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Terraform Gcp

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BagelHole
terraform-gcp

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

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill nameterraform-gcp
Stars
1.1K
Forks
161
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Terraform Gcp 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/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-gcp/terraform-gcp
  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/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/infrastructure/cloud-gcp/terraform-gcp .claude/skills/terraform-gcp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Terraform Gcp 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 Terraform Gcp 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 Terraform Gcp 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.

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
bash
gcloud auth application-default login          # local dev
export GOOGLE_APPLICATION_CREDENTIALS="sa.json" # CI/CD
terraform version

Provider Configuration

hcl
# 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 }
hcl
# 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

bash
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
hcl
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

hcl
# 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

hcl
# 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

hcl
# 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

hcl
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

hcl
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

hcl
# environments/production.tfvars
project_id  = "my-company-prod"
environment = "production"
region      = "us-central1"
bash
terraform plan -var-file=environments/production.tfvars -out=tfplan
terraform apply tfplan

CI/CD Integration

bash
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

SymptomCauseFix
Error 403: Access Not ConfiguredAPI not enabledAdd API to google_project_service resources
Error acquiring the state lockConcurrent run or stale lockRun terraform force-unlock LOCK_ID after verification
Resource already existsCreated outside TerraformImport with terraform import
Quota exceededProject quota too lowRequest increase in Cloud Console > Quotas
Plan shows destroy/recreateChanged force-new attributeUse moved blocks or terraform state mv
Backend initialization requiredChanged backend configRun terraform init -migrate-state
Cycle in resource graphCircular referencesRefactor with data sources; split applies

Related Skills

  • 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

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 Terraform Gcp AI skill do?

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

Why use Terraform Gcp on TypingMind?

Because you install it once and use it with any model. Terraform Gcp 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 Terraform Gcp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-gcp/terraform-gcp. 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 Terraform Gcp?

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 Terraform Gcp?

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

Is the Terraform Gcp AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT 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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