Use this skill when writing Terraform configurations, managing infrastructure as code, creating reusable modules, handling state backends, or detecting drift. Triggers on Terraform, HCL, infrastructure as code, IaC, providers, modules, state management, terraform plan, terraform apply, drift detection, and any task requiring declarative infrastructure provisioning.
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Use this skill when writing Terraform configurations, managing infrastructure as code, creating reusable modules, handling state backends, or detecting drift. Triggers on Terraform, HCL, infrastructure as code, IaC, providers, modules, state management, terraform plan, terraform apply, drift detection, and any task requiring declarative infrastructure provisioning.
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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)
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.
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.
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.
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.
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. Unpinned providers break
on provider releases.
Resources - The fundamental unit. Each resource block declares one
infrastructure object (aws_vpc, google_container_cluster, etc.).
Data sources - Read-only lookups of existing infrastructure not managed by
this configuration. Use data blocks to reference shared resources (AMIs,
existing VPCs, DNS zones) without importing them into state.
Modules - Containers for multiple resources that are used together. A module
is a directory with .tf files. Modules accept variable inputs and expose
output values to callers.
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 commands.
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 - Pin everything. Unpinned versions cause silent breakage.
variables.tf - Declare all inputs with descriptions and sensible defaults.
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, and outputs.tf.
Modules should express one cohesive infrastructure concern. All inputs are
declared with descriptions in variables.tf; all outputs expose only what
callers need in outputs.tf.
See references/module-patterns.md for complete module templates, versioning, and monorepo layout.
Manage environments with workspaces
Workspaces share a single backend and configuration. Use them for ephemeral
feature environments; prefer separate state files (separate key paths) for
permanent environments like staging and prod.
# 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:
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" }
}
Another apply is running, or a previous run crashed without releasing the lock
Wait for concurrent run; if stale: terraform force-unlock <LOCK_ID> (verify no concurrent apply first)
Error: inconsistent result after apply
Provider returned a different value than what was planned (often eventual consistency)
Add depends_on or increase retry logic; file a provider bug if persistent
Error: Resource already exists
Trying to create a resource that exists but is not in state
Use terraform import to bring it under management before applying
Error refreshing state: AccessDenied
Provider credentials lack read permissions on existing resources
Expand IAM policy to include Describe* / Get* / List* for affected services
Error: Cycle detected
Circular dependency between resources (A depends on B, B depends on A)
Break the cycle with depends_on or restructure - often caused by security group self-references
Plan shows replacement for unchanged resource
A computed attribute (e.g., an ARN or auto-generated field) changed externally
Run terraform refresh then re-plan; if persistent, check for provider version changes
Gotchas
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.
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.
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.
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.
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/ folder:
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:
