mcpbeat Sign in

Terraform GCP Agent Skill

Provision GCP infrastructure with Terraform. Configure providers and deploy Google Cloud resources. Use when implementing IaC for GCP.

4k tokens
context cost
the whole folder, loaded on every use
3
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
511
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill terraform-gcp

What comes with it

5 342 bytes besides the instruction
assets/vpc-module.tf
scripts/tf-init-gcp.sh

The instruction itself

14 sections, as written by the author

Terraform GCP

Provision and manage Google Cloud Platform infrastructure using Terraform with the hashicorp/google provider.

When to Use

  • Defining GCP infrastructure as code for repeatable, auditable deployments
  • Managing multi-environment setups (dev, staging, production) from a single codebase
  • Provisioning complex resource graphs (VPC + GKE + Cloud SQL + IAM) in one plan
  • Integrating infrastructure changes into CI/CD pipelines with plan/apply stages

Prerequisites

  • Terraform >= 1.5 installed
  • Google Cloud SDK or a service account key for CI
  • A GCP project with billing enabled
gcloud auth application-default login          # local dev
export GOOGLE_APPLICATION_CREDENTIALS="sa.json" # CI/CD
terraform version

Provider Configuration

# 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."
  }
}

Project Setup and State Bucket

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
}

Networking Module

# 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 }

GKE Cluster Module

# 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 }

Cloud SQL Module

# 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 }

IAM and Service Accounts

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]"
}

Root Module Composition

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]
}

Environment Configuration

# 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

CI/CD Integration

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

Troubleshooting

| 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 |

  • gcp-networking - VPC and firewall resources managed by Terraform
  • gcp-gke - GKE cluster provisioning with Terraform modules
  • gcp-cloud-sql - Cloud SQL instance management via Terraform
  • gcp-compute - Compute Engine resources defined in Terraform
  • gcp-cloud-functions - Serverless function deployment with Terraform

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

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.

13k tokens
Capacity
by microsoft
vendor ×3

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.

6k tokens scripts
Customize
by microsoft
vendor ×3

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).

8k tokens
Deploy Model
by microsoft
vendor ×3

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).

26k tokens scripts
Preset
by microsoft
vendor ×3

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).

9k tokens
Lamindb
by christophacham
×3

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.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

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.

17k tokens

How to use it

Copy the folder

Take bagelhole/terraform-gcp from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.