Provision GCP infrastructure with Terraform. Configure providers and deploy Google Cloud resources. Use when implementing IaC for GCP.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill terraform-gcp
Provision and manage Google Cloud Platform infrastructure using Terraform with the hashicorp/google provider.
gcloud auth application-default login # local dev
export GOOGLE_APPLICATION_CREDENTIALS="sa.json" # CI/CD
terraform version
# versions.tf
terraform {
required_version = ">= 1.5"
required_providers {
google = { source = "hashicorp/google"; version = "~> 5.0" }
google-beta = { source = "hashicorp/google-beta"; version = "~> 5.0" }
}
backend "gcs" { bucket = "my-project-tf-state"; prefix = "terraform/state" }
}
provider "google" { project = var.project_id; region = var.region }
provider "google-beta" { project = var.project_id; region = var.region }
# variables.tf
variable "project_id" { type = string }
variable "region" { type = string; default = "us-central1" }
variable "environment" {
type = string
validation {
condition = contains(["dev", "staging", "production"], var.environment)
error_message = "Must be dev, staging, or production."
}
}
gcloud storage buckets create gs://my-project-tf-state \
--location=us-central1 --uniform-bucket-level-access --public-access-prevention
gcloud storage buckets update gs://my-project-tf-state --versioning
terraform init
terraform plan -var="project_id=my-project" -var="environment=production" -out=tfplan
terraform apply tfplan
resource "google_project_service" "apis" {
for_each = toset([
"compute.googleapis.com", "container.googleapis.com",
"sqladmin.googleapis.com", "servicenetworking.googleapis.com",
"cloudfunctions.googleapis.com", "run.googleapis.com",
"secretmanager.googleapis.com", "artifactregistry.googleapis.com",
])
project = var.project_id
service = each.value
disable_dependent_services = false
disable_on_destroy = false
}
# modules/networking/main.tf
resource "google_compute_network" "vpc" {
name = "${var.environment}-vpc"
auto_create_subnetworks = false
routing_mode = "REGIONAL"
}
resource "google_compute_subnetwork" "main" {
name = "${var.environment}-main-subnet"
ip_cidr_range = var.subnet_cidr
region = var.region
network = google_compute_network.vpc.id
private_ip_google_access = true
log_config { aggregation_interval = "INTERVAL_5_SEC"; flow_sampling = 0.5 }
}
resource "google_compute_subnetwork" "gke" {
name = "${var.environment}-gke-subnet"
ip_cidr_range = var.gke_subnet_cidr
region = var.region
network = google_compute_network.vpc.id
private_ip_google_access = true
secondary_ip_range { range_name = "pods"; ip_cidr_range = var.pods_cidr }
secondary_ip_range { range_name = "services"; ip_cidr_range = var.services_cidr }
}
resource "google_compute_firewall" "allow_iap" {
name = "${var.environment}-allow-iap"
network = google_compute_network.vpc.name
allow { protocol = "tcp"; ports = ["22", "3389"] }
source_ranges = ["35.235.240.0/20"]
}
resource "google_compute_router" "router" {
name = "${var.environment}-router"
region = var.region
network = google_compute_network.vpc.id
}
resource "google_compute_router_nat" "nat" {
name = "${var.environment}-nat"
router = google_compute_router.router.name
region = var.region
nat_ip_allocate_option = "AUTO_ONLY"
source_subnetwork_ip_ranges_to_nat = "ALL_SUBNETWORKS_ALL_IP_RANGES"
log_config { enable = true; filter = "ERRORS_ONLY" }
}
output "vpc_id" { value = google_compute_network.vpc.id }
output "gke_subnet_id" { value = google_compute_subnetwork.gke.id }
# modules/gke/main.tf
resource "google_container_cluster" "primary" {
name = "${var.environment}-cluster"
location = var.region
release_channel { channel = var.release_channel }
workload_identity_config { workload_pool = "${var.project_id}.svc.id.goog" }
network = var.vpc_name
subnetwork = var.gke_subnet_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 = var.region
initial_node_count = var.initial_node_count
autoscaling { min_node_count = var.min_nodes; max_node_count = var.max_nodes }
management { auto_repair = true; auto_upgrade = true }
node_config {
machine_type = var.machine_type
disk_size_gb = 100
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" }
}
}
output "cluster_name" { value = google_container_cluster.primary.name }
output "cluster_endpoint" { value = google_container_cluster.primary.endpoint; sensitive = true }
# modules/cloud-sql/main.tf
resource "google_sql_database_instance" "main" {
name = "${var.environment}-db"
database_version = var.database_version
