Module Editor VS Code Extenstion AI-Based

AI-Based Module Editor with VS Code Extension

Author:

Alex Cho

| Oct 23, 2025

Summary

AI-Augmented IaC Authoring: StackGen’s AI-Based Module Editor, integrated directly into VS Code (and forks like Cursor), revolutionizes Terraform/OpenTofu module creation by providing deterministic, template-driven AI assistance that scaffolds, validates, and enforces compliance in real time within the IDE.
Determinism and Reproducibility: Unlike general-purpose GenAI tools such as ControlMonkey, StackGen guarantees identical, byte-for-byte module outputs for the same input, ensuring reproducibility, auditability, and compliance for production-grade infrastructure.
IDE-First Productivity: Embedding StackGen in VS Code eliminates the need to switch between tools, enabling inline policy enforcement, linting, and quick-fix diagnostics. This shift-left approach accelerates module delivery and reduces onboarding time for DevOps teams.
Enterprise-Grade Governance: The extension includes built-in OPA/Rego-style policy enforcement, generation hash validation, and metadata tagging, simplifying code review and CI/CD integration while maintaining strict governance and security controls.
Migration and Adoption Path: StackGen offers a clear, phased migration plan from manual IaC to AI-assisted module generation, enabling teams to incrementally adopt deterministic AI workflows, integrate validation pipelines, and achieve faster, consistent, and compliant IaC delivery across enterprises.

Authoring Infrastructure-as-Code (IaC) modules in Terraform, or OpenTofu, has long required a deep mastery of variable namespaces, naming conventions, configuration patterns, security best practices, and collaboration workflows. This complexity often leads to slow onboarding, duplicated efforts, and subtle errors that only surface in staging or production environments. The StackGen AI-Based Module Editor offers a modern reimagining of this process: a purpose-built, AI-augmented editing experience embedded directly within your IDE (like VS Code, Cursor, or any VS Code forks), designed to dramatically flatten the learning curve.

The AI-Based Module Editor transforms module authoring by providing in-editor scaffolding, context-aware suggestions, and real-time validation. As you describe your infrastructure intent, say, a secure S3 bucket with logging, it generates deterministic, reusable module templates. Key variables, inputs, and outputs are inferred and structured automatically, and compliance checks (e.g., naming conventions or resource tagging) are baked into the authoring flow. The StackGen extension surfaces all of this inline inside VS Code, Cursor, etc, allowing developers to stay in their preferred environment and accelerate productivity without sacrificing control.

Unlike many generative-AI tools that produce unpredictable or “close-enough” results, StackGen emphasizes determinism and reproducibility. This isn't vibe coding, where AI churns out boilerplate and developers blindly deploy based on the vibe. It’s a discipline-first toolset: every module output is repeatable, auditable, and traceable.

As one Redditor in r/devops put it: reddit

Meanwhile, adoption numbers reinforce the momentum and caution around AI in development. According to Stack Overflow’s 2025 survey, 84% of developers now use or plan to use AI tools, but 46% still don’t trust its accuracy, IT Pro. This highlights the need for tools like StackGen, which marry AI assistance with predictable outcomes and human oversight.

Why the VS Code Extension Matters for Infrastructure Engineers

For most DevOps engineers and cloud architects, VS Code is already the primary workspace. Terraform configurations, Helm charts, CI/CD pipeline YAMLs, and test scripts all are written and reviewed there. Switching out to a separate web console or CLI tool every time you want to generate or tweak an IaC module adds friction, breaks flow state, and increases context-switching errors.

Embedding StackGen’s AI-Based Module Editor directly inside VS Code eliminates that context shift. You describe the infrastructure intent, and the extension scaffolds a deterministic Terraform/OpenTofu module on the spot. Real-time linting, policy validation, and variable suggestions happen inline, so engineers can catch compliance or security issues before the code even hits a branch. The result: less back-and-forth with reviewers, faster iteration, and reduced cognitive load.

Traditional module authoring often means working in a separate UI or script generator, exporting code to a repo, then opening a pull request to integrate. That’s two or three feedback loops removed from your actual IDE. The StackGen extension collapses that into a single loop: author, validate, and commit in one place. It aligns with modern DevOps principles, shift-left validation, tight integration with Git, and reproducible infrastructure artifacts, without forcing teams to learn yet another standalone tool.

How the StackGen Extension Works Under the Hood

Deterministic AI Authoring

Infrastructure code isn’t like UI snippets where “close enough” is acceptable. When you’re provisioning VPCs, IAM roles, or databases, subtle changes in generated code can introduce security holes, billing surprises, or drift between environments. Deterministic AI generation means that given the same input and context, the module output is always identical, no hidden randomness, no “creative” refactoring by the AI. This reproducibility is critical for version control, compliance audits, and CI/CD pipelines that expect predictable diffs and plan/apply outcomes.

