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Kubesphere Gateway

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kubesphere
kubesphere-gateway

KubeSphere Gateway extension management Skill (ingress-nginx based, uses Kubernetes Ingress API + Gateway CRD gateway.kubesphere.io/v2alpha2). For the newer Kubernetes Gateway API (Traefik + GatewayProxy CRD), see the kubesphere-gateway-api skill instead. Covers installation, uninstallation, status checks, gateway status inspection, and troubleshooting (gateway stuck states, Helm failures, pod issues).

Overview

Publisherkubesphere
Repositorykubesphere
Skill namekubesphere-gateway
Stars
17.1K
Forks
2.8K
Bundled files
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  • 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.

  • 4 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 Kubesphere Gateway 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/kubesphere-gateway .claude/skills/kubesphere-gateway
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kubesphere Gateway 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 Kubesphere Gateway 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 Kubesphere Gateway 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.

KubeSphere Gateway

Overview

Provides external access management (ingress) for KubeSphere using ingress-nginx. Supports three-tier gateway management:

| Tier | Scope | Name Pattern | Namespace | Label | |---|---|---|---| | Cluster | Entire cluster | kubesphere-router-cluster | kubesphere-controls-system | kubesphere.io/gateway-type=cluster | | Workspace | Single workspace | kubesphere-router-workspace-{workspace} | kubesphere-controls-system | kubesphere.io/gateway-type=workspace | | Project | Single project/namespace | kubesphere-router-{namespace} | kubesphere-controls-system | kubesphere.io/gateway-type=project |

Each gateway is a standalone Helm release of ingress-nginx. The gateway-controller-manager manages the lifecycle (install/upgrade/uninstall) via Helm.

Core CRDs

  • Gateway (gateway.kubesphere.io/v2alpha2) — represents a single ingress-nginx deployment. Key fields:

    • spec.appVersion — the Helm chart version (e.g. kubesphere-nginx-ingress-<version>)
    • spec.values — Helm values for ingress-nginx (controller config, service type, resources, etc.)
    • status.stateCreating, Updating, Running, Faulted, Stopped
    • status.conditions[].type=GatewayReady — True when fully operational
    • status.loadBalancer — LB ingress IPs/hostnames
    • status.service — Service type, ports, external IPs
  • UpgradePlan (gateway.kubesphere.io/v2alpha2) — batch gateway upgrade job. Key fields:

    • spec.gatewayReferences — list of {name, namespace} to upgrade
    • spec.targetAppVersion — target version
    • status.statePending, Running, Succeeded, Failed

Monitoring Integration

Gateway exposes NGINX metrics (requests, 4xx/5xx, latency P50/P90/P99) via Prometheus. Requires the whizard-monitoring extension (optional dependency).


Before You Start

Check if Gateway extension is already installed:

bash
kubectl get installplans.kubesphere.io gateway --ignore-not-found

If found, upgrading is supported — just select a newer version in Step 1.


Installation

Step 1: Detect and Select Version

bash
ALL_VERSIONS=$(kubectl get extensionversions.kubesphere.io \
  -l kubesphere.io/extension-ref=gateway \
  -o jsonpath='{range .items[*]}{.spec.version}{"\n"}{end}' | sort -V)

LATEST_STABLE=$(echo "$ALL_VERSIONS" | grep -v -E 'alpha|beta|rc' | tail -1)
if [ -z "$LATEST_STABLE" ]; then
  LATEST_STABLE=$(echo "$ALL_VERSIONS" | tail -1)
fi

echo "Available versions:"
echo "$ALL_VERSIONS"
echo ""
echo "Latest stable: $LATEST_STABLE"

This sets ALL_VERSIONS and LATEST_STABLE. Use SELECTED_VERSION for the version chosen.

Use the question tool:

  • $LATEST_STABLE (Recommended) — accept the auto-detected version
  • (custom) — type a specific version; validate it against the printed list

Step 2: Detect and Select Clusters

bash
CLUSTER_DATA=$(kubectl get clusters.cluster.kubesphere.io \
  -o jsonpath='{range .items[*]}{.metadata.name}{"\t"}{.status.conditions[?(@.type=="Ready")].status}{"\n"}{end}')

READY_CLUSTERS=$(echo "$CLUSTER_DATA" | awk -F'\t' '$2 == "True" {print $1}')
CLUSTER_COUNT=$(echo "$READY_CLUSTERS" | wc -l)

HOST_CLUSTER=$(kubectl get clusters.cluster.kubesphere.io \
  -l 'cluster-role.kubesphere.io/host' \
  -o jsonpath='{.items[0].metadata.name}' || echo "")

echo "Ready clusters:"
echo "$READY_CLUSTERS"
echo ""
echo "Cluster count: $CLUSTER_COUNT"
echo "Host cluster: $HOST_CLUSTER"

This sets READY_CLUSTERS, CLUSTER_COUNT, HOST_CLUSTER.

