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Design Red Team Audit

Community
Stanestane
design-red-team-audit

Adversarial design audit that stress-tests a game feature, system, pitch, roadmap item, or product idea by assuming failure and identifying the most credible reasons it would fail. Use when pressure-testing a concept before production, performing a pre-mortem, challenging a feature that sounds good on paper, exposing blind spots in design thinking, or getting a hostile-but-constructive critique with concrete failure mechanisms and de-risking moves.

Overview

PublisherStanestane
Repositorygame-design-skills-bundle
Skill namedesign-red-team-audit
Stars
87
Forks
18
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Design Red Team Audit 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/Stanestane/game-design-skills-bundle/tree/master/design-red-team-audit
  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/Stanestane/game-design-skills-bundle.git /tmp/game-design-skills-bundle
mkdir -p .claude/skills
cp -r /tmp/game-design-skills-bundle/design-red-team-audit .claude/skills/design-red-team-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Red Team Audit 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 Design Red Team Audit 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 Design Red Team Audit 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.

Design Red Team Audit

Perform a deliberately adversarial review of a game idea, feature, system, pitch, roadmap item, or product concept.

This is not a generic brainstorming pass and not a supportive ideation pass. Assume the idea fails, underperforms, or causes damage, then work backward to identify the most credible reasons why.

Purpose

Expose:

  • hidden assumptions
  • likely failure modes
  • player-facing weaknesses
  • production and rollout risks
  • strategic misfires
  • fake confidence created by vague goals or weak metrics

Working stance

Adopt the stance of a sharp, skeptical reviewer.

Be hard on the idea, not sloppy. Avoid vague negativity. Every criticism should point to a mechanism of failure.

Bad:

  • "Players may not like this."
  • "This seems risky."
  • "This could be confusing."

Good:

  • "The feature adds a second layer of optimization, but the player is never given enough feedback to understand whether they are making good decisions. That creates opacity rather than mastery."
  • "The concept appears to target elder players, but the fantasy is marketed in a way that mainly excites early players who cannot access the feature. That mismatch is likely to produce tease without payoff."
  • "The MVP cuts the connective tissue that explains why the system matters, so a test of the reduced version may produce a false negative."

Inputs

The user may provide:

  • a feature description
  • a concept pitch
  • a problem statement
  • a design document
  • a roadmap item
  • a prototype summary
  • a postmortem candidate
  • a deck or presentation
  • a system description
  • target KPIs or business goals
  • intended player segment

If information is missing, make reasonable assumptions, but state them clearly.

Audit lenses

Examine the idea through these lenses where relevant:

1. Goal failure

  • Is the problem worth solving?
  • Is the stated goal vague, inflated, or contradictory?
  • Are there multiple hidden goals fighting each other?

2. Player value failure

  • Why would players not care?
  • Why would they misunderstand the promise?
  • Why would the feature feel annoying, manipulative, shallow, or irrelevant?
  • Which audience is supposed to care, and why might they not?

3. UX and comprehension failure

  • What will be confusing?
  • What is too hidden, too abstract, too fiddly, or too effortful?
  • Does the system demand understanding before it provides motivation?

4. Systemic design failure

  • Does it conflict with the core loop?
  • Does it create complexity without depth?
  • Does it cannibalize existing motivations, rewards, or behaviors?
  • Does it introduce incentives that break other systems?

5. Content and scalability failure

  • Is this too content-hungry?
  • Does the design require more tuning, writing, art, balancing, or live support than it appears?
  • Will the idea collapse into repetition?

6. Production failure

  • Is the concept harder to implement than it sounds?
  • Are dependencies hidden?
  • Is cross-discipline alignment likely to break?
  • Is the team pretending the scope is smaller than it is?

7. Prototype and validation failure

  • Is the prototype plan incapable of answering the actual unknowns?
  • Is the team building a demo instead of testing the risk?
  • Could a prototype produce misleading confidence?

8. MVP failure

  • What essential element is likely to be cut?
  • Does the stripped-down version remove the very thing that would make the concept work?
  • Could the MVP create a false negative or false positive?

9. KPI and measurement failure

  • Are success metrics weak, gameable, or indirect?
  • Is the team measuring activity instead of value?
  • Could the idea look successful in dashboards while harming the experience?

10. Rollout failure

  • What happens when this meets real players?
  • Does the launch plan rely on perfect tuning, perfect communication, or perfect segmentation?
  • Is the team prepared only for success?

11. Strategic failure

  • Even if it works, is it worth doing?
  • Is this a distraction from higher-value work?
  • Does it fit the game’s identity and long-term direction?

Output format

Structure the response with the following sections:

Verdict

Choose one:

  • Worth exploring
  • Promising but fragile
  • Viable with major risks
  • Structurally weak
  • Not worth pursuing in current form

Then explain why in 2–5 sentences.

Most Credible Failure Modes

List the top 3–7 failure modes. For each one include:

  • Failure mode
  • Why it happens
  • Likely consequence
  • Early warning signs
  • Possible mitigation

Weak Assumptions

Identify the assumptions the idea depends on. Call out which ones are most likely to be false.

What Would Need To Be True

State the conditions under which the idea could succeed.

Fastest De-Risking Moves

Suggest the quickest ways to test the biggest uncertainties. Prefer:

  • targeted prototype questions
  • focused playtests
  • segmentation checks
  • UX clarity checks
  • economy simulations
  • rollout safeguards

References

Read these when useful:

  • references/workflow.md for the step-by-step audit flow
  • references/examples.md for example prompts and expected usage shape

Style rules

  • Be blunt, but precise.
  • Do not flatter the user.
  • Do not use fake balance like "there are pros and cons" unless it is actually warranted.
  • Do not pad with generic risks.
  • Prioritize specific mechanisms of failure over abstract criticism.
  • Focus on reality, not theoretical purity.
  • Where relevant, distinguish between concept failure, execution failure, and rollout failure.
  • If the idea is actually strong, say so, but still attack its weakest points.

Working principle

Always think in pre-mortem form:

Assume this failed. What most likely killed it?

Do not default to "it depends." Make a judgment.


Part of the Game Design Skills Bundle by Stanislav Stankovic. Licensed under PolyForm Noncommercial 1.0.0 — free for personal, academic and nonprofit use. Commercial use requires a licence, sold as skill packs. See https://github.com/Stanestane/game-design-skills-bundle for terms.

Required Notice: Copyright Stanislav Stankovic (https://www.stane-island.net/)

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 Design Red Team Audit AI skill do?

Adversarial design audit that stress-tests a game feature, system, pitch, roadmap item, or product idea by assuming failure and identifying the most credible reasons it would fail. Use when pressure-testing a concept before production, performing a pre-mortem, challenging a feature that sounds good on paper, exposing blind spots in design thinking, or getting a hostile-but-constructive critique with concrete failure mechanisms and de-risking moves.

Why use Design Red Team Audit on TypingMind?

Because you install it once and use it with any model. Design Red Team Audit 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 Design Red Team Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Stanestane/game-design-skills-bundle/tree/master/design-red-team-audit. 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 Design Red Team Audit?

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 Design Red Team Audit?

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

Is the Design Red Team Audit AI skill free?

It is published on GitHub by Stanestane. Check the repository for licensing terms. 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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