Terraform and OpenTofu infrastructure as code — module design, state management, multi-environment setups, remote backends, secrets management, CI/CD integration. NOT for Pulumi, CDK, Ansible, or Kubernetes manifests.
npx skills add https://github.com/curiositech/some_claude_skills --skill terraform-iac-expert
Expert in Infrastructure as Code using Terraform and OpenTofu. Specializes in module design, state management, multi-cloud deployments, and CI/CD integration. Handles complex infrastructure patterns including multi-environment setups, remote state backends, and secure secrets management.
Works well with:
aws-solutions-architect - AWS resource patternskubernetes-orchestrator - K8s infrastructuregithub-actions-pipeline-builder - CI/CD automationsite-reliability-engineer - Production infrastructureterraform/
├── modules/
│ ├── vpc/
│ │ ├── main.tf
│ │ ├── variables.tf
│ │ └── outputs.tf
│ ├── eks/
│ └── rds/
├── environments/
│ ├── dev/
│ │ ├── main.tf
│ │ ├── variables.tf
│ │ ├── terraform.tfvars
│ │ └── backend.tf
│ ├── staging/
│ └── prod/
└── shared/
└── provider.tf
# environments/prod/main.tf
terraform {
required_version = ">= 1.5.0"
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 5.0"
}
}
backend "s3" {
bucket = "mycompany-terraform-state"
key = "prod/terraform.tfstate"
region = "us-west-2"
encrypt = true
dynamodb_table = "terraform-locks"
}
}
locals {
environment = "prod"
project = "myapp"
common_tags = {
Environment = local.environment
Project = local.project
ManagedBy = "terraform"
}
}
module "vpc" {
source = "../../modules/vpc"
environment = local.environment
cidr_block = "10.0.0.0/16"
tags = local.common_tags
}
module "eks" {
source = "../../modules/eks"
environment = local.environment
vpc_id = module.vpc.vpc_id
private_subnet_ids = module.vpc.private_subnet_ids
cluster_version = "1.29"
tags = local.common_tags
}
# modules/vpc/variables.tf
variable "environment" {
type = string
description = "Environment name (dev, staging, prod)"
validation {
condition = contains(["dev", "staging", "prod"], var.environment)
error_message = "Environment must be dev, staging, or prod."
}
}
variable "cidr_block" {
type = string
description = "VPC CIDR block"
validation {
condition = can(cidrhost(var.cidr_block, 0))
error_message = "Must be a valid CIDR block."
}
}
variable "availability_zones" {
type = list(string)
description = "List of AZs to use"
default = ["us-west-2a", "us-west-2b", "us-west-2c"]
}
variable "enable_nat_gateway" {
type = bool
description = "Enable NAT Gateway for private subnets"
default = true
}
variable "tags" {
type = map(string)
description = "Tags to apply to all resources"
default = {}
}
# modules/security-group/main.tf
resource "aws_security_group" "this" {
name = var.name
description = var.description
vpc_id = var.vpc_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
description = ingress.value.description
}
}
egress {
from_port = 0
to_port = 0
protocol = "-1"
cidr_blocks = ["0.0.0.0/0"]
}
tags = merge(var.tags, {
Name = var.name
})
}
# Reference another environment's state
data "terraform_remote_state" "shared" {
backend = "s3"
config = {
bucket = "mycompany-terraform-state"
key = "shared/terraform.tfstate"
region = "us-west-2"
}
}
# Use outputs from shared state
resource "aws_instance" "app" {
ami = data.terraform_remote_state.shared.outputs.base_ami_id
instance_type = "t3.medium"
subnet_id = data.terraform_remote_state.shared.outputs.private_subnet_id
}
# .github/workflows/terraform.yml
name: Terraform
on:
pull_request:
paths:
- 'terraform/**'
push:
branches: [main]
paths:
- 'terraform/**'
env:
TF_VERSION: 1.6.0
AWS_REGION: us-west-2
jobs:
plan:
runs-on: ubuntu-latest
permissions:
contents: read
pull-requests: write
id-token: write # For OIDC
steps:
- uses: actions/checkout@v4
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::123456789:role/terraform-github-actions
aws-region: ${{ env.AWS_REGION }}
- uses: hashicorp/setup-terraform@v3
with:
terraform_version: ${{ env.TF_VERSION }}
- name: Terraform Init
working-directory: terraform/environments/prod
run: terraform init
- name: Terraform Plan
working-directory: terraform/environments/prod
run: terraform plan -out=tfplan
- name: Upload Plan
uses: actions/upload-artifact@v4
with:
name: tfplan
path: terraform/environments/prod/tfplan
apply:
needs: plan
runs-on: ubuntu-latest
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
environment: production
steps:
- uses: actions/checkout@v4
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::123456789:role/terraform-github-actions
aws-region: ${{ env.AWS_REGION }}
- uses: hashicorp/setup-terraform@v3
with:
terraform_version: ${{ env.TF_VERSION }}
- name: Download Plan
uses: actions/download-artifact@v4
with:
name: tfplan
path: terraform/environments/prod
- name: Terraform Apply
working-directory: terraform/environments/prod
run: terraform apply -auto-approve tfplan
# Import existing AWS resource into state
terraform import aws_s3_bucket.existing my-existing-bucket
# Import using for_each key
terraform import 'aws_iam_user.users["alice"]' alice
# Generate configuration from import (Terraform 1.5+)
terraform plan -generate-config-out=generated.tf
# Reference secrets from AWS Secrets Manager
data "aws_secretsmanager_secret_version" "db_password" {
secret_id = "prod/db/password"
}
resource "aws_db_instance" "main" {
# ... other config ...
password = data.aws_secretsmanager_secret_version.db_password.secret_string
}
# Mark outputs as sensitive
output "db_connection_string" {
value = "postgres://admin:${aws_db_instance.main.password}@${aws_db_instance.main.endpoint}"
sensitive = true
}
~> constraints, not >=depends_on when implicit deps aren't enoughAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
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