Manage Compute Engine instances and instance templates. Configure managed instance groups and preemptible VMs. Use when deploying compute resources on GCP.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill gcp-compute
Deploy, manage, and scale Compute Engine virtual machines on Google Cloud Platform.
gcloud) installed and authenticatedroles/compute.admin or scoped roles for instance managementgcloud auth list
gcloud config set project $PROJECT_ID
gcloud services enable compute.googleapis.com
| Family | Example | vCPUs | Memory | Use Case |
|--------|---------|-------|--------|----------|
| E2 | e2-micro | 0.25 | 1 GB | Dev/test, microservices |
| E2 | e2-medium | 1 | 4 GB | Light web servers |
| N2 | n2-standard-4 | 4 | 16 GB | General-purpose production |
| N2 | n2-highmem-8 | 8 | 64 GB | In-memory caches, databases |
| C2 | c2-standard-16 | 16 | 64 GB | Compute-intensive, HPC |
# List machine types available in a zone
gcloud compute machine-types list --zones=us-central1-a --filter="name~'e2-'"
# Create a custom machine type (6 vCPUs, 24 GB RAM)
gcloud compute instances create custom-vm \
--custom-cpu=6 --custom-memory=24GB \
--zone=us-central1-a \
--image-family=debian-12 --image-project=debian-cloud
# Production instance with shielded VM and startup script
gcloud compute instances create web-server \
--machine-type=e2-medium \
--zone=us-central1-a \
--image-family=debian-12 \
--image-project=debian-cloud \
--boot-disk-size=20GB \
--boot-disk-type=pd-balanced \
--tags=http-server,https-server \
--labels=env=production,team=backend \
--metadata=enable-oslogin=TRUE \
--shielded-secure-boot \
--shielded-vtpm \
--shielded-integrity-monitoring
# Instance with a startup script and service account
gcloud compute instances create app-server \
--machine-type=e2-standard-2 \
--zone=us-central1-a \
--image-family=ubuntu-2204-lts \
--image-project=ubuntu-os-cloud \
--boot-disk-size=50GB \
--metadata-from-file=startup-script=startup.sh \
--service-account=app-sa@${PROJECT_ID}.iam.gserviceaccount.com \
--scopes=cloud-platform
# Instance with an additional data disk
gcloud compute instances create db-server \
--machine-type=n2-highmem-4 \
--zone=us-central1-a \
--image-family=debian-12 --image-project=debian-cloud \
--boot-disk-size=20GB \
--create-disk=name=data-disk,size=200GB,type=pd-ssd,auto-delete=no
#!/bin/bash
# startup.sh - runs on first boot and every reboot
set -euo pipefail
apt-get update && apt-get install -y nginx
systemctl enable nginx && systemctl start nginx
curl -X PUT -H "Metadata-Flavor: Google" \
"http://metadata.google.internal/computeMetadata/v1/instance/guest-attributes/startup/status" \
-d "complete"
# Create an instance template
gcloud compute instance-templates create web-template \
--machine-type=e2-medium \
--image-family=debian-12 --image-project=debian-cloud \
--boot-disk-size=20GB --tags=http-server \
--metadata-from-file=startup-script=startup.sh
# Create a regional managed instance group (MIG) with health check
gcloud compute health-checks create http http-health-check \
--port=80 --request-path=/healthz \
--check-interval=10s --timeout=5s \
--healthy-threshold=2 --unhealthy-threshold=3
gcloud compute instance-groups managed create web-mig \
--template=web-template --size=3 \
--region=us-central1 \
--health-check=http-health-check --initial-delay=120
# Configure autoscaling
gcloud compute instance-groups managed set-autoscaling web-mig \
--region=us-central1 \
--min-num-replicas=2 --max-num-replicas=10 \
--target-cpu-utilization=0.65 --cool-down-period=90
# Rolling update to a new template
gcloud compute instance-groups managed rolling-action start-update web-mig \
--version=template=web-template-v2 \
--region=us-central1 --max-surge=3 --max-unavailable=0
# Spot VM (recommended over legacy preemptible)
gcloud compute instances create spot-worker \
--machine-type=n2-standard-8 \
--zone=us-central1-a \
--image-family=debian-12 --image-project=debian-cloud \
--provisioning-model=SPOT \
--instance-termination-action=STOP
# Spot instance template for batch MIG
gcloud compute instance-templates create batch-template \
--machine-type=n2-standard-4 \
--image-family=debian-12 --image-project=debian-cloud \
