Terraform Module Library
Terraform Module Library is an code AI skill with a core value of Build reusable Terraform modules for AWS, Azure, and GCP infrastructure following infrastructure-as-code best practices. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Build reusable Terraform modules for AWS, Azure, and GCP infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, ...
Quick Facts
mkdir -p ./skills/terraform-module-library && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/terraform-module-library/SKILL.md -o ./skills/terraform-module-library/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Terraform Module Library
Production-ready Terraform module patterns for AWS, Azure, and GCP infrastructure.
Do not use this skill when
- The task is unrelated to terraform module library
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.
Purpose
Create reusable, well-tested Terraform modules for common cloud infrastructure patterns across multiple cloud providers.
Use this skill when
- Build reusable infrastructure components
- Standardize cloud resource provisioning
- Implement infrastructure as code best practices
- Create multi-cloud compatible modules
- Establish organizational Terraform standards
Module Structure
terraform-modules/
├── aws/
│ ├── vpc/
│ ├── eks/
│ ├── rds/
│ └── s3/
├── azure/
│ ├── vnet/
│ ├── aks/
│ └── storage/
└── gcp/
├── vpc/
├── gke/
└── cloud-sql/Standard Module Pattern
module-name/
├── main.tf # Main resources
├── variables.tf # Input variables
├── outputs.tf # Output values
├── versions.tf # Provider versions
├── README.md # Documentation
├── examples/ # Usage examples
│ └── complete/
│ ├── main.tf
│ └── variables.tf
└── tests/ # Terratest files
└── module_test.goAWS VPC Module Example
**main.tf:**
resource "aws_vpc" "main" {
cidr_block = var.cidr_block
enable_dns_hostnames = var.enable_dns_hostnames
enable_dns_support = var.enable_dns_support
tags = merge(
{
Name = var.name
},
var.tags
)
}
resource "aws_subnet" "private" {
count = length(var.private_subnet_cidrs)
vpc_id = aws_vpc.main.id
cidr_block = var.private_subnet_cidrs[count.index]
availability_zone = var.availability_zones[count.index]
tags = merge(
{
Name = "${var.name}-private-${count.index + 1}"
Tier = "private"
},
var.tags
)
}
resource "aws_internet_gateway" "main" {
count = var.create_internet_gateway ? 1 : 0
vpc_id = aws_vpc.main.id
tags = merge(
{
Name = "${var.name}-igw"
},
var.tags
)
}**variables.tf:**
variable "name" {
description = "Name of the VPC"
type = string
}
variable "cidr_block" {
description = "CIDR block for VPC"
type = string
validation {
condition = can(regex("^([0-9]{1,3}\\.){3}[0-9]{1,3}/[0-9]{1,2}$", var.cidr_block))
error_message = "CIDR block must be valid IPv4 CIDR notation."
}
}
variable "availability_zones" {
description = "List of availability zones"
type = list(string)
}
variable "private_subnet_cidrs" {
description = "CIDR blocks for private subnets"
type = list(string)
default = []
}
variable "enable_dns_hostnames" {
description = "Enable DNS hostnames in VPC"
type = bool
default = true
}
variable "tags" {
description = "Additional tags"
type = map(string)
default = {}
}**outputs.tf:**
output "vpc_id" {
description = "ID of the VPC"
value = aws_vpc.main.id
}
output "private_subnet_ids" {
description = "IDs of private subnets"
value = aws_subnet.private[*].id
}
output "vpc_cidr_block" {
description = "CIDR block of VPC"
value = aws_vpc.main.cidr_block
}Best Practices
1. **Use semantic versioning** for modules
2. **Document all variables** with descriptions
3. **Provide examples** in examples/ directory
4. **Use validation blocks** for input validation
5. **Output important attributes** for module composition
6. **Pin provider versions** in versions.tf
7. **Use locals** for computed values
8. **Implement conditional resources** with count/for_each
9. **Test modules** with Terratest
10. **Tag all resou
🎯 Best For
- UI designers
- Product designers
- Claude users
- Software engineers
- Development teams
💡 Use Cases
- Generating component mockups
- Creating design system tokens
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Terraform Module Library to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does this work with Figma?
Some design skills integrate with Figma plugins. Check the Works With section for supported tools.
Is Terraform Module Library compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Terraform Module Library?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Terraform Module Library?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/terraform-module-library/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping usability testing
AI-generated designs should be validated with real users before development.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.