api.py clear-cache
python nodegroup_api.py request GET /kapis/infra.kubesphere.io/v1alpha1/nodegroups
When you need to confirm how an operation works, read these files first from the nodegroup source repository root:
pkg/kapis/infra/v1alpha1/register.gopkg/kapis/infra/v1alpha1/handler.gopkg/kapis/infra/v1alpha1/workspace_handler.gopkg/controller/nodegroup/nodegroup_controller.gopkg/constants/constants.goDo not invent unsupported operations. Base the workflow on the source repository.
Cluster-scoped resource.
Important fields:
spec.aliasspec.descriptionspec.managerstatus.statepython nodegroup_api.py ... query commands.For any write operation, follow this order:
python nodegroup_api.py nodegroup list
python nodegroup_api.py nodegroup get <name>
python nodegroup_api.py nodegroup create \
--name <nodegroup-name> \
--alias "<alias>" \
--description "<description>" \
--manager "<manager>"
python nodegroup_api.py nodegroup get <nodegroup-name>
Prefer patching for small changes.
python nodegroup_api.py nodegroup patch <name> \
--alias "<new-alias>" \
--description "<new-description>" \
--manager "<manager>"
python nodegroup_api.py nodegroup get <name>
For unsupported fields or ad hoc testing, use raw request mode.
For PATCH, send a JSON Patch array:
python nodegroup_api.py request PATCH /kapis/infra.kubesphere.io/v1alpha1/nodegroups/<name> '[{"op":"add","path":"/spec/alias","value":"<new-alias>"}]'
Only do this when explicitly requested.
python nodegroup_api.py nodegroup delete <name>
If delete hangs, inspect finalizers:
python nodegroup_api.py nodegroup get <name>
Relevant finalizer:
finalizers.nodegroups.kubesphere.ioNode binding is implemented through nodegroup APIs and reflected with label:
apps.edgewize.io/nodegroup=<nodegroup-name>Read current state:
kubectl get node <node-name> --show-labels
kubectl get nodes -l apps.edgewize.io/nodegroup=<nodegroup-name>
Operate through the bundled script:
python nodegroup_api.py bind node --nodegroup <nodegroup-name> --node <node-name>
python nodegroup_api.py unbind node --nodegroup <nodegroup-name> --node <node-name>
# Verify
kubectl get nodes -l apps.edgewize.io/nodegroup=<nodegroup-name>
kubectl get node <node-name> -o yaml
Read current state:
kubectl get ns <namespace> --show-labels
Operate through the bundled script:
python nodegroup_api.py bind namespace --nodegroup <nodegroup-name> --namespace <namespace>
python nodegroup_api.py unbind namespace --nodegroup <nodegroup-name> --namespace <namespace>
# Verify
python nodegroup_api.py nodegroup get <nodegroup-name>
kubectl get ns <namespace> -o yaml
python nodegroup_api.py bind workspace --nodegroup <nodegroup-name> --workspace <workspace>
python nodegroup_api.py unbind workspace --nodegroup <nodegroup-name> --workspace <workspace>
When an operation appears to succeed but state is wrong, check these in order:
Useful checks:
python nodegroup_api.py nodegroup get <name>
kubectl get node <node-name> -o yaml
kubectl get ns <namespace> -o yaml
kubectl get cm nodegroup-config -n kubesphere-system -o yaml
kubectl get pods -n kubesphere-system | grep nodegroup
Important constants:
apps.edgewize.io/nodegroupapps.edgewize.io/namespace-nodegroup.infra.kubesphere.io/parentinfra.kubesphere.io/ippool-If the user asks how nodegroup is deployed or configured, inspect these files from the nodegroup source repository root:
config/nodegroup/templates/nodegroup-apiserver.ymlconfig/nodegroup/templates/nodegroup-controller-manager.yamlconfig/nodegroup/templates/nodegroup-config.yamlconfig/nodegroup/values.yamlConfigMap key:
nodegroup.yamlNotable config sections:
schedulingpolicyrbacippoolIf the user asks for an operation that already has a dedicated nodegroup API, prefer the bundled script and the dedicated nodegroup API semantics over manual label hacking.
Examples:
python nodegroup_api.py bind node ...python nodegroup_api.py bind namespace ...python nodegroup_api.py bind workspace ...python nodegroup_api.py request ...Only fall back to direct manifest edits or label repair when the request is explicitly about low-level repair.
When using this skill:
nodegroup_api.py command or API path to run nextAssess 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/nodegroup 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.
The instructions reference pip.
Without those the skill loads but fails at the first command.