refactor-module
Break monolithic Terraform configurations into reusable, well-structured modules with clear contracts and migration paths.
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What it does
Analyzes existing code to identify refactoring candidates, groups resources by logical function, and assesses complexity before design
Generates module interfaces with typed variables, validation rules, and descriptive outputs following HashiCorp best practices
Provides state migration strategies using moved blocks (Terraform 1.1+) or manual terraform state mv co
Installation Guide
How to use refactor-module on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
refactor-module
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches refactor-module from hashicorp/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate refactor-module. Access via /refactor-module in your agent's command palette.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Skill: Refactor Module
Overview
This skill guides AI agents in transforming monolithic Terraform configurations into reusable, maintainable modules following HashiCorp's module design principles and community best practices.
Capability Statement
The agent will analyze existing Terraform code and systematically refactor it into well-structured modules with:
- Clear interface contracts (variables and outputs)
- Proper encapsulation and abstraction
- Versioning and documentation
- Testing frameworks
- Migration path for existing state
Prerequisites
- Existing Terraform configuration to refactor
- Understanding of resource dependencies
- Access to current state file (for migration planning)
- Knowledge of module registry patterns
Input Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
source_directory |
string | Yes | Path to existing Terraform configuration |
module_name |
string | Yes | Name for the new module |
abstraction_level |
string | No | "simple", "intermediate", "advanced" (default: intermediate) |
preserve_state |
boolean | Yes | Whether to maintain state compatibility |
target_registry |
string | No | Target module registry (local, private, public) |
Execution Steps
1. Analysis Phase
**Identify Refactoring Candidates**
- Group resources by logical function
- Identify repeated patterns
- Map resource dependencies
- Detect configuration coupling
- Analyze variable usage patterns
**Complexity Assessment**
- Count resource relationships
- Measure variable propagation depth
- Identify cross-resource references
- Evaluate state migration complexity
2. Module Design
Interface Design
# Define clear input contract
variable "network_config" {
description = "Network configuration parameters"
type = object({
cidr_block = string
availability_zones = list(string)
enable_nat = bool
})
validation {
condition = can(cidrhost(var.network_config.cidr_block, 0))
error_message = "CIDR block must be valid IPv4 CIDR."
}
}
# Define output contract
output "vpc_id" {
description = "ID of the created VPC"
value = aws_vpc.main.id
}
output "private_subnet_ids" {
description = "List of private subnet IDs"
value = { for k, v in aws_subnet.private : k => v.id }
}
Encapsulation Strategy
**What to Include in Module:**
- Tightly coupled resources (VPC + subnets)
- Resources with shared lifecycle
- Configuration with clear boundaries
**What to Keep Separate:**
- Cross-cutting concerns (monitoring, tagging)
- Resources with different lifecycles
- Provider-specific configurations
3. Code Transformation
Before: Monolithic Configuration
# main.tf (monolithic)
resource "aws_vpc" "main" {
cidr_block = "10.0.0.0/16"
enable_dns_hostnames = true
tags = {
Name = "production-vpc"
Environment = "prod"
}
}
resource "aws_subnet" "public_1" {
vpc_id = aws_vpc.main.id
cidr_block = "10.0.1.0/24"
availability_zone = "us-east-1a"
tags = {
Name = "public-subnet-1"
Type = "public"
}
}
resource "aws_subnet" "public_2" {
vpc_id = aws_vpc.main.id
cidr_block = "10.0.2.0/24"
availability_zone = "us-east-1b"
tags = {
Name = "public-subnet-2"
Type = "public"
}
}
resource "aws_internet_gateway" "main" {
vpc_id = aws_vpc.main.id
tags = {
Name = "production-igw"
}
}
# ... more repetitive subnet and routing resources
After: Modular Structure
# modules/vpc/main.tf
locals {
subnet_count = length(var.availability_zones)
}
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(
var.tags,
{
Name = var.name
}
)
}
resource "aws_subnet" "public" {
for_each = var.create_public_subnets ? toset(var.availability_zones) : []
vpc_id = aws_vpc.main.id
cidr_block = cidrsubnet(var.cidr_block, 8, index(var.availability_zones, each.value))
availability_zone = each.value
map_public_ip_on_launch = true
tags = merge(
var.tags,
{
Name = "${var.name}-public-${each.value}"
Type = "public"
}
)
}
resource "aws_internet_gateway" "main" {
count = var.create_public_subnets ? 1 : 0
vpc_id = aws_vpc.main.id
tags = merge(
var.tags,
{
Name = "${var.name}-igw"
}
)
}
# modules/vpc/variables.tf
variable "name" {
description = "Name prefix for all resources"
type = string
}
variable "cidr_block" {
description = "CIDR block for the VPC"
type = string
validation {
condition = can(cidrhost(var.cidr_block, 0))
error_message = "Must be a valid IPv4 CIDR block."
}
}
variable "availability_zones" {
description = "List of availability zones"
type = list(string)
}
variable "create_public_subnets" {
description = "Whether to create public subnets"
type = bool
default = true
}
variable "enable_dns_hostnames" {
description = "Enable DNS hostnames in the VPC"
type = bool
default = true
}
variable "enable_dns_support" {
description = "Enable DNS support in the VPC"
type = bool
default = true
}
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Use Cases
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- EEmma Sanchez★★★★★Dec 8, 2024
refactor-module reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAmina Nasser★★★★★Dec 4, 2024
refactor-module is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- AAva Harris★★★★★Nov 27, 2024
refactor-module is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- IIsabella White★★★★★Nov 23, 2024
refactor-module reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAdvait Garcia★★★★★Oct 18, 2024
Useful defaults in refactor-module — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- IIsabella Kim★★★★★Oct 14, 2024
I recommend refactor-module for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- KKofi Brown★★★★★Sep 25, 2024
refactor-module is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- OOlivia Agarwal★★★★★Sep 25, 2024
refactor-module has been reliable in day-to-day use. Documentation quality is above average for community skills.
- YYash Thakker★★★★★Sep 5, 2024
refactor-module reduced setup friction for our internal harness; good balance of opinion and flexibility.
- DDhruvi Jain★★★★★Aug 24, 2024
I recommend refactor-module for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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