terraform-style-guide

hashicorp/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/hashicorp/agent-skills --skill terraform-style-guide
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summary

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

  • Enforces two-space indentation, lowercase underscore naming, and standard file organization across terraform.tf , providers.tf , main.tf , variables.tf , outputs.tf , and locals.tf
  • Requires type and description on all variables and outputs, with validation rules and sensitive flag support for credentials
  • Prioritizes for_each over count for dynamic resources, applies security hardening (encryption,
skill.md

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:

  1. Start with provider configuration and version constraints
  2. Create data sources before dependent resources
  3. Build resources in dependency order
  4. Add outputs for key resource attributes
  5. 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 = ">= 1.7"

  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }
}

# 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

When generating code, apply security hardening:

  • Enable encryption at rest by default
  • Configure private networking where applicable
  • Apply principle of least privilege for security groups
  • Enable logging and monitoring
  • Never hardcode credentials or secrets
  • Mark sensitive outputs with sensitive = true

Example: Secure S3 Bucket

resource "aws_s3_bucket" "data" {
  bucket = "${var.project}-${var.environment}-data"
  tags   = local.common_tags
}

resource "aws_s3_bucket_versioning" "data" {
  bucket = aws_s3_bucket.data.id

  versioning_configuration {
    status = "Enabled"
  }
}

resource "aws_s3_bucket_server_side_encryption_configuration" "data" 
how to use terraform-style-guide

How to use terraform-style-guide on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add terraform-style-guide
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/hashicorp/agent-skills --skill terraform-style-guide

The skills CLI fetches terraform-style-guide from GitHub repository hashicorp/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/terraform-style-guide

Reload or restart Cursor to activate terraform-style-guide. Access the skill through slash commands (e.g., /terraform-style-guide) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.726 reviews
  • Shikha Mishra· Dec 28, 2024

    terraform-style-guide has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Camila Thompson· Dec 28, 2024

    terraform-style-guide fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Nov 19, 2024

    Solid pick for teams standardizing on skills: terraform-style-guide is focused, and the summary matches what you get after install.

  • Omar Robinson· Nov 19, 2024

    Registry listing for terraform-style-guide matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Sakshi Patil· Nov 15, 2024

    terraform-style-guide reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Dhruvi Jain· Oct 10, 2024

    We added terraform-style-guide from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Luis Chen· Oct 10, 2024

    Keeps context tight: terraform-style-guide is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chaitanya Patil· Oct 6, 2024

    I recommend terraform-style-guide for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kofi Tandon· Sep 5, 2024

    terraform-style-guide fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kofi Nasser· Aug 24, 2024

    terraform-style-guide is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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