Claude-skill-registry iac-automation
Terraform, Pulumi, CloudFormation, and infrastructure as code for data platforms
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/iac-automation" ~/.claude/skills/majiayu000-claude-skill-registry-iac-automation && rm -rf "$T"
manifest:
skills/data/iac-automation/SKILL.mdsource content
Infrastructure as Code
Production infrastructure automation with Terraform, Pulumi, and cloud-native IaC patterns.
Quick Start
# Terraform - AWS Data Lake Infrastructure terraform { required_providers { aws = { source = "hashicorp/aws" version = "~> 5.0" } } backend "s3" { bucket = "terraform-state-prod" key = "data-lake/terraform.tfstate" region = "us-east-1" } } # Data Lake S3 Bucket resource "aws_s3_bucket" "data_lake" { bucket = "company-data-lake-${var.environment}" tags = { Environment = var.environment ManagedBy = "terraform" } } resource "aws_s3_bucket_versioning" "data_lake" { bucket = aws_s3_bucket.data_lake.id versioning_configuration { status = "Enabled" } } # Glue Catalog Database resource "aws_glue_catalog_database" "analytics" { name = "analytics_${var.environment}" } # Output output "data_lake_bucket" { value = aws_s3_bucket.data_lake.bucket }
Core Concepts
1. Terraform Modules
# modules/data-pipeline/main.tf variable "pipeline_name" { type = string description = "Name of the data pipeline" } variable "schedule" { type = string default = "cron(0 2 * * ? *)" } resource "aws_glue_job" "etl" { name = var.pipeline_name role_arn = aws_iam_role.glue.arn command { script_location = "s3://${var.scripts_bucket}/jobs/${var.pipeline_name}.py" python_version = "3" } default_arguments = { "--job-language" = "python" "--enable-metrics" = "true" "--enable-spark-ui" = "true" } glue_version = "4.0" worker_type = "G.1X" number_of_workers = 2 } resource "aws_glue_trigger" "scheduled" { name = "${var.pipeline_name}-trigger" schedule = var.schedule type = "SCHEDULED" actions { job_name = aws_glue_job.etl.name } } # Usage module "customer_pipeline" { source = "./modules/data-pipeline" pipeline_name = "customer-etl" schedule = "cron(0 3 * * ? *)" }
2. State Management
# Remote state configuration terraform { backend "s3" { bucket = "terraform-state" key = "env/prod/terraform.tfstate" region = "us-east-1" encrypt = true dynamodb_table = "terraform-locks" } } # State locking with DynamoDB resource "aws_dynamodb_table" "terraform_locks" { name = "terraform-locks" billing_mode = "PAY_PER_REQUEST" hash_key = "LockID" attribute { name = "LockID" type = "S" } } # Import existing resources # terraform import aws_s3_bucket.existing bucket-name # Move resources between states # terraform state mv module.old.resource module.new.resource
3. Pulumi (Python)
import pulumi import pulumi_aws as aws # Configuration config = pulumi.Config() environment = config.require("environment") # S3 Data Lake data_lake = aws.s3.Bucket( "data-lake", bucket=f"company-data-lake-{environment}", versioning=aws.s3.BucketVersioningArgs(enabled=True), tags={"Environment": environment, "ManagedBy": "pulumi"} ) # Glue Database analytics_db = aws.glue.CatalogDatabase( "analytics", name=f"analytics_{environment}" ) # Lambda for data processing data_processor = aws.lambda_.Function( "data-processor", runtime="python3.11", handler="handler.main", role=lambda_role.arn, code=pulumi.FileArchive("./lambda"), environment=aws.lambda_.FunctionEnvironmentArgs( variables={"BUCKET": data_lake.bucket} ) ) # Export outputs pulumi.export("bucket_name", data_lake.bucket) pulumi.export("database_name", analytics_db.name)
4. Environment Management
# environments/prod/main.tf module "data_platform" { source = "../../modules/data-platform" environment = "prod" vpc_cidr = "10.0.0.0/16" instance_type = "r5.2xlarge" min_capacity = 2 max_capacity = 10 tags = { Environment = "prod" CostCenter = "data-engineering" } } # Workspace-based environments # terraform workspace new prod # terraform workspace select prod locals { env_config = { dev = { instance_type = "t3.medium" min_nodes = 1 } prod = { instance_type = "r5.xlarge" min_nodes = 3 } } config = local.env_config[terraform.workspace] }
Tools & Technologies
| Tool | Purpose | Version (2025) |
|---|---|---|
| Terraform | IaC standard | 1.7+ |
| Pulumi | IaC with Python | 3.100+ |
| CloudFormation | AWS native | Latest |
| Terragrunt | Terraform wrapper | 0.55+ |
| tfsec | Security scanning | 1.28+ |
| Checkov | Policy as code | 3.2+ |
Troubleshooting Guide
| Issue | Symptoms | Root Cause | Fix |
|---|---|---|---|
| State Lock | Can't apply | Previous run crashed | |
| Drift | Plan shows changes | Manual changes | Import or recreate |
| Cycle Error | Dependency cycle | Circular references | Refactor dependencies |
| Provider Error | Auth failed | Wrong credentials | Check AWS profile |
Best Practices
# ✅ DO: Use variables with validation variable "environment" { type = string validation { condition = contains(["dev", "staging", "prod"], var.environment) error_message = "Environment must be dev, staging, or prod." } } # ✅ DO: Tag all resources default_tags { tags = { ManagedBy = "terraform" Environment = var.environment } } # ✅ DO: Use data sources for existing resources data "aws_vpc" "existing" { id = var.vpc_id } # ❌ DON'T: Hard-code values # ❌ DON'T: Store state locally in production # ❌ DON'T: Skip plan review before apply
Resources
Skill Certification Checklist:
- Can write Terraform modules
- Can manage remote state
- Can use workspaces for environments
- Can implement security best practices
- Can automate with CI/CD