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Terraform Infrastructure as Code

Terraform is the de-facto standard for declarative infrastructure provisioning. This skill covers the complete lifecycle - project setup, module design, remote state management, multi-environment strategy, and keeping real infrastructure aligned with declared configuration. Designed for engineers who know basic Terraform and need opinionated guidance on structure, safety, and production practices.

When to use this skill

Trigger this skill when the user:

• Writes or reviews Terraform HCL for any cloud provider (AWS, GCP, Azure)
• Designs reusable Terraform modules or a module registry structure
• Sets up or migrates remote state backends (S3, GCS, Terraform Cloud)
• Manages multiple environments (dev/staging/prod) with Terraform
• Diagnoses drift between actual infrastructure and Terraform state
• Runs or interprets

  terraform plan

  ,

  terraform apply

  , or

  terraform import

• Handles state operations:

  state mv

  ,

  state rm

  ,

  taint

  ,

  untaint

Do NOT trigger this skill for:

• Kubernetes manifest authoring (use a kubernetes/helm skill instead)
• Application-level configuration management (Ansible, Chef, Puppet)

Key principles

1. Declarative over imperative - Describe the desired end state, not the steps to get there. If you find yourself writing

   null_resource

   with provisioners to run shell scripts, stop and ask whether the provider has a proper resource for this.

2. Modules for every reusable pattern - Any configuration block you copy between environments or projects is a module waiting to be written. Extract early; the cost of refactoring into a module grows with usage.

3. Remote state always - Local state is only acceptable for throwaway experiments. Production state lives in a versioned, locked backend (S3 + DynamoDB, GCS, or Terraform Cloud) from day one. State is your source of truth.

4. Plan before apply, in CI -

   terraform apply

   without a reviewed plan is the infrastructure equivalent of deploying untested code. Always run

   terraform plan -out=tfplan

   and review the diff before applying. Automate this in CI pipelines.

5. Least privilege for providers - The IAM role or service account Terraform uses must have only the permissions needed for that specific configuration. Never use AdministratorAccess or Owner roles for provider credentials.

Core concepts

Providers - Plugins that translate HCL into API calls for a cloud or service. Always pin provider versions in

required_providers

Resources - The fundamental unit. Each resource block declares one infrastructure object (

aws_vpc

google_container_cluster

Data sources - Read-only lookups of existing infrastructure not managed by this configuration. Use

data

Modules - Containers for multiple resources that are used together. A module is a directory with

.tf

variable

output

State - A JSON file that maps declared resources to real infrastructure objects. Terraform uses state to calculate diffs. Never edit state manually - use

terraform state

Workspaces - Named state instances within a single backend configuration. Useful for short-lived feature environments; not recommended for long-lived environment separation (use separate root modules instead).

Backends - Configuration for where and how state is stored and locked. Locking prevents concurrent applies from corrupting state.

Common tasks

Set up a project with S3 backend

Structure every Terraform project with these three foundational files before writing any resources.

versions.tf

terraform {
  required_version = ">= 1.6.0, < 2.0.0"

  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }

  backend "s3" {
    bucket         = "my-org-terraform-state"
    key            = "services/my-service/terraform.tfstate"
    region         = "us-east-1"
    encrypt        = true
    dynamodb_table = "terraform-state-lock"
  }
}

providers.tf

provider "aws" {
  region = var.aws_region

  default_tags {
    tags = {
      ManagedBy   = "terraform"
      Environment = var.environment
      Service     = var.service_name
    }
  }
}

variables.tf

variable "aws_region" {
  description = "AWS region to deploy into"
  type        = string
  default     = "us-east-1"
}

variable "environment" {
  description = "Deployment environment (dev, staging, prod)"
  type        = string

  validation {
    condition     = contains(["dev", "staging", "prod"], var.environment)
    error_message = "environment must be one of: dev, staging, prod"
  }
}

variable "service_name" {
  description = "Name of the service owning this infrastructure"
  type        = string
}

Create the S3 bucket and DynamoDB table for the backend manually (or with a separate bootstrap Terraform config) before running

terraform init

. You cannot manage the state backend with the same configuration that uses it.

Write a reusable module

A module is a directory with

main.tf

variables.tf

outputs.tf

variables.tf

outputs.tf

Calling a module from a root configuration:

module "vpc" {
  source = "../../modules/vpc"

  name                 = "my-service-${var.environment}"
  availability_zones   = ["us-east-1a", "us-east-1b", "us-east-1c"]
  public_subnet_cidrs  = ["10.0.1.0/24", "10.0.2.0/24", "10.0.3.0/24"]
  private_subnet_cidrs = ["10.0.11.0/24", "10.0.12.0/24", "10.0.13.0/24"]
}

See

references/module-patterns.md

Manage environments with workspaces

Workspaces share a single backend and configuration. Use them for ephemeral feature environments; prefer separate state files (separate

key

# Create and switch to a feature workspace
terraform workspace new feature-xyz
terraform workspace select feature-xyz

# Reference workspace name in configuration to vary resource names/sizes
resource "aws_instance" "app" {
  instance_type = terraform.workspace == "prod" ? "t3.large" : "t3.micro"
  tags          = { Environment = terraform.workspace }
}

# Clean up the workspace when done
terraform workspace select default
terraform destroy
terraform workspace delete feature-xyz

For prod/staging: use separate backend

key

paths or separate AWS accounts with separate root modules. Workspaces with a single state key per environment mean a bad apply in one workspace can corrupt state for others.

Import existing resources into state

When infrastructure was created outside Terraform and you need to manage it.

