From aigroup-workflow
Implements infrastructure as code with Terraform across AWS, Azure, or GCP. Handles module development, state management, provider configuration, multi-environment workflows, and infrastructure testing with error recovery patterns.
How this skill is triggered — by the user, by Claude, or both
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/aigroup-workflow:terraform-engineerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Senior Terraform engineer specializing in infrastructure as code across AWS, Azure, and GCP with expertise in modular design, state management, and production-grade patterns.
Senior Terraform engineer specializing in infrastructure as code across AWS, Azure, and GCP with expertise in modular design, state management, and production-grade patterns.
terraform fmt and terraform validate, then tflint; if any errors are reported, fix them and re-run until all checks pass cleanly before proceedingterraform plan -out=tfplan, review output carefully, then terraform apply tfplan; if the plan fails, see error recovery belowValidation failures (step 5): Fix reported errors → re-run terraform validate → repeat until clean. For tflint warnings, address rule violations before proceeding.
Plan failures (step 6):
terraform refresh to reconcile state with real resources, or use terraform state rm / terraform import to realign specific resources, then re-plan.terraform init if provider plugins are stale, then re-plan.depends_on references or restructure module outputs to resolve unknown values, then re-plan.After any fix, return to step 5 to re-validate before re-running the plan.
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Modules | references/module-patterns.md | Creating modules, inputs/outputs, versioning |
| State | references/state-management.md | Remote backends, locking, workspaces, migrations |
| Providers | references/providers.md | AWS/Azure/GCP configuration, authentication |
| Testing | references/testing.md | terraform plan, terratest, policy as code |
| Best Practices | references/best-practices.md | DRY patterns, naming, security, cost tracking |
terraform fmt and terraform validate.terraform directoriesmain.tf
resource "aws_s3_bucket" "this" {
bucket = var.bucket_name
tags = var.tags
}
variables.tf
variable "bucket_name" {
description = "Name of the S3 bucket"
type = string
validation {
condition = length(var.bucket_name) > 3
error_message = "bucket_name must be longer than 3 characters."
}
}
variable "tags" {
description = "Tags to apply to all resources"
type = map(string)
default = {}
}
outputs.tf
output "bucket_id" {
description = "ID of the created S3 bucket"
value = aws_s3_bucket.this.id
}
terraform {
backend "s3" {
bucket = "my-tf-state"
key = "env/prod/terraform.tfstate"
region = "us-east-1"
encrypt = true
dynamodb_table = "terraform-lock"
}
}
terraform {
required_version = ">= 1.5.0"
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 5.0"
}
azurerm = {
source = "hashicorp/azurerm"
version = "~> 3.0"
}
}
}
When implementing Terraform solutions, provide: module structure (main.tf, variables.tf, outputs.tf), backend and provider configuration, example usage with tfvars, and a brief explanation of design decisions.
npx claudepluginhub codeape-7/ai-agent-workflowgroupImplements Terraform infrastructure as code across AWS, Azure, or GCP. Handles module development, state management, backend migration, provider configuration, multi-environment workflows, and infrastructure testing.
Provisions and manages cloud infrastructure using Terraform with HCL modules, remote state backends, and plan/apply workflow. Useful for IaC, multi-environment setup, and migrating from ClickOps or CloudFormation.
Provides Terraform expertise for HCL module design, state management, workspaces, provider configuration, drift detection, Terragrunt, infrastructure testing, and resource lifecycle management. Use for module reviews, remote backends, CI scanning, and zero-downtime deployments.