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.
npx skills add https://github.com/Jeffallan/claude-skills --skill 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.
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.
Assess 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.
Take jeffallan/terraform-engineer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.