Agent skill

GCP Gke

by sickn33 in sickn33/agentic-awesome-skills

Deploy and manage Google Kubernetes Engine clusters. An agent skill from sickn33/agentic-awesome-skills.

MITAuto-check passedDevOps & Cloud

Install GCP Gke

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill gcp-gke -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills gcp-gke --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gcp-gke .claude/skills/gcp-gke && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gcp-gke
GitHub stars
47k
Used in
2 other repos
Token cost
~2.5k tokens
SKILL.md length
320 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Deploy and manage Google Kubernetes Engine clusters. An agent skill from sickn33/agentic-awesome-skills.

  • Running Kubernetes on GCP
  • SKILL.md covers When to Use, Prerequisites, Standard vs Autopilot and Create a Standard Cluster, plus 10 more sections
  • Calls gcloud and kubectl; reaches googleapis.com
  • Tasks that involve Container orchestration

What it does

GCP Gke is an agent skill from sickn33/agentic-awesome-skills. Deploy and manage Google Kubernetes Engine clusters. Configure node pools, networking, and workload identity. Use when running Kubernetes on GCP.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

It sits in DevOps & Cloud, covering Container orchestration. It works with Google Kubernetes Engine, Google Cloud and Kubernetes. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Running Kubernetes on GCP
  • Tasks that involve Container orchestration

Example prompts

  • “/gcp-gke”

Requirements

  • Docker
  • Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

What it can do on your machine

Read from SKILL.md and the folder at commit 1c7bdea. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gcloud
    • kubectl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • googleapis.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

    From compatibility in the SKILL.md frontmatter.

Context cost

GCP Gke loads about 2.5k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 320 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 1c7bdea, republished under its MIT licence (© sickn33). 320 words, ~2,510 tokens.

Download SKILL.mdSave it as .claude/skills/gcp-gke/SKILL.md (or your agent's skills folder).
name
gcp-gke
description
Deploy and manage Google Kubernetes Engine clusters. Configure node pools, networking, and workload identity. Use when running Kubernetes on GCP.
compatibility
Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.
category
devops
risk
critical
source
https://github.com/BagelHole/DevOps-Security-Agent-Skills
source_repo
BagelHole/DevOps-Security-Agent-Skills
source_type
community
date_added
2026-09-20
license
MIT
license_source
https://github.com/BagelHole/DevOps-Security-Agent-Skills/blob/main/LICENSE
metadata.author
devops-skills
metadata.version
1.0

Google Kubernetes Engine (GKE)

Deploy, operate, and scale managed Kubernetes clusters on Google Cloud Platform.

When to Use

  • Running containerized microservices at scale with automatic scaling and healing
  • Workloads requiring fine-grained orchestration, service mesh, or custom scheduling
  • Teams already invested in Kubernetes tooling (Helm, Argo CD, Flux)
  • When Cloud Run's request-based model does not fit (long-running, stateful workloads)

Prerequisites

  • Google Cloud SDK (gcloud) and kubectl installed
  • APIs enabled: Kubernetes Engine, Compute Engine
  • IAM role roles/container.admin for cluster management
bash
gcloud services enable container.googleapis.com compute.googleapis.com
gcloud components install kubectl

Standard vs Autopilot

FeatureStandardAutopilot
Node managementYou manage node poolsGoogle manages nodes
PricingPay per node (VM)Pay per pod resource request
GPU/TPUFull supportSupported (with limits)
DaemonSetsAllowedRestricted
Best forFull control, specialized HWHands-off, cost-optimized

Create a Standard Cluster

bash
gcloud container clusters create prod-cluster \
  --region=us-central1 --num-nodes=2 \
  --machine-type=e2-standard-4 --disk-size=100 \
  --enable-autoscaling --min-nodes=1 --max-nodes=5 \
  --enable-autorepair --enable-autoupgrade \
  --release-channel=regular \
  --workload-pool=${PROJECT_ID}.svc.id.goog \
  --enable-ip-alias --enable-network-policy \
  --enable-shielded-nodes \
  --logging=SYSTEM,WORKLOAD --monitoring=SYSTEM,WORKLOAD \
  --labels=env=production,team=platform

gcloud container clusters get-credentials prod-cluster --region=us-central1

Create an Autopilot Cluster

bash
gcloud container clusters create-auto autopilot-prod \
  --region=us-central1 --release-channel=regular \
  --workload-pool=${PROJECT_ID}.svc.id.goog \
  --network=my-vpc --subnetwork=gke-subnet

Node Pools

bash
# High-memory pool with taint
gcloud container node-pools create highmem-pool \
  --cluster=prod-cluster --region=us-central1 \
  --machine-type=n2-highmem-8 --disk-size=200 --disk-type=pd-ssd \
  --num-nodes=1 --enable-autoscaling --min-nodes=0 --max-nodes=4 \
  --node-labels=workload=memory-intensive \
  --node-taints=dedicated=highmem:NoSchedule

