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Game Design Prototyping Companion

Community
Stanestane
game-design-prototyping-companion

Track game design prototype ideas, branching outcomes, dead ends, baselines, and next experiments, and optionally generate a simple SVG visualization of prototype evolution. Use when a prototype leads to several possible follow-up paths, when the team needs to backtrack without losing learned branches, when exploring unknowns through multiple iterations, or when you want both a written prototype log and a branch map of how the concept evolved over time.

Overview

PublisherStanestane
Repositorygame-design-skills-bundle
Skill namegame-design-prototyping-companion
Stars
87
Forks
18
Bundled files
4
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.

  • 4 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 Prototyping Companion 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-prototyping-companion
  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-prototyping-companion .claude/skills/game-design-prototyping-companion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Design Prototyping Companion 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 Prototyping Companion 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 Prototyping Companion 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 Prototyping Companion

Track prototype evolution, not just prototype results.

Use this skill when prototyping produces branching decisions, dead ends, alternative paths, and backtracking. The aim is to preserve learning structure: what was tested, what was learned, what branch it created, which path was followed, and which paths remain available to revisit.

This skill can also generate a simple SVG branch map from a lightweight text or JSON structure.

What to produce

Generate one or more of these outputs:

  1. Prototype log - what was tested and why
  2. Branch record - what paths emerged from the result
  3. Decision state - which branch is current, parked, dead, baseline, or promising
  4. Backtrack notes - what can be revisited later and under what condition
  5. SVG branch map - a visual map of prototype evolution

Core principle

A prototype is not just a yes/no answer. It often creates a tree:

  • one branch becomes the current path
  • another becomes a dead end
  • another becomes a parked idea worth revisiting later
  • another reveals a stronger question than the original one

The skill should preserve that tree.

Workflow

1. Define the prototype node

For each prototype node, record:

  • Node ID
  • Prototype name
  • Question being tested
  • What was built or simulated
  • What was learned
  • Result state

Use result states such as:

  • baseline
  • promising
  • branch trigger
  • dead end
  • parked
  • production candidate

2. Record branch options

When a prototype produces several next moves, capture each branch explicitly.

For each branch, record:

  • Branch ID
  • Parent node
  • New idea or variation
  • Reason it exists
  • Current status

3. Mark the chosen path without deleting the others

Do not treat the selected branch as the only meaningful output. Preserve:

  • abandoned paths
  • deferred paths
  • weird side paths
  • stronger substitute ideas revealed by the test

4. Add backtrack logic

If a branch is not chosen now, record:

  • what would justify revisiting it
  • what blocker currently prevents it
  • what later discovery might make it relevant again

5. Generate a visual map when useful

Use scripts/branch_map_svg.py to render a simple SVG from a branch-map JSON file.

Read:

  • references/branch-map-format.md for the input structure
  • references/example-branch-map.json for an example

Response structure

Use this structure unless the user asks for something else:

Prototype Node

  • Node ID: ...
  • Question: ...
  • Built / simulated: ...
  • Learned: ...
  • State: ...

Branches Created

  1. ...
  2. ...
  3. ...

Current Chosen Path

  • ...

Parked / Revisit Later

  • ...

Suggested Next Prototype

  • ...

Visualization workflow

When the user wants a visual branch map:

  1. write the branch data to JSON using the format in references/branch-map-format.md
  2. run scripts/branch_map_svg.py <input.json> <output.svg>
  3. return the SVG path and summarize what the map shows

Style rules

  • Preserve branching history.
  • Prefer explicit node IDs over vague prose.
  • Distinguish clearly between what was learned and what was merely assumed.
  • Do not erase dead ends; label them.
  • Do not confuse the current path with the best possible path forever.

References

  • references/branch-map-format.md for the JSON structure
  • references/example-branch-map.json for a starter example
  • references/state-labels.md for recommended branch/node labels

Working principle

Prototype trees are design memory. If you only remember the path you chose, you lose the intelligence of the paths you rejected.


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 Prototyping Companion AI skill do?

Track game design prototype ideas, branching outcomes, dead ends, baselines, and next experiments, and optionally generate a simple SVG visualization of prototype evolution. Use when a prototype leads to several possible follow-up paths, when the team needs to backtrack without losing learned branches, when exploring unknowns through multiple iterations, or when you want both a written prototype log and a branch map of how the concept evolved over time.

Why use Game Design Prototyping Companion on TypingMind?

Because you install it once and use it with any model. Game Design Prototyping Companion 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 Prototyping Companion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Stanestane/game-design-skills-bundle/tree/master/game-design-prototyping-companion. 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 Prototyping Companion?

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 Prototyping Companion?

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

Is the Game Design Prototyping Companion 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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