Operate FrontendExtension (FE) resources in frontend-forge: create, update, rebuild, inspect package artifacts, download packages, publish, unpublish, delete, and debug package/publish controller behavior. Use this skill whenever the user mentions FE operations, FrontendExtension lifecycle, extension package/download/publish/unpublish, artifact ConfigMaps, package Jobs, publisher Jobs, publish target ConfigMaps or Secrets, rebuild-token, package-state/publish-state labels, or troubleshooting FE status in a Kubernetes cluster.
npx skills add https://github.com/kubesphere/kubesphere --skill frontend-forge-fe-operations
Use this skill for operational work around FrontendExtension resources:
Packaging, Ready, and Failed, plus publish/unpublish phases such as Pending, Running, Succeeded, and FailedIf the task is about FrontendIntegration runtime JSBundle creation, use the FI operations skill instead. FE package/publish does not create a runtime JSBundle in the current cluster.
references/lifecycle.mdreferences/lifecycle.mdreferences/api.mdreferences/inspection.mdreferences/inspection.md, then check controller logsfrontendextensions.frontend-forge.kubesphere.io.extension-controller component.BUILD_SERVICE_BASE_URL is reachable from package Jobs.ConfigMap/ksbuilder-publish-config in extension-frontend-forge.Prefer live cluster state for operations and repo docs for expected behavior:
kubectl get fe <name> -o yamlkubectl get jobs -n <work-namespace> -l frontend-forge.kubesphere.io/fe-name=<name>spec/frontend-extension-design.mdspec/crds.mdspec/k8s-resources.mdconfig/samples/frontendextension-inspecttask.yamlcrates/api/src/fe.rscrates/frontend-extension-controllercrates/frontend-forge-extension-apiFrontendExtensionfeextension-frontend-forgefe-<fe-name>-package-<artifact-key-12>-a<attempt>extension-frontend-forgestatus.artifact.storage.refbinaryData["package.tgz"], data["artifact.json"], and data["files.json"]extension-frontend-forgefe-<fe-name>-publish-<request-id-hash-short>fe-<fe-name>-unpublish-<request-id-hash-short>ConfigMap or SecretConfigMap/ksbuilder-publish-config in the release namespaceDefault names and namespaces can change through Helm values and controller environment variables. Use FE status, Job labels, and controller deployment env vars before assuming defaults.
Inspect FE:
kubectl get fe <name> -o yaml
kubectl get fe <name> -o jsonpath='{.status}{"\n"}'
kubectl get fe <name> -o jsonpath='{.status.phase}{" "}{.status.publish.phase}{" "}{.status.unpublish.phase}{"\n"}'
kubectl get fe -l frontend-forge.kubesphere.io/package-state=ready
kubectl get fe -l frontend-forge.kubesphere.io/publish-state=published
Create or update:
kubectl apply -f <file.yaml>
kubectl apply -f config/samples/frontendextension-inspecttask.yaml
Find package Job and logs:
kubectl get fe <name> -o jsonpath='{.status.packageJob.namespace}{" "}{.status.packageJob.name}{"\n"}'
kubectl -n <job-namespace> get job <job-name> -o yaml
kubectl -n <job-namespace> logs job/<job-name>
Find artifact ConfigMap:
kubectl get fe <name> -o jsonpath='{.status.artifact.storage.ref.namespace}{" "}{.status.artifact.storage.ref.name}{" "}{.status.artifact.storage.key}{"\n"}'
kubectl -n <artifact-namespace> get cm <artifact-configmap-name> -o yaml
Force a rebuild:
kubectl annotate fe <name> frontend-forge.kubesphere.io/rebuild-token="$(date +%s)" --overwrite
Inspect publish or unpublish Jobs:
kubectl get jobs -n extension-frontend-forge -l frontend-forge.kubesphere.io/fe-name=<name>
kubectl get fe <name> -o jsonpath='{.status.publish.jobRef.namespace}{" "}{.status.publish.jobRef.name}{"\n"}'
kubectl get fe <name> -o jsonpath='{.status.unpublish.jobRef.namespace}{" "}{.status.unpublish.jobRef.name}{"\n"}'
kubectl -n <job-namespace> logs job/<job-name>
FE API operations:
KS_API=https://<kubesphere-host>
FE_API="$KS_API/kapis/frontend-forge-api.kubesphere.io/v1alpha1/frontendextensions"
curl -fS "$FE_API/<name>"
curl -fS -u "user:password" "$FE_API/<name>" # user runs this if /kapis returns 401/403
curl -fS "$FE_API/<name>/publish" # read publish status only
curl -fS -X POST -H 'Content-Type: application/json' --data '{"requestId":"manual-1","expectedArtifactDigest":"sha256:<digest>"}' "$FE_API/<name>/publish"
curl -fS "$FE_API/<name>/unpublish" # read unpublish status only
