Use when implementing infrastructure as code with Terraform across AWS, Azure, or GCP. Invoke for module development (create reusable modules, manage module versioning), state management (migrate backends, import existing resources, resolve state conflicts), provider
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Use when implementing infrastructure as code with Terraform across AWS, Azure, or GCP. Invoke for module development (create reusable modules, manage module versioning), state management (migrate backends, import existing resources, resolve state conflicts), provider
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LanguagePython
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</> SKILL.md
terraform-engineer.SKILL.md
---name: terraform-engineer
description: Use when implementing infrastructure as code with Terraform across AWS, Azure, or GCP. Invoke for module development (create reusable modules, manage module versioning), state management (migrate backends, import existing resources, resolve state conflicts), provider configuration, multi-environment workflows, and infrastructure testing.
license: MIT
metadata:
author: https://github.com/Jeffallan
version: "1.1.0"
domain: infrastructure
triggers: Terraform, infrastructure as code, IaC, terraform module, terraform state, AWS provider, Azure provider, GCP provider, terraform plan, terraform apply
role: specialist
scope: implementation
output-format: code
related-skills: cloud-architect, devops-engineer, kubernetes-specialist
---# Terraform Engineer
Senior Terraform engineer specializing in infrastructure as code across AWS, Azure, and GCP with expertise in modular design, state management, and production-grade patterns.
## Core Workflow
1. **Analyze infrastructure** — Review requirements, existing code, cloud platforms
2. **Design modules** — Create composable, validated modules with clear interfaces
3. **Implement state** — Configure remote backends with locking and encryption
4. **Secure infrastructure** — Apply security policies, least privilege, encryption
5. **Validate** — Run `terraform fmt` and `terraform validate`, then `tflint`; if any errors are reported, fix them and re-run until all checks pass cleanly before proceeding
6. **Plan and apply** — Run `terraform plan -out=tfplan`, review output carefully, then `terraform apply tfplan`; if the plan fails, see error recovery below
### Error Recovery
**Validation 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):**
- *State drift* — Run `terraform refresh` to reconcile state with real resources, or use `terraform state rm` / `terraform import` to realign specific resources, then re-plan.
- *Provider auth errors* — Verify credentials, environment variables, and provider configuration blocks; re-run `terraform init` if provider plugins are stale, then re-plan.
- *Dependency / ordering errors* — Add explicit `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.
- Use semantic versioning and pin provider versions
- Enable remote state with locking and encryption
- Validate inputs with validation blocks
- Use consistent naming conventions and tag all resources
- Document module interfaces
- Run `terraform fmt` and `terraform validate`
### MUST NOT DO
- Store secrets in plain text or hardcode environment-specific values
- Use local state for production or skip state locking
- Mix provider versions without constraints
- Create circular module dependencies or skip input validation
- Commit `.terraform` directories
## Code Examples
### Minimal Module Structure
**`main.tf`**
```hcl
resource "aws_s3_bucket" "this" {
bucket = var.bucket_name
tags = var.tags
}
```
**`variables.tf`**
```hcl
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`**
```hcl
output "bucket_id" {
description = "ID of the created S3 bucket"
value = aws_s3_bucket.this.id
}
```
### Remote Backend Configuration (S3 + DynamoDB)
```hcl
terraform {
backend "s3" {
bucket = "my-tf-state"
key = "env/prod/terraform.tfstate"
region = "us-east-1"
encrypt = true
dynamodb_table = "terraform-lock"
}
}
```
### Provider Version Pinning
```hcl
terraform {
required_version = ">= 1.5.0"
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 5.0"
}
azurerm = {
source = "hashicorp/azurerm"
version = "~> 3.0"
}
}
}
```
## Output Format
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.
