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Frontend Forge Fe Operations

OrganizationPopular
kubesphere
frontend-forge-fe-operations

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.

Overview

Publisherkubesphere
Repositorykubesphere
Skill namefrontend-forge-fe-operations
Stars
17.1K
Forks
2.8K
Bundled files
5
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kubesphere on GitHub. Read the source before you install it.

Installation

Install the Frontend Forge Fe Operations AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kubesphere/kubesphere.git /tmp/kubesphere
mkdir -p .claude/skills
cp -r /tmp/kubesphere/skills/frontend-forge-fe-operations .claude/skills/frontend-forge-fe-operations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontend Forge Fe Operations in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Frontend Forge Fe Operations on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Frontend Forge Fe Operations is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Frontend Forge FE Operations

When to use

Use this skill for operational work around FrontendExtension resources:

  • Create or update an FE manifest
  • Trigger or inspect package generation
  • Force a rebuild with the rebuild-token annotation
  • Download the generated package artifact
  • Publish, unpublish, or delete an extension
  • Inspect FE status, conditions, labels, Jobs, artifact ConfigMaps, and publish targets
  • Debug FE package phases such as Packaging, Ready, and Failed, plus publish/unpublish phases such as Pending, Running, Succeeded, and Failed

If 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.

Quick Entry

  • Create or update FE -> read references/lifecycle.md
  • Rebuild package -> read references/lifecycle.md
  • Publish, unpublish, delete-with-unpublish, list through API, or download -> read references/api.md
  • Inspect package Job, artifact ConfigMap, publish Job, labels, or conditions -> read references/inspection.md
  • Debug stuck or failed state -> read references/inspection.md, then check controller logs

Preconditions

  • The current Kubernetes context is pointed at the target cluster.
  • The FE CRD exists: frontendextensions.frontend-forge.kubesphere.io.
  • The FE controller is installed, usually as the extension-controller component.
  • The FE API is installed when using download, publish, unpublish, or delete-with-unpublish HTTP operations.
  • The build service configured by BUILD_SERVICE_BASE_URL is reachable from package Jobs.
  • Publish target data exists when publishing, normally ConfigMap/ksbuilder-publish-config in extension-frontend-forge.

Source of truth

Prefer live cluster state for operations and repo docs for expected behavior:

  • kubectl get fe <name> -o yaml
  • kubectl get jobs -n <work-namespace> -l frontend-forge.kubesphere.io/fe-name=<name>
  • spec/frontend-extension-design.md
  • spec/crds.md
  • spec/k8s-resources.md
  • config/samples/frontendextension-inspecttask.yaml
  • crates/api/src/fe.rs
  • crates/frontend-extension-controller
  • crates/frontend-forge-extension-api

Resource Model

  • FrontendExtension
    • cluster-scoped
    • short name: fe
    • source object for package, artifact, download, publish, and unpublish state
  • Package Job
    • namespaced
    • default namespace: extension-frontend-forge
    • typical name: fe-<fe-name>-package-<artifact-key-12>-a<attempt>
  • Artifact ConfigMap
    • namespaced
    • default namespace: extension-frontend-forge
    • referenced by status.artifact.storage.ref
    • contains binaryData["package.tgz"], data["artifact.json"], and data["files.json"]
  • Publish or unpublish Job
    • namespaced
    • default namespace: extension-frontend-forge
    • typical names:
      • fe-<fe-name>-publish-<request-id-hash-short>
      • fe-<fe-name>-unpublish-<request-id-hash-short>
  • Publish target
    • ConfigMap or Secret
    • default chart target is usually ConfigMap/ksbuilder-publish-config in the release namespace

Default 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.

Common Commands

Inspect FE:

bash
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:

bash
kubectl apply -f <file.yaml>
kubectl apply -f config/samples/frontendextension-inspecttask.yaml

Find package Job and logs:

bash
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:

bash
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:

bash
kubectl annotate fe <name> frontend-forge.kubesphere.io/rebuild-token="$(date +%s)" --overwrite