--provisioning-model=SPOT \
--instance-termination-action=DELETE
# Create a snapshot
gcloud compute disks snapshot web-server \
--zone=us-central1-a \
--snapshot-names=web-server-snap-$(date +%Y%m%d)
# Scheduled snapshot policy
gcloud compute resource-policies create snapshot-schedule daily-backup \
--region=us-central1 --max-retention-days=14 \
--daily-schedule --start-time=03:00
gcloud compute disks add-resource-policies web-server \
--zone=us-central1-a --resource-policies=daily-backup
# Create a custom image from an instance
gcloud compute instances stop web-server --zone=us-central1-a
gcloud compute images create web-golden-image \
--source-disk=web-server --source-disk-zone=us-central1-a \
--family=web-server --labels=version=v1
resource "google_compute_instance" "web" {
name = "web-server"
machine_type = "e2-medium"
zone = "us-central1-a"
tags = ["http-server", "https-server"]
boot_disk {
initialize_params {
image = "debian-cloud/debian-12"
size = 20
type = "pd-balanced"
}
}
network_interface {
subnetwork = google_compute_subnetwork.main.id
access_config {}
}
metadata_startup_script = file("${path.module}/startup.sh")
service_account {
email = google_service_account.app.email
scopes = ["cloud-platform"]
}
shielded_instance_config {
enable_secure_boot = true
enable_vtpm = true
enable_integrity_monitoring = true
}
}
resource "google_compute_instance_template" "web" {
name_prefix = "web-"
machine_type = "e2-medium"
disk {
source_image = "debian-cloud/debian-12"
auto_delete = true
boot = true
disk_size_gb = 20
}
network_interface {
subnetwork = google_compute_subnetwork.main.id
}
lifecycle { create_before_destroy = true }
}
resource "google_compute_region_instance_group_manager" "web" {
name = "web-mig"
base_instance_name = "web"
region = "us-central1"
version {
instance_template = google_compute_instance_template.web.id
}
target_size = 3
named_port { name = "http"; port = 80 }
auto_healing_policies {
health_check = google_compute_health_check.http.id
initial_delay_sec = 120
}
}
resource "google_compute_region_autoscaler" "web" {
name = "web-autoscaler"
region = "us-central1"
target = google_compute_region_instance_group_manager.web.id
autoscaling_policy {
min_replicas = 2
max_replicas = 10
cooldown_period = 90
cpu_utilization { target = 0.65 }
}
}
# SSH into an instance
gcloud compute ssh web-server --zone=us-central1-a
# List all instances with status
gcloud compute instances list \
--format="table(name,zone,status,machineType.basename())"
# Stop / start / resize
gcloud compute instances stop web-server --zone=us-central1-a
gcloud compute instances set-machine-type web-server \
--machine-type=e2-standard-4 --zone=us-central1-a
gcloud compute instances start web-server --zone=us-central1-a
# View serial port output (debug startup scripts)
gcloud compute instances get-serial-port-output web-server --zone=us-central1-a
| Symptom | Cause | Fix |
|---------|-------|-----|
| Instance stuck in STAGING | Quota exceeded or resource unavailable | Check quota with gcloud compute project-info describe; try another zone |
| Startup script not running | Syntax errors or wrong metadata key | Check serial output; ensure key is startup-script not startup_script |
| Cannot SSH | Firewall blocks port 22 or OS Login misconfigured | Add firewall rule for tcp:22; verify enable-oslogin metadata |
| Preempted too often | Zone resource pressure | Use Spot VM with STOP action; spread across zones in a MIG |
| Disk out of space | Boot disk too small | Use gcloud compute disks resize; enable --storage-auto-increase for data disks |
| MIG not healing | Health check misconfigured or initial delay too short | Verify health check path returns 200; increase --initial-delay |
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.
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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).
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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 bagelhole/gcp-compute 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.
The instructions reference apt.
Without those the skill loads but fails at the first command.