StackGen’s approach

Inside the VS Code extension, StackGen’s AI engine uses structured templates and a rules-based layer on top of large language models. As you describe the desired resource (“EKS cluster with managed node groups”), the extension maps your intent to a curated library of Terraform/OpenTofu module patterns. Variables, outputs, tags, and policy guardrails are inserted consistently. The generated code is cached and signed so that re-runs of the same prompt produce byte-for-byte identical modules unless you change the input. This allows teams to pin module versions and trust that pipeline previews match what will be applied.

Difference from ControlMonkey (non-deterministic GenAI)

ControlMonkey and similar tools lean heavily on general-purpose generative AI. Those outputs can vary from run to run or drift as the underlying model updates, meaning the same prompt today may not yield the same Terraform tomorrow. That’s fine for brainstorming or one-off scripts, but risky for production IaC. StackGen deliberately trades “creativity” for “consistency”: its AI suggestions are bound by deterministic templates and policy checks so that teams get predictable, reviewable modules every time. This distinction, deterministic vs. non-deterministic, is what makes StackGen viable for compliance-heavy infrastructure, not just prototypes.

Live Demo: Building a Module with StackGen Extension

This section walks through a real-world example of authoring a Terraform/OpenTofu module directly inside the StackGen VS Code extension. It shows how deterministic AI authoring behaves under realistic IaC conditions.

1) Open the StackGen Module Editor and create the spec

Module name: secure_s3
Region: us-east-1
Bucket name (or pattern): platform-secure-s3-${var.env}
Encryption: KMS (choose either managed or supply KMS key ID)
Logging target: audit-logs (existing bucket) and prefix secure_s3/
ACL: private
Tags: team=platform, env=stage

2) Generate the module (inside VS Code)

Generate.

main.tf (resources)
variables.tf (typed inputs)
outputs.tf
README.md
optionally examples/ and tests/

3) Run local Terraform validation (single repo/module)

Open a VS Code terminal in the module folder and run:


  



cd modules/custom_bucket

# init without backend so it's safe for local validation
terraform init -backend=false

# format check
terraform fmt -check

# validate HCL/syntax
terraform validate

# plan (produces a plan file; doesn't apply)
terraform plan -var-file=dev.tfvars -out=plan.tfplan

If providers need credentials, set AWS_PROFILE or env vars beforehand:


  



export AWS_PROFILE=your-profile
# or
export AWS_ACCESS_KEY_ID=...
export AWS_SECRET_ACCESS_KEY=...

To see the plan JSON (useful for automated checking):


  



terraform show -json plan.tfplan | jq .

4) Run static security/lint checks (recommended)


  



Install and run the tools (examples):
# tflint (linter)
tflint --init
tflint

# tfsec (security scanner)
tfsec .

# checkov (policy-as-code)
checkov -d .

These detect common misconfigurations (public buckets, missing encryption, insecure lifecycle, etc.).

5) Run the checks in parallel inside VS Code

Open 3 terminal panes (Ctrl+Shift+5 / split) and run:


  



# Pane 1
cd modules/secure_s3 && terraform init -backend=false && terraform validate

# Pane 2
cd modules/secure_s3 && tflint

# Pane 3
cd modules/secure_s3 && checkov -d .

This is simple and resilient; you’ll see each check’s output separately.

6) Verify deterministic generation (byte-for-byte comparison)

You want to prove the generated output is deterministic. Steps:

Linux / macOS


  



# 1) compute hash after first generate
(cd modules/secure_s3 && find . -type f -name '*.tf' -print | sort | xargs cat) | sha256sum > /tmp/gen.hash1

# 2) regenerate in StackGen (click Generate again)
# 3) compute hash again
(cd modules/secure_s3 && find . -type f -name '*.tf' -print | sort | xargs cat) | sha256sum > /tmp/gen.hash2

# 4) compare
diff /tmp/gen.hash1 /tmp/gen.hash2 || echo "hashes differ (non-deterministic)"

Windows (PowerShell)


  



# 1) create a combined file and hash it
Get-ChildItem .\modules\secure_s3 -Recurse -Filter *.tf | Sort-Object FullName | ForEach-Object { Get-Content $_.FullName } | Out-File combined.txt -Encoding utf8
certutil -hashfile combined.txt SHA256 > gen.hash1.txt

# 2) regenerate in StackGen
# 3) repeat and compare the two certutil outputs

If hashes match → generation is deterministic (good). If they differ, StackGen may include timestamps, metadata, or use non-deterministic templates, report to the Team and include the StackGen generation ID.