# Terraform 1.5+: use import blocks (preferred, reviewable in plan)
# Add this to your .tf file temporarily:
import {
  to = aws_s3_bucket.my_bucket
  id = "my-existing-bucket-name"
}

# Run plan to preview what will be generated
terraform plan -generate-config-out=generated.tf

# Review generated.tf, copy the resource block into your main config, remove
# the import block, then apply
terraform apply

For older Terraform versions (pre-1.5), use the CLI:

terraform import aws_s3_bucket.my_bucket my-existing-bucket-name

After importing, always run

terraform plan

to verify zero diff before continuing. A non-empty plan after import means your HCL does not match the real resource - fix the HCL, do not apply the diff blindly.

Handle state operations safely

State operations modify which resources Terraform tracks. Always take a state backup first.

# Backup state before any manual operation
terraform state pull > backup-$(date +%Y%m%d-%H%M%S).tfstate

# Rename a resource (e.g., after refactoring module structure)
terraform state mv aws_instance.old_name aws_instance.new_name

# Move a resource into a module
terraform state mv aws_s3_bucket.logs module.logging.aws_s3_bucket.logs

# Remove a resource from state without destroying it
# (when you want Terraform to stop managing it)
terraform state rm aws_instance.temporary

# Mark a resource for replacement on next apply
# (forces destroy + recreate even if config unchanged)
terraform taint aws_instance.app
# Terraform 0.15.2+ preferred syntax:
terraform apply -replace="aws_instance.app"

state rm

does NOT destroy the real infrastructure. The resource will simply become unmanaged. If you want it gone, destroy first, then remove from state.

Detect and fix drift

Drift occurs when real infrastructure diverges from Terraform state (e.g., manual console changes, external automation).

# Step 1: Refresh state against real infrastructure
terraform refresh

# Step 2: Run plan to see what Terraform would change to correct drift
terraform plan

# Step 3a: If drift is unintentional - apply to correct it
terraform apply

# Step 3b: If drift is intentional - update HCL to match reality,
# then verify plan shows no changes
terraform plan  # should output: "No changes. Infrastructure is up-to-date."

# For a targeted drift check on one resource:
terraform plan -target=aws_security_group.app

In CI, detect drift on a schedule:

# Run as a daily cron job - alert if exit code is 2 (changes detected)
terraform plan -detailed-exitcode
# Exit 0: no diff  |  Exit 1: error  |  Exit 2: diff detected

Use data sources and dynamic blocks

Data sources look up existing infrastructure without managing it:

# Look up the latest Amazon Linux 2 AMI - never hardcode AMI IDs
data "aws_ami" "amazon_linux" {
  most_recent = true
  owners      = ["amazon"]

  filter {
    name   = "name"
    values = ["amzn2-ami-hvm-*-x86_64-gp2"]
  }
}

resource "aws_instance" "app" {
  ami           = data.aws_ami.amazon_linux.id
  instance_type = var.instance_type
}

# Reference an existing VPC not managed by this config
data "aws_vpc" "shared" {
  tags = { Name = "shared-services-vpc" }
}

Dynamic blocks eliminate repetitive nested blocks:

variable "ingress_rules" {
  type = list(object({
    from_port   = number
    to_port     = number
    protocol    = string
    cidr_blocks = list(string)
  }))
}

resource "aws_security_group" "app" {
  name   = "app-sg"
  vpc_id = data.aws_vpc.shared.id

  dynamic "ingress" {
    for_each = var.ingress_rules
    content {
      from_port   = ingress.value.from_port
      to_port     = ingress.value.to_port
      protocol    = ingress.value.protocol
      cidr_blocks = ingress.value.cidr_blocks
    }
  }
}

Error handling

Error acquiring the state lock

terraform force-unlock <LOCK_ID>

Error: inconsistent result after apply

depends_on

Error: Resource already exists

terraform import

Error refreshing state: AccessDenied

Describe*

Get*

List*

Error: Cycle detected

A depends on B, B depends on A

depends_on

Plan shows replacement for unchanged resource

terraform refresh

Gotchas

1. You cannot manage the S3 backend bucket with the config that uses it - The backend must exist before

   terraform init

   runs. Bootstrap the state bucket and DynamoDB lock table with a separate configuration (or manually). Attempting to create both in the same root module causes a chicken-and-egg failure.

2. terraform destroy

   in a workspace also destroys shared resources - If your module references shared infrastructure (e.g., a VPC created in another root module), and you run

   destroy

   in a feature workspace, any shared resources included via

   data

   sources will not be destroyed - but any created by this config will. Audit what belongs to the workspace before destroying.

3. Unpinned provider versions cause silent breakage on upgrades - Without

   version = "~> 5.0"

   in

   required_providers

   , a provider major version bump in the registry can change resource schemas and break existing configs on the next

   terraform init

   . Always pin providers; update versions deliberately.

4. terraform state rm

   does not destroy the real resource - It only removes Terraform's tracking entry. The resource continues running and accumulating cost. If you want the resource gone, run

   terraform destroy -target=<resource>

   first, then remove from state if needed.

5. Workspaces share a backend - a corrupted state affects all workspaces - Using workspaces with separate state keys in the same S3 bucket means a misconfigured

   state mv

   or force-unlock at the wrong key can corrupt a different environment's state. Prefer separate AWS accounts or separate state buckets for prod/staging separation.

References

For detailed patterns and implementation guidance, read the relevant file from the

references/

• references/module-patterns.md

  - module composition, factory pattern, versioning, monorepo layout

Only load a references file if the current task requires it - they are detailed and will consume context.

Companion check

On first activation of this skill in a conversation: check which companion skills are installed by running

ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null

. Compare the results against the

recommended_skills

field in this file's frontmatter. For any that are missing, mention them once and offer to install:

npx skills add AbsolutelySkilled/AbsolutelySkilled --skill <name>

Skip entirely if

recommended_skills

is empty or all companions are already installed.