  • 1 cluster → skip selection, auto-use it. Set TARGET_CLUSTERS="$HOST_CLUSTER"
  • Multiple clusters → use question with multiple: true:
    • All clustersTARGET_CLUSTERS="$READY_CLUSTERS"
    • Host cluster onlyTARGET_CLUSTERS="$HOST_CLUSTER"
    • (custom) — validate each name against $READY_CLUSTERS

Step 3: Generate and Apply InstallPlan

bash
./scripts/generate-installplan.sh "$SELECTED_VERSION" "$TARGET_CLUSTERS"

This generates the YAML to /tmp/gateway-installplan.yaml, runs --dry-run=server, then prints the apply command.

For configurable extension values (ingress-nginx default settings, image registry, upgrade tool config, etc.), see references/extension-values.md.

Apply it:

bash
kubectl apply -f /tmp/gateway-installplan.yaml

Tell the user "Installing". Then ask if they want to check status. If yes:

bash
./scripts/check-status.sh poll

Status Checking

PurposeCommand
Single snapshot./scripts/check-status.sh quick
Wait until complete (5min timeout)./scripts/check-status.sh poll

Logic:

  • All Installed → ✓ success
  • Any Failed → ✗ prints full status
  • Timeout (300s) → ⚠ prints current status
  • In progress → prints every 10s

Uninstallation

Always confirm with the user before proceeding.

Uninstall from all clusters

bash
if ! kubectl get installplans.kubesphere.io gateway &>/dev/null; then
  echo "Gateway is not installed."
  exit 0
fi

Confirm with the user, then delete:

bash
kubectl delete installplans.kubesphere.io gateway --ignore-not-found

Verify cleanup:

bash
./scripts/verify-uninstall.sh

Success criteria:

  1. InstallPlan is deleted
  2. No active pods remain in extension-gateway namespace

Uninstall from specific clusters

WARNING: Do NOT delete the InstallPlan. Only remove target clusters from the placement list.

Confirm which clusters to remove, compute remaining clusters, then patch:

bash
kubectl patch installplans.kubesphere.io gateway --type='json' \
  -p='[{"op": "replace", "path": "/spec/clusterScheduling/placement/clusters", "value": ["<REMAINING_CLUSTER_1>", "<REMAINING_CLUSTER_2>"]}]'

Success: patch returns OK + removed clusters no longer in .status.clusterSchedulingStatuses.


Gateway Operations

List Gateways

Gateways are organized by tier (see Overview), with each tier identified by the label kubesphere.io/gateway-type. List them grouped by tier:

bash
echo "=== Cluster Gateway ==="
kubectl get gateways.gateway.kubesphere.io -n kubesphere-controls-system \
  -l kubesphere.io/gateway-type=cluster

echo -e "\n=== Workspace Gateways ==="
kubectl get gateways.gateway.kubesphere.io -n kubesphere-controls-system \
  -l kubesphere.io/gateway-type=workspace

echo -e "\n=== Project Gateways ==="
kubectl get gateways.gateway.kubesphere.io -n kubesphere-controls-system \
  -l kubesphere.io/gateway-type=project

Check Gateway Status

Pick a gateway name from the List Gateways output and run:

bash
GW_NS="kubesphere-controls-system"
GW_NAME="<gateway-name-from-list>"

# app.kubernetes.io/instance uses the Helm release name if available, otherwise the Gateway name
GW_INSTANCE=$(kubectl get gateways.gateway.kubesphere.io -n $GW_NS $GW_NAME -o jsonpath='{.status.helmRelease.name}' 2>/dev/null)
if [ -z "$GW_INSTANCE" ]; then
  GW_INSTANCE="$GW_NAME"
fi

kubectl get gateways.gateway.kubesphere.io -n $GW_NS $GW_NAME -o wide
kubectl describe gateways.gateway.kubesphere.io -n $GW_NS $GW_NAME
kubectl get gateways.gateway.kubesphere.io -n $GW_NS $GW_NAME -o yaml
kubectl get pods -n $GW_NS -l "app.kubernetes.io/instance=$GW_INSTANCE"

Gateway states:

StateMeaning
CreatingFirst-time Helm install in progress
UpdatingHelm upgrade in progress (spec changed)
RunningFully operational (all replicas available)
FaultedDeployment missing, stopped unexpectedly, or health probe timeout
StoppedScaled to zero replicas intentionally

Troubleshooting

Set $GW_NAME according to the gateway tier being troubleshot (see naming rules in Overview):