# GPU pool
gcloud container node-pools create gpu-pool \
  --cluster=prod-cluster --region=us-central1 \
  --machine-type=n1-standard-8 \
  --accelerator=type=nvidia-tesla-t4,count=1 \
  --num-nodes=0 --enable-autoscaling --min-nodes=0 --max-nodes=4 \
  --node-taints=nvidia.com/gpu=present:NoSchedule

# Spot pool for batch workloads
gcloud container node-pools create spot-pool \
  --cluster=prod-cluster --region=us-central1 \
  --machine-type=e2-standard-4 --spot \
  --num-nodes=0 --enable-autoscaling --min-nodes=0 --max-nodes=20 \
  --node-taints=cloud.google.com/gke-spot=true:NoSchedule

Workload Identity

bash
# Create GSA and grant permissions
gcloud iam service-accounts create app-gsa
gcloud projects add-iam-policy-binding ${PROJECT_ID} \
  --member="serviceAccount:app-gsa@${PROJECT_ID}.iam.gserviceaccount.com" \
  --role="roles/storage.objectViewer"

# Create KSA and bind to GSA
kubectl create namespace myapp
kubectl create serviceaccount app-ksa --namespace=myapp
gcloud iam service-accounts add-iam-policy-binding \
  app-gsa@${PROJECT_ID}.iam.gserviceaccount.com \
  --role=roles/iam.workloadIdentityUser \
  --member="serviceAccount:${PROJECT_ID}.svc.id.goog[myapp/app-ksa]"
kubectl annotate serviceaccount app-ksa --namespace=myapp \
  iam.gke.io/gcp-service-account=app-gsa@${PROJECT_ID}.iam.gserviceaccount.com

Deploying Workloads

yaml
# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-app
  namespace: myapp
spec:
  replicas: 3
  selector:
    matchLabels: { app: web-app }
  template:
    metadata:
      labels: { app: web-app }
    spec:
      serviceAccountName: app-ksa
      containers:
      - name: web
        image: us-central1-docker.pkg.dev/PROJECT_ID/repo/web-app:v1.2.0
        ports: [{ containerPort: 8080 }]
        resources:
          requests: { cpu: 250m, memory: 512Mi }
          limits: { cpu: 500m, memory: 1Gi }
        readinessProbe:
          httpGet: { path: /healthz, port: 8080 }
          initialDelaySeconds: 5
        livenessProbe:
          httpGet: { path: /healthz, port: 8080 }
          initialDelaySeconds: 15
      topologySpreadConstraints:
      - maxSkew: 1
        topologyKey: topology.kubernetes.io/zone
        whenUnsatisfiable: DoNotSchedule
        labelSelector:
          matchLabels: { app: web-app }
---
apiVersion: v1
kind: Service
metadata: { name: web-app, namespace: myapp }
spec:
  selector: { app: web-app }
  ports: [{ port: 80, targetPort: 8080 }]
  type: ClusterIP

Ingress with Managed SSL

yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: web-ingress
  namespace: myapp
  annotations:
    kubernetes.io/ingress.class: "gce"
    networking.gke.io/managed-certificates: "web-cert"
    kubernetes.io/ingress.global-static-ip-name: "web-static-ip"
spec:
  rules:
  - host: app.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service: { name: web-app, port: { number: 80 } }
---
apiVersion: networking.gke.io/v1
kind: ManagedCertificate
metadata: { name: web-cert, namespace: myapp }
spec:
  domains: [app.example.com]
bash
gcloud compute addresses create web-static-ip --global

Terraform Configuration

hcl
resource "google_container_cluster" "primary" {
  name     = "prod-cluster"
  location = "us-central1"

  release_channel { channel = "REGULAR" }
  workload_identity_config { workload_pool = "${var.project_id}.svc.id.goog" }

  network    = google_compute_network.vpc.name
  subnetwork = google_compute_subnetwork.gke.name

  ip_allocation_policy {
    cluster_secondary_range_name  = "pods"
    services_secondary_range_name = "services"
  }

  private_cluster_config {
    enable_private_nodes   = true
    master_ipv4_cidr_block = "172.16.0.0/28"
  }

  network_policy { enabled = true }
  logging_config { enable_components = ["SYSTEM_COMPONENTS", "WORKLOADS"] }
  monitoring_config {
    enable_components = ["SYSTEM_COMPONENTS", "WORKLOADS"]
    managed_prometheus { enabled = true }
  }

  remove_default_node_pool = true
  initial_node_count       = 1
}

resource "google_container_node_pool" "primary" {
  name     = "primary-pool"
  cluster  = google_container_cluster.primary.name
  location = "us-central1"

  initial_node_count = 2
  autoscaling { min_node_count = 1; max_node_count = 5 }
  management  { auto_repair = true; auto_upgrade = true }

  node_config {
    machine_type = "e2-standard-4"
    disk_size_gb = 100
    disk_type    = "pd-balanced"
    oauth_scopes = ["https://www.googleapis.com/auth/cloud-platform"]
    shielded_instance_config {
      enable_secure_boot          = true
      enable_integrity_monitoring = true
    }
    metadata = { disable-legacy-endpoints = "true" }
  }
}

resource "google_compute_subnetwork" "gke" {
  name          = "gke-subnet"
  ip_cidr_range = "10.0.0.0/20"
  region        = "us-central1"
  network       = google_compute_network.vpc.id

  secondary_ip_range { range_name = "pods";     ip_cidr_range = "10.4.0.0/14" }
  secondary_ip_range { range_name = "services"; ip_cidr_range = "10.8.0.0/20" }
}