7) Use StackGen quick-fixes and inline diagnostics

Hover the diagnostic in the editor for an explanation.
Click the quick-fix lightbulb (or Ctrl+.) to apply suggested fixes (e.g., add required KMS key param from org presets).
If the output includes a warning about missing org tags, use the extension action to populate from your org policy.

CI/CD Integration Patterns with StackGen-Authored Modules

1. StackGen + GitHub Actions Example

Once a module is generated via the StackGen Extension, treat it like any other IaC code, check it into Git. A common pattern is to run terraform init + terraform validate (or opentofu validate) every time someone pushes or opens a PR. This catches drift or template changes before merging.

Sample validate-module.yml:


  



name: Validate StackGen Module

on:
  push:
    branches: [ main ]
  pull_request:

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Set up Terraform
        uses: hashicorp/setup-terraform@v3
        with:
          terraform_version: 1.8.4

      - name: Initialize module
        run: terraform init ./modules/s3_bucket

      - name: Validate module
        run: terraform validate ./modules/s3_bucket

      - name: Plan (optional)
        run: terraform plan -input=false -out=tfplan ./modules/s3_bucket

This validates the StackGen-authored module (for example, your main.tf S3 bucket module) on every push. You can also hook in OPA/Conftest or Checkov to enforce policy.

2. StackGen + GitLab CI/CD

In GitLab, use a similar pattern but in .gitlab-ci.yml. The goal is that every MR runs init + validate so reviewers see consistent, deterministic output before approving.

Sample .gitlab-ci.yml:


  



stages:
  - validate

validate_stackgen_module:
  stage: validate
  image: hashicorp/terraform:1.8.4
  script:
    - terraform init ./modules/s3_bucket
    - terraform validate ./modules/s3_bucket
    - terraform plan -input=false -out=tfplan ./modules/s3_bucket
  only:
    - merge_requests
    - main

This job runs inside GitLab’s pipeline, validates the module, and optionally generates a plan. Because StackGen modules are deterministic, the plan diff will be stable across runs, making MR reviews and approvals far less noisy.

Branching Hygiene and Governance with the Extension

Below are two of the StackGen features, which help with branching hygiene and governance with the Extension

1. Guardrails and Policy Enforcement

One of the differentiators of the StackGen Extension is that it doesn’t just generate Terraform/OpenTofu snippets; it applies guardrails before the code ever leaves your IDE. As you define a module, the extension runs its deterministic AI engine through a built-in policy layer. This layer can consume rules written in OPA/Rego-style syntax or JSON-based policies. For example, you can ship a rule such as:


  



package stackgen.s3

deny[msg] {
  input.resource_type == "aws_s3_bucket"
  not input.encryption_enabled
  msg := "S3 buckets must have encryption enabled"
}

The extension evaluates the generated module against these policies in real time. Violations show up as inline diagnostics (squiggly underlines, problems pane entries) in VS Code/Cursor. That means developers can remediate security/compliance issues before committing to Git, and platform teams don’t have to rely solely on pipeline-time checks.

Behind the scenes, these guardrails are enforced via a local policy engine bundled with the extension. It’s deterministic as well; the same code and same policies will always produce the same pass/fail results, so your CI/CD pipelines won’t suddenly start failing due to non-deterministic AI behavior.

2. Code Review Simplification

Because StackGen produces smaller, focused modules by default (e.g., a self-contained S3 bucket module instead of a monolithic storage.tf), your pull/merge requests are naturally shorter. Reviewers see a handful of resources with clear variable blocks and outputs instead of sprawling 400-line diffs.

The extension also annotates generated files with metadata such as “Generated by StackGen AI Module Editor vX.Y” and hashes of the prompt/config, which makes diffs easier to reason about. If you regenerate a module with a changed input, reviewers can tell exactly what changed, no hidden AI refactors.

In practice, this reduces review latency: reviewers can run terraform plan against the module knowing it’s deterministic and policy-compliant, and approve with confidence. Combined with your branching strategy (feature branches for each module, protected main), this approach raises the quality bar while keeping velocity high.

Tooling Comparison: StackGen vs Other Solutions

1. StackGen vs ControlMonkey

StackGen: deterministic, IDE-first, reproducible

StackGen’s extension sits directly in VS Code/Cursor so developers author modules without leaving their normal workflow. Its AI is bounded by deterministic templates and a policy layer, so given the same prompt, the output is byte-for-byte identical. This makes terraform plan outputs stable across runs and simplifies both CI/CD validation and compliance audits.

