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Game Design Prototype Intent Audit

Community
Stanestane
game-design-prototype-intent-audit

Audit a game feature, system concept, prototype plan, or preproduction proposal to determine whether the prototype is meant to sell the idea or reveal unknowns, and whether the prototype scope matches that intent. Use when teams are unclear about why they are prototyping, when a prototype risks becoming a demo in disguise, or when precious prototype time may be spent proving known strengths instead of testing real uncertainties.

Overview

PublisherStanestane
Repositorygame-design-skills-bundle
Skill namegame-design-prototype-intent-audit
Stars
87
Forks
18
Bundled files
3
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.

  • 3 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 Game Design Prototype Intent 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/game-design-prototype-intent-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/game-design-prototype-intent-audit .claude/skills/game-design-prototype-intent-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Design Prototype Intent 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 Game Design Prototype Intent 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 Game Design Prototype Intent 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.

Game Design Prototype Intent Audit

Decide what the prototype is for before deciding what the prototype should contain.

Use this skill to audit whether a prototype is being built to convince stakeholders that an idea works, or to reveal the least-understood parts of the design. These two goals require different prototype choices. Confusing them is one of the most common ways to waste prototype time.

Read references/prototype-modes.md when classifying the prototype intent. Read references/unknowns-checklist.md when identifying what a learning prototype should target. Read references/failure-patterns.md when diagnosing common prototype mistakes.

What to produce

Produce:

  1. Prototype read - what is being prototyped and why
  2. Intent diagnosis - whether this is a selling demo, a learning prototype, or a confused hybrid
  3. Scope fit - whether the prototype contents match the stated intent
  4. Unknowns coverage - what the prototype is or is not actually testing
  5. Risk diagnosis - what time or learning may be wasted under the current plan
  6. Recommendation - what to prototype instead, emphasize, cut, or sequence differently

Process

1. Clarify the prototype ask

Ask:

  • what idea, feature, or concept is being prototyped?
  • who is the prototype for?
  • what decision is the prototype supposed to unlock?
  • what would count as success for the prototype itself?

2. Diagnose intent

The prototype usually serves one dominant purpose:

  • Selling demo - prove the concept is exciting, legible, or worth greenlighting
  • Learning prototype - reveal unknowns and answer risky design questions
  • Confused hybrid - trying to sell the idea while also pretending to test unknowns, without doing either well

3. Check scope fit

Ask whether the implementation focus matches the intent. For example:

  • selling demos usually emphasize strengths, clarity, and presentability
  • learning prototypes should target unclear, risky, or least-understood aspects

4. Audit unknowns

If the team claims the prototype is for learning, identify:

  • what specific unknowns are being tested
  • whether the prototype can actually answer them
  • what it is avoiding or glossing over

5. Diagnose failure mode

Common failures include:

  • demo dressed up as learning
  • prototype solving the easiest part, not the riskiest part
  • too much polish hiding too little insight
  • trying to answer too many questions at once
  • building broad slices when one narrow uncertainty should be isolated

6. Recommend a better prototype move

Possible recommendations:

  • turn it honestly into a selling demo
  • strip it down into a learning prototype
  • split one prototype into two sequential prototypes
  • narrow the question set
  • focus on the riskiest unknown first

Response structure

Prototype Read

  • ...

Intent Diagnosis

  • ...

Scope Fit

  • ...

Unknowns Coverage

  • ...

Risk Diagnosis

  • ...

Recommendation

  • ...

Fast mode

  • Who is this prototype for?
  • Is it trying to sell the idea or learn something?
  • What unknowns are actually being tested?
  • What is being polished that does not answer the real question?
  • What is the better prototype plan?

Style rules

  • Be explicit about the dominant intent.
  • Do not flatter vague prototype plans.
  • Prefer one answered question over many implied ones.
  • If the prototype is really a demo, say so cleanly.
  • If the prototype is not testing the true risk, point that out directly.

Working principle

A prototype is not automatically useful because it exists. Its value comes from whether it is built for the right purpose and judged by the right standard.


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 Game Design Prototype Intent Audit AI skill do?

Audit a game feature, system concept, prototype plan, or preproduction proposal to determine whether the prototype is meant to sell the idea or reveal unknowns, and whether the prototype scope matches that intent. Use when teams are unclear about why they are prototyping, when a prototype risks becoming a demo in disguise, or when precious prototype time may be spent proving known strengths instead of testing real uncertainties.

Why use Game Design Prototype Intent Audit on TypingMind?

Because you install it once and use it with any model. Game Design Prototype Intent 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 Game Design Prototype Intent Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Stanestane/game-design-skills-bundle/tree/master/game-design-prototype-intent-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 Game Design Prototype Intent 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 Game Design Prototype Intent Audit?

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

Is the Game Design Prototype Intent 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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