curl -fS -X POST -H 'Content-Type: application/json' --data '{"requestId":"manual-unpublish-1"}' "$FE_API/<name>/unpublish"
curl -fS -X POST -H 'Content-Type: application/json' --data '{"unpublish":true}' "$FE_API/<name>/delete"
curl -fS -u "user:password" -X POST -H 'Content-Type: application/json' --data '{"unpublish":true}' "$FE_API/<name>/delete"
curl -fL "$FE_API/<name>/download" -o <name>.tgz
-n with kubectl get fe; FrontendExtension is cluster-scoped./kapis/frontend-forge-api.kubesphere.io/v1alpha1/frontendextensions; use direct service port-forwarding mainly for local debugging./kapis returns 401 or 403, guide the user to run curl -u "user:password" ... or use their normal KubeSphere authenticated session. Do not ask them to paste credentials into the conversation.kubectl delete fe <name> to unpublish first. Use the FE API delete endpoint with {"unpublish":true} for that behavior.status.artifact.artifactKey as build cache identity, not package content digest. Use status.artifact.digest for the generated package digest.status.artifact.storage.ref unless the user has explicitly requested artifact cleanup and you have confirmed it is not the current artifact.Destructive operations:
.status.publish.phase, .status.publish.active, and .status.publish.artifactDigest.{"unpublish":true} so the controller can unpublish before deleting.kubectl delete fe <name> for published extensions unless the user explicitly wants to skip unpublish.Rebuild:
frontend-forge.kubesphere.io/rebuild-token, record current .status.observedSourceHash, .status.artifact.digest, .status.artifact.artifactKey, .status.publish, and frontend-forge.kubesphere.io/publish-fresh.--overwrite.Ready; republish only after confirming the new status.artifact.digest.Artifact ConfigMap:
binaryData["package.tgz"], data["artifact.json"], or data["files.json"] in place. A manual edit can make status, annotations, and digest disagree..status.artifact.storage.ref during normal troubleshooting. If cleanup is requested, first confirm the current FE artifact points somewhere else or the FE itself is being deleted.FE package state:
.status.phase.status.observedGeneration.status.observedSourceHash.status.observedRebuildToken.status.conditions.status.packageJob.status.artifact.status.downloadPublish state:
.status.publish.phase.status.publish.active.status.publish.requestId.status.publish.artifactDigest.status.publish.jobRef.status.publish.lastError.status.unpublish.phase.status.unpublish.requestId.status.unpublish.extensionName.status.unpublish.jobRef.status.unpublish.lastErrorLabels used for filtering:
frontend-forge.kubesphere.io/package-state: packaging, ready, failedfrontend-forge.kubesphere.io/publish-state: not-published, publishing, published, failedfrontend-forge.kubesphere.io/publish-fresh: true, falseStatus consistency with implementation:
Pending, Packaging, Ready, or Failed.Pending, Running, Succeeded, or Failed.NotRequested, Pending, Running, Succeeded, or Failed.NotRequested, Pending, Running, Succeeded, or Failed.SourceValid, ArtifactReady, DownloadReady, and PublishSucceeded.PublishFailed appears as a PublishSucceeded condition reason when status.publish.phase=Failed; it is not a top-level FE phase.Ready, inspect status.conditions, status.packageJob, package Job logs, build-service reachability, and artifact ConfigMap state.Ready but download fails, inspect status.download, status.artifact.storage, the artifact ConfigMap key, and digest consistency.status.publish or status.unpublish, publisher Job logs, target ConfigMap or Secret contents, and API responses. Use annotations as diagnostic evidence, not as the primary operation path.When status and Job logs are insufficient:
kubectl -n extension-frontend-forge logs deploy/frontend-forge-extension-controller --tail=200
kubectl -n extension-frontend-forge logs deploy/frontend-forge-extension-api --tail=200
Deployment names can vary by Helm release name. If those commands fail, list deployments with:
kubectl -n extension-frontend-forge get deploy -l app.kubernetes.io/component=extension-controller
kubectl -n extension-frontend-forge get deploy -l app.kubernetes.io/component=extension-api
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/frontend-forge-fe-operations 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.