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Terrashark

Community
LukasNiessen
terrashark

Prevent Terraform/OpenTofu hallucinations by diagnosing and fixing failure modes: identity churn, secret exposure, blast-radius mistakes, CI drift, and compliance gate gaps. Use when generating, reviewing, refactoring, or migrating IaC and when building delivery/testing pipelines.

Overview

PublisherLukasNiessen
Repositoryterrashark
Skill nameterrashark
Stars
715
Forks
27
Bundled files
58
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 58 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LukasNiessen on GitHub. Read the source before you install it.

Installation

Install the Terrashark AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LukasNiessen/terrashark/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LukasNiessen/terrashark.git \
  .claude/skills/terrashark
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Terrashark in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Terrashark on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Terrashark is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Terrashark: Failure-Mode Workflow for Terraform/OpenTofu

Run this workflow top to bottom.

1) Capture execution context

Record before writing code:

  • runtime (terraform or tofu) and exact version
  • provider(s), target platform, and state backend
  • execution path (local CLI, CI, HCP Terraform/TFE, Atlantis)
  • environment criticality (dev/shared/prod)

If unknown, state assumptions explicitly.

2) Diagnose likely failure mode(s)

Select one or more based on user intent and risk:

  • identity churn: resource addressing instability, refactor breakage
  • secret exposure: secrets in state, logs, defaults, artifacts
  • blast radius: oversized stacks, weak boundaries, unsafe applies
  • CI drift: version mismatch, unreviewed applies, missing artifacts
  • compliance gate gaps: missing policies/approvals/audit controls

3) Load only the relevant reference file(s)

Primary references:

  • references/identity-churn.md
  • references/secret-exposure.md
  • references/blast-radius.md
  • references/ci-drift.md
  • references/compliance-gates.md

Supplemental references (only when needed):

  • references/testing-matrix.md
  • references/quick-ops.md
  • references/examples-good.md
  • references/examples-bad.md
  • references/examples-neutral.md
  • references/coding-standards.md
  • references/module-architecture.md
  • references/ci-delivery-patterns.md
  • references/security-and-governance.md
  • references/do-dont-patterns.md
  • references/mcp-integration.md

Conditional references (CRR; load only on detected signals):

  • references/conditional/backend-state-safety.md (backend is s3, azurerm, gcs, remote, cloud, pg, consul, or local, or task mentions backend migration, locking, state backup, or restore)
  • references/conditional/trusted-modules.md (provider is aws, azurerm, google, oci, or ibm)

Do not load multiple conditional references unless the task spans multiple detected backends, providers, or tools.

4) Propose fix path with explicit risk controls

For each fix, include:

  • why this addresses the failure mode
  • what could still go wrong
  • guardrails (tests, approvals, rollback)

5) Generate implementation artifacts

When applicable, output:

  • HCL changes (typed vars, stable keys, bounded versions)
  • migration blocks (moved, import strategy)
  • CI pipeline updates (plan/apply separation, artifacts, policy checks)
  • compliance controls (approvals, policy rules, evidence paths)

When a trusted registry module covers the requested resource and the user has not asked for raw HCL, default to that module with an exact version pin (see references/conditional/trusted-modules.md).

6) Validate before finalize

Always provide command sequence tailored to runtime and risk tier. Never recommend direct production apply without reviewed plan and approval.

7) Output contract

Return:

  • assumptions and version floor
  • selected failure mode(s)
  • chosen remediation and tradeoffs
  • validation/test plan
  • rollback/recovery notes for destructive-impact changes

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Terrashark AI skill do?

Prevent Terraform/OpenTofu hallucinations by diagnosing and fixing failure modes: identity churn, secret exposure, blast-radius mistakes, CI drift, and compliance gate gaps. Use when generating, reviewing, refactoring, or migrating IaC and when building delivery/testing pipelines.

Why use Terrashark on TypingMind?

Because you install it once and use it with any model. Terrashark is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Terrashark in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LukasNiessen/terrashark/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Terrashark?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Terrashark?

As many as you like. As long as a model supports skills, you can use Terrashark with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Terrashark AI skill free?

Yes. It is published on GitHub by LukasNiessen under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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