  • Cluster → GW_NAME=kubesphere-router-cluster
  • Workspace → GW_NAME=kubesphere-router-workspace-${WORKSPACE}
  • Project → GW_NAME=kubesphere-router-${NAMESPACE}

Common namespace: GW_NS=kubesphere-controls-system

$GW_INSTANCE is auto-resolved from status.helmRelease.name (falls back to $GW_NAME). If not yet set, run:

bash
GW_INSTANCE=$(kubectl get gateways.gateway.kubesphere.io -n $GW_NS $GW_NAME -o jsonpath='{.status.helmRelease.name}' 2>/dev/null)
if [ -z "$GW_INSTANCE" ]; then
  GW_INSTANCE="$GW_NAME"
fi

Gateway stuck in Creating or Updating state

bash
kubectl describe gateways.gateway.kubesphere.io -n $GW_NS $GW_NAME

kubectl logs -n extension-gateway -l app=gateway-controller-manager --tail=200 | grep -iE "(error|helm|install|upgrade|reconcile)"

kubectl get deployment -n $GW_NS -l "app.kubernetes.io/instance=$GW_INSTANCE,app.kubernetes.io/component=controller"

kubectl get configmap -n $GW_NS $GW_NAME -o yaml

Common causes: Chart ConfigMap missing/corrupted, Helm wrapper timeout, invalid spec.values.

Gateway shows Faulted state

bash
kubectl get deployment -n $GW_NS $GW_NAME -o wide
kubectl describe deployment -n $GW_NS $GW_NAME
kubectl get pods -n $GW_NS -l "app.kubernetes.io/instance=$GW_INSTANCE" -o wide

POD_NAME=$(kubectl get pods -n $GW_NS -l "app.kubernetes.io/instance=$GW_INSTANCE" -o jsonpath='{.items[0].metadata.name}')
kubectl describe pod -n $GW_NS $POD_NAME
kubectl logs -n $GW_NS $POD_NAME --tail=100

Common causes: Image pull failure, resource constraints, port conflicts, missing ConfigMap/Secret.

Gateway pod crash-looping / CrashLoopBackOff

bash
kubectl logs -n $GW_NS -l "app.kubernetes.io/instance=$GW_INSTANCE" --tail=100 --previous
kubectl get events -n $GW_NS --sort-by='.lastTimestamp' | tail -20
kubectl exec -n $GW_NS -l "app.kubernetes.io/instance=$GW_INSTANCE" -- cat /etc/nginx/nginx.conf 2>/dev/null | head -50
kubectl get configmap -n $GW_NS -l "app.kubernetes.io/instance=$GW_INSTANCE" -o yaml

Common causes: Misconfigured nginx config, port conflicts, resource limits (OOMKilled), missing dependencies (ConfigMap/Secret).

Log search not working

Gateway log search proxies to whizard-telemetry-apiserver:

bash
kubectl get configmap -n extension-gateway gateway-agent-backend-config -o yaml
kubectl get pods -n extension-whizard-telemetry
kubectl get svc -n extension-whizard-telemetry whizard-telemetry-apiserver
kubectl logs -n extension-gateway -l app=gateway-apiserver --tail=100 | grep -iE "(log|search|whizard|proxy)"

Common causes: Whizard-telemetry not installed or not running, misconfigured gateway-agent-backend-config, network policy blocking cross-namespace traffic.

Gateway controller not reconciling

bash
kubectl get pods -n extension-gateway -l app=gateway-controller-manager
kubectl logs -n extension-gateway -l app=gateway-controller-manager --tail=200
kubectl get validatingwebhookconfiguration -l "app.kubernetes.io/managed-by=Helm,kubesphere.io/extension-ref=gateway"
kubectl get deployment -n extension-gateway -l app=gateway-controller-manager -o yaml

Common causes: Controller pod not running, webhook configuration blocking updates, Helm release state mismatch, RBAC permission issues.

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 Kubesphere Gateway AI skill do?

KubeSphere Gateway extension management Skill (ingress-nginx based, uses Kubernetes Ingress API + Gateway CRD gateway.kubesphere.io/v2alpha2). For the newer Kubernetes Gateway API (Traefik + GatewayProxy CRD), see the kubesphere-gateway-api skill instead. Covers installation, uninstallation, status checks, gateway status inspection, and troubleshooting (gateway stuck states, Helm failures, pod issues).

Why use Kubesphere Gateway on TypingMind?

Because you install it once and use it with any model. Kubesphere Gateway 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 Kubesphere Gateway in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kubesphere/kubesphere/tree/master/skills/kubesphere-gateway. 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 Kubesphere Gateway?

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 Kubesphere Gateway?

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

Is the Kubesphere Gateway 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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