Common Operations

bash
gcloud container clusters list
gcloud container clusters upgrade prod-cluster --region=us-central1 --master
kubectl top nodes && kubectl top pods --namespace=myapp
kubectl scale deployment web-app --replicas=5 --namespace=myapp
kubectl autoscale deployment web-app --namespace=myapp --min=3 --max=20 --cpu-percent=70
kubectl logs -f deployment/web-app --namespace=myapp --all-containers

Troubleshooting

SymptomCauseFix
Pods stuck in PendingNo nodes with enough resourcesCheck autoscaler; add larger node pool; verify resource requests
ImagePullBackOffWrong image path or missing AR accessVerify image URL; grant roles/artifactregistry.reader to node SA
Workload Identity wrong accountKSA annotation missingRe-annotate KSA; restart pods to pick up new token
Nodes NotReadyDisk/memory pressure or network issueRun kubectl describe node; check taints and conditions
Ingress returns 502Backend pods failing health checkVerify readiness probe; check NEG health in Console
Cluster create quota errorInsufficient regional CPU/IP quotaRequest quota increase in IAM & Admin > Quotas
Network policy not workingNot enabled on clusterRecreate with --enable-network-policy or use Dataplane V2
  • gcp-networking - VPC, firewall rules, and load balancers for GKE clusters
  • terraform-gcp - Provision GKE clusters with Infrastructure as Code
  • gcp-compute - When workloads are better suited for VMs than containers
  • gcp-cloud-sql - Connecting GKE pods to Cloud SQL via sidecar proxy

Limitations

  • Infrastructure commands can disrupt services: confirm target host/scope and have backups/snapshots before mutating state.
  • Docs-only import: upstream scripts and templates not bundled.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/gcp-gke of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 1c7bdea

Used in 2 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

GCP Gke next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

GCP Gke compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
GCP Gke this skillsickn33/agentic-awesome-skills47k2 repos~2.5kAutomated safety check: PassMIT
Devopsnicepkg/auto-company1952 repos~814Automated safety check: PassMIT
Kcli Cluster Deploymentkarmab/kcli653—~1.5kAutomated safety check: PassApache-2.0
Gke Cost Analysisgoogle/skills21k—~1.5kAutomated safety check: PassApache-2.0
Gke AI Troubleshooting Tpu Mxla Hanggoogle/skills21k—~3.7kAutomated safety check: PassApache-2.0
Gke AI Troubleshooting Tpu Performance Degradationgoogle/skills21k—~3.5kAutomated safety check: PassApache-2.0

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Categories

Questions about GCP Gke

What does GCP Gke do?

Deploy and manage Google Kubernetes Engine clusters. An agent skill from sickn33/agentic-awesome-skills. GCP Gke is an agent skill from sickn33/agentic-awesome-skills. Deploy and manage Google Kubernetes Engine clusters.

When should I use GCP Gke?

GCP Gke fits situations like: running Kubernetes on GCP; tasks that involve Container orchestration.

How do I install GCP Gke in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill gcp-gke -a claude-code`. Or copy the skill folder (skills/gcp-gke in sickn33/agentic-awesome-skills) into .claude/skills/gcp-gke in your project. Claude Code loads it when a task matches its description.

How do I install GCP Gke in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill gcp-gke -a codex`. Or copy the skill folder (skills/gcp-gke in sickn33/agentic-awesome-skills) into .agents/skills/gcp-gke in your project. Codex loads it when a task matches its description.

Can I use GCP Gke in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill gcp-gke -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gcp-gke, .gemini/skills/gcp-gke, .github/skills/gcp-gke and .opencode/skills/gcp-gke in your project.

What does GCP Gke need to run?

Going by SKILL.md and its folder, GCP Gke needs the command-line tools its instructions call (gcloud and kubectl). Our summary lists: Docker. Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled..

Does GCP Gke access the network?

SKILL.md names 1 domain. In commands or code: googleapis.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is GCP Gke safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does GCP Gke use?

GCP Gke is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does GCP Gke use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to GCP Gke?

Skills that share tags, products or a category with GCP Gke: Devops (nicepkg/auto-company, 195 stars), Kcli Cluster Deployment (karmab/kcli, 653 stars), Gke Cost Analysis (google/skills, 21k stars) and Gke AI Troubleshooting Tpu Mxla Hang (google/skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains GCP Gke?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,443 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 10, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.