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Shatter

Community
Hmbown
shatter

Shatter is proactive, controlled destruction for learning. You are the attacker against your own system. The goal is a brittleness report with remediation priorities.

Overview

PublisherHmbown
RepositoryWizards-of-the-Ghosts
Skill nameshatter
Stars
106
Forks
10
Bundled files
Instructions only
LicenseCC0-1.0
Links
Skill sourceRepository
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Shatter 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.
  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/Hmbown/Wizards-of-the-Ghosts.git /tmp/Wizards-of-the-Ghosts
mkdir -p .claude/skills
cp -r /tmp/Wizards-of-the-Ghosts/generated/hermes/containment-and-intervention/shatter .claude/skills/shatter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Shatter 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 Shatter 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 Shatter 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.

Shatter

Find the resonant frequency and break it on purpose.

What This Skill Does

Shatter is proactive, controlled destruction for learning. You are the attacker against your own system. The goal is a brittleness report with remediation priorities. In this grimoire, Shatter is treated as a metaphorical spell with a shipping-now delivery profile. Canonical reference input: Shatter (spell).

When To Use

  • Activate Shatter when the user wants to deliberately break, stress, or adversarially test a system they own to discover weaknesses before real failures occur. This is proactive brittleness discovery, not reactive incident response.

Prerequisites

  • No extra runtime dependencies beyond Hermes Agent and the normal toolset for this session.

Procedure

  1. Restate the target, the success condition, and any no-touch boundaries before taking action.
  2. Define blast radius: What is in scope? What must be protected? What is the rollback plan? Never test without containment boundaries.
  3. Identify resonant frequencies: What assumptions have never been tested? Where are the single points of failure? What edge cases are unexercised?
  4. Apply controlled force: Inject faults (kill nodes, drop packets, fill disks), generate adversarial inputs, simulate load spikes, or run game-day scenarios. One variable at a time unless testing cascade behavior.
  5. Observe failure modes: What broke? What bent gracefully? What held? Document thresholds, error messages, recovery behavior, and unexpected side effects.
  6. Deliver brittleness report: Ranked structural weaknesses, resilience scorecard (held/bent/shattered), and prioritized remediation recommendations.
  7. Package the result as the deliverables below, with confidence, assumptions, and unresolved risk called out explicitly.

Deliverables

  • A brittleness report: what broke, at what threshold, and what the failure mode looked like.
  • A prioritized list of structural weaknesses with remediation recommendations.
  • A resilience scorecard: what held up, what bent gracefully, and what shattered.

Pitfalls / Guardrails

  • Keep the metaphor anchored to a real mechanism instead of drifting into lore.
  • Only test systems you own or have explicit authorization to test
  • Always define blast radius and rollback plan before beginning
  • If real damage occurs, stop immediately and switch to incident response
  • Chaos engineering without containment is just chaos

Verification

  • Check that the result includes every deliverable promised above.
  • Check that confirmed facts, assumptions, and inferences are visibly separated.
  • Check that the metaphor still maps cleanly to a real operational mechanism.

Example Invocation

text
/shatter stress-test a system, plan, or codebase by finding its resonant frequencies — the assumptions, edge cases, and structural weaknesses that would break under real pressure

Frequently asked questions

What does the Shatter AI skill do?

Shatter is proactive, controlled destruction for learning. You are the attacker against your own system. The goal is a brittleness report with remediation priorities.

Why use Shatter on TypingMind?

Because you install it once and use it with any model. Shatter 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 Shatter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Hmbown/Wizards-of-the-Ghosts/tree/main/generated/hermes/containment-and-intervention/shatter. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Shatter?

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 Shatter?

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

Is the Shatter AI skill free?

Yes. It is published on GitHub by Hmbown under the CC0-1.0 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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