Inspect publish or unpublish Jobs:

bash
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:

bash
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

Operating Rules

  • Do not use -n with kubectl get fe; FrontendExtension is cluster-scoped.
  • Use the FE HTTP API for publish, unpublish, download, and delete-with-unpublish when available. It validates artifact readiness, publish target, digest expectations, and idempotency.
  • For KubeSphere users, prefer /kapis/frontend-forge-api.kubesphere.io/v1alpha1/frontendextensions; use direct service port-forwarding mainly for local debugging.
  • If /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.
  • Treat publish and unpublish annotations as implementation details written by the FE API. Inspect them for debugging, but do not make raw annotation patches the default workflow.
  • Directly patch publish/unpublish annotations only as a last-resort recovery step when the API is unavailable and the operator explicitly accepts the risk. Missing or stale generation, source hash, artifact digest, or target annotations can produce failed publish status.
  • Do not expect package creation to publish the extension. Packaging and publishing are separate flows.
  • Do not expect direct kubectl delete fe <name> to unpublish first. Use the FE API delete endpoint with {"unpublish":true} for that behavior.
  • Treat status.artifact.artifactKey as build cache identity, not package content digest. Use status.artifact.digest for the generated package digest.
  • Treat artifact ConfigMaps as controller-owned outputs. Read them for inspection, but do not edit or delete the ConfigMap referenced by status.artifact.storage.ref unless the user has explicitly requested artifact cleanup and you have confirmed it is not the current artifact.

Safety Rules

Destructive operations:

  • Before deleting an FE, check .status.publish.phase, .status.publish.active, and .status.publish.artifactDigest.
  • If a published extension should be removed, use the FE API delete endpoint with {"unpublish":true} so the controller can unpublish before deleting.
  • Avoid kubectl delete fe <name> for published extensions unless the user explicitly wants to skip unpublish.
  • Before deleting Jobs or artifact ConfigMaps, verify owner refs, labels, and whether FE status still references them.

Rebuild:

  • Rebuild changes artifact cache identity and can make a previously published artifact stale. Before setting 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.
  • Prefer a unique rebuild token, such as a timestamp or incident id, and use --overwrite.
  • After rebuild, verify the new package reaches Ready; republish only after confirming the new status.artifact.digest.

Artifact ConfigMap:

  • Use the FE API download endpoint for package bytes when possible; it checks phase, download readiness, source hash, storage kind, and digest.
  • Do not mutate binaryData["package.tgz"], data["artifact.json"], or data["files.json"] in place. A manual edit can make status, annotations, and digest disagree.
  • Do not delete the ConfigMap named by .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.

Status Fields To Check

FE package state:

  • .status.phase
  • .status.observedGeneration
  • .status.observedSourceHash
  • .status.observedRebuildToken
  • .status.conditions
  • .status.packageJob
  • .status.artifact
  • .status.download

Publish 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.lastError

Labels used for filtering:

  • frontend-forge.kubesphere.io/package-state: packaging, ready, failed
  • frontend-forge.kubesphere.io/publish-state: not-published, publishing, published, failed
  • frontend-forge.kubesphere.io/publish-fresh: true, false

Status consistency with implementation:

  • FE package phase is only Pending, Packaging, Ready, or Failed.
  • Package Job phase is Pending, Running, Succeeded, or Failed.
  • Publish phase is NotRequested, Pending, Running, Succeeded, or Failed.
  • Unpublish phase is NotRequested, Pending, Running, Succeeded, or Failed.
  • Controller condition types are 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.

Troubleshooting Order

  1. Inspect the FE object and status first.
  2. If package state is not Ready, inspect status.conditions, status.packageJob, package Job logs, build-service reachability, and artifact ConfigMap state.
  3. If package state is Ready but download fails, inspect status.download, status.artifact.storage, the artifact ConfigMap key, and digest consistency.
  4. If publish or unpublish fails, inspect 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.
  5. If live behavior differs from expected defaults, inspect controller deployment env vars and Helm values before changing the FE.

Controller Logs

When status and Job logs are insufficient:

bash
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:

bash
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Frontend Forge Fe Operations AI skill do?

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.

Why use Frontend Forge Fe Operations on TypingMind?

Because you install it once and use it with any model. Frontend Forge Fe Operations is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Frontend Forge Fe Operations in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kubesphere/kubesphere/tree/master/skills/frontend-forge-fe-operations. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Frontend Forge Fe Operations?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Frontend Forge Fe Operations?

As many as you like. As long as a model supports skills, you can use Frontend Forge Fe Operations with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Frontend Forge Fe Operations AI skill free?

It is published on GitHub by kubesphere. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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