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Gke Service Networking

OrganizationPopular
google
gke-service-networking

Configures GKE edge networking, traffic routing, load balancing, and private service endpoints. Use when configuring Gateway API manifests, standard Ingress, Cloud Armor WAF security policies, Container-Native Load Balancing (NEGs), Private Service Connect (PSC), or Google-managed SSL certificates on GKE, and to troubleshoot Ingress and load-balancer 502/5xx errors, backend health-check failures, connection draining, and TLS/SSL policy enforcement. Don't use for core cluster IP planning, Dataplane V2 network policies, or node NAT egress (use gke-networking instead).

Overview

Publishergoogle
Repositoryskills
Skill namegke-service-networking
Stars
20.5K
Forks
1.7K
Bundled files
8
LicenseApache-2.0
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Gke Service Networking 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.
    https://github.com/google/skills/tree/main/skills/cloud/gke-service-networking
  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/google/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/cloud/gke-service-networking .claude/skills/gke-service-networking
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gke Service Networking 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 Gke Service Networking 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 Gke Service Networking 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.

GKE Service Networking Skill

This skill provides workflows for exposing applications running on GKE securely to the internet or internal networks.

Deployable manifest templates live in assets/ — edit the # Replace ... placeholders before applying.

Workflows

1. Configure Gateway API (Recommended)

The Gateway API is the modern way to manage routing in Kubernetes.

Prerequisites: Gateway API must be enabled on the cluster (enabled by default on new clusters running GKE 1.26+; on older supported versions enable it with --gateway-api=standard).

Templates:

  • assets/gateway.yaml — external Gateway using the gke-l7-global-external-managed GatewayClass with an HTTP listener.
  • assets/httproute.yaml — HTTPRoute attaching to the Gateway via parentRefs and routing a path prefix to a Service backendRef.
  • assets/httproute-traffic-split.yaml — HTTPRoute demonstrating weighted traffic splitting (e.g. 90/10) for canary deployments across backend services.
bash
kubectl apply -f assets/gateway.yaml
kubectl apply -f assets/httproute.yaml

Traffic Splitting (Canary Deployments):

HTTPRoute supports weighted traffic splitting across multiple backend Services for canary rollouts:

yaml
spec:
  rules:
    - backendRefs:
        - name: app-v1
          port: 80
          weight: 90
        - name: app-v2
          port: 80
          weight: 10

2. Configure Standard GKE Ingress

Use standard Ingress for simpler use cases or legacy setups.

Template: assets/ingress.yaml — GCE Ingress (kubernetes.io/ingress.class: "gce" annotation) routing to a Service.

3. Secure with Cloud Armor

Cloud Armor provides WAF and DDoS protection.

  1. Create a Security Policy in Cloud Armor:

    bash
    gcloud compute security-policies create {security_policy_name} \
      --description "WAF policy for {app_name}"
    
    # Example rule: block an abusive IP range
    gcloud compute security-policies rules create 1000 \
      --security-policy {security_policy_name} \
      --action deny-403 \
      --src-ip-ranges "203.0.113.0/24" \
      --description "Block abusive range"
  2. Reference it in a BackendConfig: assets/backendconfig.yaml (sets spec.securityPolicy.name).

  3. Associate the BackendConfig with your Service via annotations:

    yaml
    # In your Kubernetes Service manifest metadata.annotations:
    cloud.google.com/backend-config: '{"default": "{backend_config_name}"}'
    # Or for specific port mappings:
    cloud.google.com/backend-config: '{"ports": {"80": "{backend_config_name}"}}'

4. Configure Google-Managed SSL Certificates

Automatically provision and renew SSL certificates.

Legacy Ingress approach: apply assets/managed-certificate.yaml (a ManagedCertificate listing your domains), then reference it in the Ingress annotations:

yaml
networking.gke.io/managed-certificates: {certificate_name}

Gateway API approach: for standard Certificate Manager integration, create a CertificateMap and reference it in the Gateway metadata annotations using the exact annotation networking.gke.io/certmap (spelled without any hyphens in certmap):

yaml
metadata:
  annotations:
    networking.gke.io/certmap: {certificate_map_name}

[!IMPORTANT] The annotation key is strictly networking.gke.io/certmap (do not use cert-map or certificate-map).

Alternatively, reference a Kubernetes Secret in the HTTPS listener's tls.certificateRefs. Both variants are in assets/gateway-https.yaml.

5. Enable Container-Native Load Balancing (Recommended)

Container-native load balancing allows load balancers to target Kubernetes Pods directly, rather than targeting nodes. This improves latency and distribution.

Prerequisites: Cluster must be VPC-native.

How it works: the cloud.google.com/neg annotation on a Service triggers creation of a NEG that mirrors the Pod IPs. GKE often adds it for you — but not always, and knowing which case you are in is the whole point.

yaml
# In your Kubernetes Service manifest metadata.annotations:
cloud.google.com/neg: '{"ingress": true}'

When the annotation is automatic (do not add it by hand):

  • Internal Ingress — container-native load balancing is always used, not optional. Internal Ingress always uses GCE_VM_IP_PORT NEGs and requires a VPC-native cluster.
  • External Ingress, but only when all four hold: the cluster is VPC-native, is not on Shared VPC, does not use GKE Network Policy, and has the HttpLoadBalancing add-on enabled (on by default — do not disable it). GKE then annotates Services automatically.

When you must add it explicitly:

  • Standalone NEGs — you manage the load balancer yourself instead of letting Ingress own it. Required if the LB must be configured outside GKE, since Ingress overwrites managed load balancer settings on sync or upgrade. You become responsible for every part of the load balancer.
  • Any external-Ingress cluster failing one of the four conditions above — Shared VPC, GKE Network Policy, or non-VPC-native. Enable per Service.
  • Legacy configurations — some older external Ingress objects created on VPC-native clusters still use instance group backends.

