mcpbeat Sign in

Terraform Style Guide Agent Skill

by hashicorp

Generate Terraform HCL code following HashiCorp's official style conventions and best practices. Use when writing, reviewing, or generating Terraform configurations.

3k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
71 d ago
last touched
this folder, not the whole repository

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/hashicorp/agent-skills --skill terraform-style-guide

The instruction itself

19 sections, as written by the author

Terraform Style Guide

Generate and maintain Terraform code following HashiCorp's official style conventions and best practices.

Reference: HashiCorp Terraform Style Guide

Code Generation Strategy

When generating Terraform code:

  • Start with provider configuration and version constraints
  • Create data sources before dependent resources
  • Build resources in dependency order
  • Add outputs for key resource attributes
  • Use variables for all configurable values

File Organization

| File | Purpose |

|------|---------|

| terraform.tf | Terraform and provider version requirements |

| providers.tf | Provider configurations |

| main.tf | Primary resources and data sources |

| variables.tf | Input variable declarations (alphabetical) |

| outputs.tf | Output value declarations (alphabetical) |

| locals.tf | Local value declarations |

Example Structure

# terraform.tf
terraform {
  required_version = ">= 1.14"

  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 6.0"
    }
  }
}

# variables.tf
variable "environment" {
  description = "Target deployment environment"
  type        = string

  validation {
    condition     = contains(["dev", "staging", "prod"], var.environment)
    error_message = "Environment must be dev, staging, or prod."
  }
}

# locals.tf
locals {
  common_tags = {
    Environment = var.environment
    ManagedBy   = "Terraform"
  }
}

# main.tf
resource "aws_vpc" "main" {
  cidr_block           = var.vpc_cidr
  enable_dns_hostnames = true

  tags = merge(local.common_tags, {
    Name = "${var.project_name}-${var.environment}-vpc"
  })
}

# outputs.tf
output "vpc_id" {
  description = "ID of the created VPC"
  value       = aws_vpc.main.id
}

Code Formatting

Indentation and Alignment

  • Use two spaces per nesting level (no tabs)
  • Align equals signs for consecutive arguments
resource "aws_instance" "web" {
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t2.micro"
  subnet_id     = "subnet-12345678"

  tags = {
    Name        = "web-server"
    Environment = "production"
  }
}

Block Organization

Arguments precede blocks, with meta-arguments first:

resource "aws_instance" "example" {
  # Meta-arguments
  count = 3

  # Arguments
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t2.micro"

  # Blocks
  root_block_device {
    volume_size = 20
  }

  # Lifecycle last
  lifecycle {
    create_before_destroy = true
  }
}

Naming Conventions

  • Use lowercase with underscores for all names
  • Use descriptive nouns excluding the resource type
  • Be specific and meaningful
  • Resource names must be singular, not plural
  • Default to main for resources where a specific descriptive name is redundant or unavailable, provided only one instance exists
# Bad
resource "aws_instance" "webAPI-aws-instance" {}
resource "aws_instance" "web_apis" {}
variable "name" {}

# Good
resource "aws_instance" "web_api" {}
resource "aws_vpc" "main" {}
variable "application_name" {}

Variables

Every variable must include type and description:

variable "instance_type" {
  description = "EC2 instance type for the web server"
  type        = string
  default     = "t2.micro"

  validation {
    condition     = contains(["t2.micro", "t2.small", "t2.medium"], var.instance_type)
    error_message = "Instance type must be t2.micro, t2.small, or t2.medium."
  }
}

variable "database_password" {
  description = "Password for the database admin user"
  type        = string
  sensitive   = true
}

Outputs

Every output must include description:

output "instance_id" {
  description = "ID of the EC2 instance"
  value       = aws_instance.web.id
}

output "database_password" {
  description = "Database administrator password"
  value       = aws_db_instance.main.password
  sensitive   = true
}

Dynamic Resource Creation

Prefer for_each over count

# Bad - count for multiple resources
resource "aws_instance" "web" {
  count = var.instance_count
  tags  = { Name = "web-${count.index}" }
}

# Good - for_each with named instances
variable "instance_names" {
  type    = set(string)
  default = ["web-1", "web-2", "web-3"]
}

resource "aws_instance" "web" {
  for_each = var.instance_names
  tags     = { Name = each.key }
}

count for Conditional Creation

resource "aws_cloudwatch_metric_alarm" "cpu" {
  count = var.enable_monitoring ? 1 : 0

  alarm_name = "high-cpu-usage"
  threshold  = 80
}

Security Best Practices

Refer to SECURITY.md. It includes guidance on encrypting resources,

preventing sensitive data in state, and secure configurations.

Version Pinning

terraform {
  required_version = ">= 1.14"

  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 6.0"
    }
  }
}

Use the latest major version of each provider and the latest minor version of

Terraform, unless otherwise constrained by a dependency lock file or by other

modules used by the configuration.

Version constraint operators:

  • = 1.0.0 - Exact version
  • >= 1.0.0 - Greater than or equal
  • ~> 1.0 - Allow rightmost component to increment
  • >= 1.0, < 2.0 - Version range

Provider Configuration

provider "aws" {
  region = "us-west-2"

  default_tags {
    tags = {
      ManagedBy = "Terraform"
      Project   = var.project_name
    }
  }
}

# Aliased provider for multi-region
provider "aws" {
  alias  = "east"
  region = "us-east-1"
}

Version Control

Never commit:

  • terraform.tfstate, terraform.tfstate.backup
  • .terraform/ directory
  • *.tfplan
  • .tfvars files with sensitive data

Always commit:

  • All .tf configuration files
  • .terraform.lock.hcl (dependency lock file)

Validation Tools

Run before committing:

terraform fmt -recursive
terraform validate

Additional tools:

  • tflint - Linting and best practices
  • checkov / tfsec - Security scanning

Code Review Checklist

  • [ ] Code formatted with terraform fmt
  • [ ] Configuration validated with terraform validate
  • [ ] Files organized according to standard structure
  • [ ] All variables have type and description
  • [ ] All outputs have descriptions
  • [ ] Resource names use descriptive nouns with underscores
  • [ ] Version constraints pinned explicitly
  • [ ] Sensitive values marked with sensitive = true
  • [ ] No hardcoded credentials or secrets
  • [ ] Security best practices applied

*Based on: HashiCorp Terraform Style Guide*

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

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.

13k tokens
Capacity
by microsoft
vendor ×3

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.

6k tokens scripts
Customize
by microsoft
vendor ×3

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).

8k tokens
Deploy Model
by microsoft
vendor ×3

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).

26k tokens scripts
Preset
by microsoft
vendor ×3

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).

9k tokens
Lamindb
by christophacham
×3

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.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

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.

17k tokens

How to use it

Copy the folder

Take hashicorp/terraform-style-guide from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.