terraform-gcp
terraform-gcp is an engineering AI skill with a core value of Provision GCP infrastructure with Terraform. It
helps developers solve real-world problems in the engineering domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Provision GCP infrastructure with Terraform. Configure providers and deploy Google Cloud resources. Use when implementing IaC for GCP.
Quick Facts
mkdir -p ./skills/terraform-gcp && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/terraform-gcp/SKILL.md -o ./skills/terraform-gcp/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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 versionProvider 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 tfplanresource "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 🎯 Best For
- Claude users
- AI users
💡 Use Cases
- Using terraform-gcp in daily workflow
- Automating repetitive engineering tasks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply terraform-gcp to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
How do I install terraform-gcp?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/terraform-gcp/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.