Terrashark

Abstract

1. Introduction

LLMs hallucinate a lot when it comes to Terraform. This skill fixes it. It includes best practices for Terraform and OpenTofu - good, bad, and neutral examples so the AI avoids common mistakes. Using TerraShark, the AI keeps proven practices in mind, eliminates hallucinations, and defaults to modular, reusable, security-first design.

Most Terraform skills dump huge text-of-walls onto the agent and burn expensive tokens - with no upside. LLMs don't need the entire Terraform docs again. TerraShark was aggressively de-duplicated and optimized for maximum quality per token.

The #1 Terraform skill for Claude Code and Codex, measured by GitHub stars.

2. Objective

Terraform Skill for Claude Code and Codex. LLMs hallucinate a lot with Terraform - TerraShark fixes this. It eliminates hallucinations, is designed for modular and secure code and grounds your IaC in the official Hashicorp Terraform best practices.

This project demonstrates how AWS can be applied to a real-world Blockchain & Cloud Computing problem.

4. Technology Stack

• Failure-mode workflow: TerraShark does not just give the agent Terraform facts. It forces the agent to identify the likely failure mode first, then load the exact reference material needed for that risk.
• Output contract: TerraShark responses include assumptions, remediation choices, tradeoffs, validation steps, and rollback notes. Other skills leave that structure to the model.
• Token efficiency: TerraShark keeps the activation path tiny and moves depth into focused references. This gives the agent the right Terraform context without turning every request into a large reference dump.
• Compliance mapping: TerraShark includes explicit mappings for ISO 27001, SOC 2, FedRAMP, GDPR, PCI DSS, and HIPAA. Other skills focus more on scanners than audit-ready control mapping.
• Trusted module awareness: TerraShark knows when to prefer mature vendor and community modules for AWS, Azure, GCP, Oracle Cloud, and IBM Cloud, reducing the surface area for hallucinated raw resources.
• LLM-specific hallucination prevention: TerraShark is designed around the ways AI agents fail at infrastructure code: unstable identity, leaked secrets, wide blast radius, CI drift, and weak compliance gates.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/LukasNiessen/terrashark.git
cd terrashark

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add a CI/CD pipeline with GitHub Actions
• Deploy to a public test network or cloud free tier
• Add monitoring and cost alerts

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Why does this problem need a blockchain or cloud-native design?
2. Explain the smart contract / infrastructure components and how they interact.
3. How are gas costs or cloud costs kept under control?
4. How is the system secured (keys, IAM, access control)?
5. How would the solution scale to many more users?

9. Source Code & License

This project is developed by LukasNiessen and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.