ControlMonkey: GenAI-based, non-deterministic, more corrections needed

ControlMonkey is more of a hosted SaaS that leans on a general-purpose generative model. The same input may produce different Terraform each time, which can introduce unpredictable diffs or manual clean-ups. It’s useful for brainstorming IaC snippets but less suited for production modules where reproducibility and reviewability are key.

2. StackGen vs Traditional Terraform Module Authoring

Manual module writing = slower onboarding

With raw Terraform/OpenTofu, teams typically copy existing modules, tweak variables, and manually wire outputs. New hires must learn HCL, provider nuances, and company guardrails before shipping anything. That leads to longer onboarding and higher risk of misconfigurations.

StackGen Extension = lower barrier, faster delivery

By bringing deterministic AI authoring into the IDE, StackGen lowers the skill barrier. Engineers describe what they need in structured prompts, the extension scaffolds compliant modules, and inline diagnostics enforce policies before commit. This shortens the module creation cycle from days to minutes and keeps everything in version control with predictable diffs.

Here’s a feature-comparison table:

Feature	StackGen Extension	ControlMonkey	Traditional Terraform Module Authoring
AI Engine	Deterministic, template-bounded AI built into VS Code/Cursor extension	Non-deterministic GenAI hosted SaaS	No AI; manual HCL writing
Reproducibility	Same input → identical module output (byte-for-byte)	Same input can yield different Terraform per run	100% manual, depends on developer consistency
IDE Integration	Deep VS Code/Cursor integration, inline diagnostics and policy checks	Web UI/SaaS; no direct IDE integration	Works in any editor but no built-in guardrails
Policy Guardrails	Built-in OPA/Rego-style checks before commit	Limited or pipeline-time only	External tools needed (OPA, tfsec, Checkov)
Diff and Review	Small, focused modules with metadata for easy review	Larger, less predictable diffs; more manual clean-up	Depends entirely on developer discipline
Onboarding Speed	New engineers can author compliant modules without deep HCL expertise	Requires learning both SaaS UI and Terraform basics	Steepest learning curve, full HCL + internal standards
Pipeline Behavior	Predictable plan/apply outcomes, easy CI/CD integration	Variable plan outputs; extra validation steps needed	Standard Terraform behavior; manual pipelines
Use Case Fit	Production IaC with compliance, reproducibility, and IDE-first workflows	Experimentation, one-off scripts, early prototyping	Established teams with heavy Terraform expertise

Migration Path: From Manual IaC to StackGen Extension: step-by-step

Below is an actionable, hands-on migration playbook you can run start-to-finish. It’s split into phases (prep → pilot → migrate → rollout → post-migration). After StackGen extension is installed, PAT configured, and you have a repo + CI access. Replace , and AWS/KMS values with your org values.

Phase 0: Prep (inventory and safety)

Create a migration branch and working dir


  



git checkout -b migration/stackgen-onboarding
mkdir -p stackgen-migration

Inventory existing modules and usages


  



# list module blocks in repo (portable)
git grep -n "module \"" > stackgen-migration/module-inventory.txt
# find root modules that reference module paths/sources
git grep -n "source = \"modules/" >> stackgen-migration/module-inventory.txt || true

Open stackgen-migration/module-inventory.txt and mark modules by priority (low-risk test modules first).

Backup state for each affected workspace


  



# run from the terraform root that uses the module
terraform init
terraform state pull > ../stackgen-migration/<workspace>-state-before.json

Store these backups in a safe place (not in repo). If using an S3 backend, ensure locking is enabled and coordinate with the team.

Phase 1: Configure StackGen for your repo

Connect StackGen to the repo and set org presets

In VS Code → StackGen panel → Workspace settings: point StackGen to your repo root (if asked), set default tags, required variables, and policy bundle path.
If your org uses a repo config file, create a config stub at repo root for reusable presets (example below: adapt to your org / StackGen config format):


  



# stackgen.config.yaml  (example / illustrative)
templates_path: modules/_templates
org_tags:
  owner: infra
  team: platform
policy_bundle: policies/

Push a small policy bundle (OPA/Rego) into policies/


  



package stackgen.s3

deny[msg] {
  input.kind == "aws_s3_bucket"
  not input.server_side_encryption_configuration
  msg := "S3 buckets must have server-side encryption"
}

StackGen will surface violations inline during generation.

Phase 2: Pilot (generate, validate, prove determinism)

Pick one low-risk module to pilot


  



git checkout -b feat/stackgen/pilot-custom-bucket

Generate module via StackGen in VS Code

Use StackGen panel → New Module → select S3 template → fill fields.
Save generated files to modules/custom_bucket/.