Not supported / no NEG fallback:

  • Windows Server node pools.
  • Routes-based (non-VPC-native) clusters with external Ingress — the Ingress controller falls back to unmanaged instance groups spanning all nodes.

Scale consequence: without NEGs a cluster is capped at 1,000 nodes, and non-NEG Services behind Ingress stop functioning correctly beyond that. With NEGs there is no GKE node limit.

6. Configure Private Service Connect (PSC)

Private Service Connect allows you to expose services in one VPC to consumers in another VPC securely, without VPC peering.

Prerequisite: The backing Service must be an internal passthrough Network Load Balancer — i.e. type: LoadBalancer with the networking.gke.io/load-balancer-type: "Internal" annotation. The ServiceAttachment requires this; a ClusterIP or external LoadBalancer Service will not work.

Steps:

  1. Create an internal LoadBalancer Service for your workload.
  2. Create a ServiceAttachment referencing that Service: assets/service-attachment.yaml (sets connectionPreference, the PSC NAT subnet, and the Service resourceRef).
  3. Share the ServiceAttachment URI with consumers to create a PSC endpoint in their VPC.

7. Topology Aware Routing (Cost & Latency Optimization)

To minimize cross-zone data transfer costs and network latency, configure Kubernetes Services with Topology Aware Routing. This routes traffic to Pods in the same zone as the originating client:

yaml
# In your Kubernetes Service manifest metadata.annotations:
service.kubernetes.io/topology-mode: auto

Troubleshooting

Diagnose Ingress / load-balancer data-plane failures. These map to both Ingress (BackendConfig / FrontendConfig) and Gateway (GCPBackendPolicy / HealthCheckPolicy / GCPGatewayPolicy). Stay at the read-only → propose-manifest boundary; never apply live mutations directly.

502 / 5xx with UNHEALTHY backends (health checks)

The Google Cloud load-balancer health check is separate from Kubernetes liveness/readiness probes — it runs from outside the cluster, so a Pod can be Ready while the backend service still shows UNHEALTHY.

  1. Allow the Google health-check source ranges to the node/Pod serving port. GKE usually creates this rule automatically, but on Shared VPC or with hand-managed firewalls it can be missing:

    bash
    gcloud compute firewall-rules create allow-lb-health-checks \
      --allow tcp:SERVING_PORT \
      --source-ranges 130.211.0.0/22,35.191.0.0/16 \
      --target-tags NODE_TAG
  2. Point the health check at a healthy endpoint. If the default / returns a non-200, set a custom health check with a BackendConfig (Ingress) or a HealthCheckPolicy (Gateway):

    yaml
    # BackendConfig (Ingress)
    spec:
      healthCheck:
        requestPath: /healthz
        port: 8080
        checkIntervalSec: 15
        timeoutSec: 5
  3. Confirm the Service is container-native (NEG) so the check targets Pod IPs rather than nodes (see workflow 5).

502 / dropped requests during rollouts or on long requests

  • Enable connection draining so in-flight requests finish before a backend Pod is removed during a rolling update or scale-down:

    yaml
    # BackendConfig (Ingress)
    spec:
      connectionDraining:
        drainingTimeoutSec: 60
  • Raise the backend timeout for slow or streaming responses — a 502/408 on a request that runs longer than the backend response timeout is the classic symptom. Set timeoutSec in the BackendConfig (Ingress) or GCPBackendPolicy (Gateway).

TLS / SSL handshake failures or weak-cipher enforcement

  • Ingress: attach an SSL policy (minimum TLS version / cipher profile) with a FrontendConfig sslPolicy, and optionally force HTTP→HTTPS with redirectToHttps:

    yaml
    # FrontendConfig (external Ingress only)
    spec:
      sslPolicy: gke-ingress-ssl-policy
      redirectToHttps:
        enabled: true
  • Gateway: attach the SSL policy name in a GCPGatewayPolicy. For a regional Gateway, create and reference a regional SSL policy.

Gotchas

  1. Certificate Manager API must be enabled for the networking.gke.io/certmap annotation to work (gcloud services enable certificatemanager.googleapis.com); without it the Gateway fails to provision the certificate map.
  2. Regional Gateway classes need a proxy-only subnet: classes like gke-l7-regional-external-managed and gke-l7-rilb require a subnet with --purpose=REGIONAL_MANAGED_PROXY in the region; the Gateway stays unprogrammed without it.
  3. ManagedCertificate provisioning depends on DNS: the certificate stays in Provisioning until the domain's A/AAAA records point at the load balancer IP, and can take 15-60 minutes after DNS is correct.

References

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 Gke Service Networking AI skill do?

Configures GKE edge networking, traffic routing, load balancing, and private service endpoints. Use when configuring Gateway API manifests, standard Ingress, Cloud Armor WAF security policies, Container-Native Load Balancing (NEGs), Private Service Connect (PSC), or Google-managed SSL certificates on GKE, and to troubleshoot Ingress and load-balancer 502/5xx errors, backend health-check failures, connection draining, and TLS/SSL policy enforcement. Don't use for core cluster IP planning, Dataplane V2 network policies, or node NAT egress (use gke-networking instead).

Why use Gke Service Networking on TypingMind?

Because you install it once and use it with any model. Gke Service Networking 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 Gke Service Networking in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/skills/tree/main/skills/cloud/gke-service-networking. 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 Gke Service Networking?

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 Gke Service Networking?

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

Is the Gke Service Networking AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 license. 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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