region = var.region
settings {
tier = var.tier
availability_type = var.environment == "production" ? "REGIONAL" : "ZONAL"
disk_type = "PD_SSD"
disk_size = var.disk_size
disk_autoresize = true
backup_configuration {
enabled = true
start_time = "02:00"
point_in_time_recovery_enabled = true
backup_retention_settings { retained_backups = var.environment == "production" ? 30 : 7 }
}
ip_configuration {
ipv4_enabled = false
private_network = var.vpc_id
require_ssl = true
}
database_flags { name = "max_connections"; value = var.max_connections }
}
deletion_protection = var.environment == "production"
depends_on = [var.private_vpc_connection]
}
resource "google_sql_database" "app" { name = var.database_name; instance = google_sql_database_instance.main.name }
resource "google_sql_user" "app" { name = var.db_user; instance = google_sql_database_instance.main.name; password = random_password.db.result }
resource "random_password" "db" { length = 32; special = true }
output "connection_name" { value = google_sql_database_instance.main.connection_name }
output "private_ip" { value = google_sql_database_instance.main.private_ip_address }
resource "google_service_account" "gke_nodes" {
account_id = "${var.environment}-gke-nodes"
display_name = "GKE Node Pool SA"
}
resource "google_project_iam_member" "gke_nodes" {
for_each = toset([
"roles/logging.logWriter", "roles/monitoring.metricWriter",
"roles/artifactregistry.reader",
])
project = var.project_id
role = each.value
member = "serviceAccount:${google_service_account.gke_nodes.email}"
}
resource "google_service_account" "app" {
account_id = "${var.environment}-app"
display_name = "Application SA"
}
resource "google_service_account_iam_member" "workload_identity" {
service_account_id = google_service_account.app.name
role = "roles/iam.workloadIdentityUser"
member = "serviceAccount:${var.project_id}.svc.id.goog[myapp/app-ksa]"
}
module "networking" {
source = "./modules/networking"
project_id = var.project_id
environment = var.environment
region = var.region
}
module "gke" {
source = "./modules/gke"
project_id = var.project_id
environment = var.environment
region = var.region
vpc_name = module.networking.vpc_id
gke_subnet_name = module.networking.gke_subnet_id
node_sa_email = google_service_account.gke_nodes.email
depends_on = [module.networking]
}
module "database" {
source = "./modules/cloud-sql"
project_id = var.project_id
environment = var.environment
region = var.region
vpc_id = module.networking.vpc_id
database_version = "POSTGRES_16"
tier = "db-custom-4-16384"
private_vpc_connection = module.networking.private_vpc_connection
depends_on = [module.networking]
}
# environments/production.tfvars
project_id = "my-company-prod"
environment = "production"
region = "us-central1"
terraform plan -var-file=environments/production.tfvars -out=tfplan
terraform apply tfplan
terraform init -input=false
terraform validate && terraform fmt -check
terraform plan -var-file=environments/${ENV}.tfvars -out=tfplan -input=false
terraform apply -input=false tfplan
# Import existing resources
terraform import google_compute_network.vpc projects/${PROJECT_ID}/global/networks/prod-vpc
# State management
terraform state list
terraform state mv google_compute_instance.old google_compute_instance.new
| Symptom | Cause | Fix |
|---------|-------|-----|
| Error 403: Access Not Configured | API not enabled | Add API to google_project_service resources |
| Error acquiring the state lock | Concurrent run or stale lock | Run terraform force-unlock LOCK_ID after verification |
| Resource already exists | Created outside Terraform | Import with terraform import |
| Quota exceeded | Project quota too low | Request increase in Cloud Console > Quotas |
| Plan shows destroy/recreate | Changed force-new attribute | Use moved blocks or terraform state mv |
| Backend initialization required | Changed backend config | Run terraform init -migrate-state |
| Cycle in resource graph | Circular references | Refactor with data sources; split applies |
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.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
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).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
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/terraform-gcp from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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same name cannot sit side by side — one of them will be ignored.