Add deterministic metadata file


  



# Linux/macOS
MODULE_PATH=modules/custom_bucket
HASH=$(find $MODULE_PATH -type f -name '*.tf' -print | sort | xargs cat | sha256sum | cut -d' ' -f1)
echo $HASH > $MODULE_PATH/GENERATION.hash

Commit these files:


  



git add modules/custom_bucket
git commit -m "chore(module): add custom_bucket (generated by StackGen) gen:$HASH"
git push -u origin feat/stackgen/pilot-custom-bucket

Run local verification (in VS Code terminal)


  



cd modules/custom_bucket
terraform init -backend=false
terraform fmt -check
terraform validate
# optional local security checks
tflint --init && tflint
tfsec .
checkov -d .

Prove determinism


  



NEW_HASH=$(find $MODULE_PATH -type f -name '*.tf' -print | sort | xargs cat | sha256sum | cut -d' ' -f1)
test "$HASH" = "$NEW_HASH" && echo "deterministic: OK" || echo "NOT deterministic"

If hashes differ, inspect generated files for timestamps or comments; request StackGen to disable variable timestamps or include a stable signature.

Phase 3: CI gate and PR patterns

Add a PR validation workflow (example GitHub Action)


  



name: Validate StackGen Module
on:
  pull_request:
    paths:
      - 'modules/**'

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Verify generation hash
        run: |
          MODULE=modules/custom_bucket
          HASH=$(find $MODULE -type f -name '*.tf' -print | sort | xargs cat | sha256sum | cut -d' ' -f1)
          echo "computed=$HASH"
          echo "committed=$(cat $MODULE/GENERATION.hash || echo '')"
          if [ -f $MODULE/GENERATION.hash ]; then
            if [ "$HASH" != "$(cat $MODULE/GENERATION.hash)" ]; then
              echo "ERROR: generation hash mismatch" && exit 1
            fi
          fi

      - name: Setup Terraform
        uses: hashicorp/setup-terraform@v3
        with:
          terraform_version: '1.8.4'

      - name: Terraform init & validate
        run: terraform init $MODULE && terraform validate $MODULE

      - name: Run tflint
        run: |
          curl -fsSL https://raw.githubusercontent.com/terraform-linters/tflint/master/install_linux.sh | bash
          tflint --init && tflint
        shell: bash
        working-directory: .
      - name: Run security scans
        run: tfsec $MODULE || true

This ensures that the generated code is validated and the generation hash matches the committed hash.

Conclusion

The StackGen AI-Based Module Editor with its VS Code extension gives infrastructure teams an IDE-first, deterministic way to build Terraform and OpenTofu modules. Instead of switching between a browser, command line, and code review tools, engineers can generate, validate, and enforce policy directly inside their editor. By gradually migrating from manual HCL authoring to StackGen-authored modules, teams shorten lead time, improve consistency, and get predictable CI/CD outcomes.

Because the extension sits on top of a deterministic AI engine (not a black-box text generator), you gain reproducibility and compliance guardrails that align with enterprise IaC standards. Combined with automated validation workflows, this reduces onboarding friction and review fatigue, especially in large organisations with strict policies. Give it a try yourself.

FAQs

Q1. How is StackGen’s AI different from ChatGPT-like GenAI tools?

StackGen’s engine is template-bounded and deterministic. Given the same inputs and policies it always produces byte-identical modules. ChatGPT-style GenAI is free-form and can output slightly different code on each run, which makes compliance and reproducibility harder.

Q2. Can I still edit the generated module code manually?

Yes. Generated modules are plain HCL files in your repo. You can edit them, add variables, or refactor just like a hand-written module. However, if you change code outside the generator’s control, the generation hash in CI will differ; teams often run StackGen’s “regenerate” to reconcile changes.

Q3. Does the extension support both Terraform and OpenTofu?

It does. Under the hood, the generator outputs standard HCL that is valid for Terraform or OpenTofu. You can select which engine to target in the extension’s settings or your stackgen.config.yaml.

Q4. Is this extension suitable for large enterprise IaC practices?

Yes. The determinism, built-in policy bundle, and CI integration make it fit for enterprise governance. You can point it at a central template repository, enforce OPA/Rego guardrails, and embed generation hash checks into your pipelines for full auditability across hundreds of modules.

About StackGen:

StackGen is the pioneer in Autonomous Infrastructure Platform (AIP) technology, helping enterprises transition from manual Infrastructure-as-Code (IaC) management to fully autonomous operations. Founded by infrastructure automation experts and headquartered in the San Francisco Bay Area, StackGen serves leading companies across technology, financial services, manufacturing, and entertainment industries.

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