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Game Design Player Segment Perception Audit

Community
Stanestane
game-design-player-segment-perception-audit

Audit a game feature, live update, roadmap item, event, or content drop by how different player segments are likely to perceive it. Use when a feature is aimed at one cohort but visible to others, when update messaging may create excitement for players who cannot meaningfully access the feature, or when you need to understand how new, mid, and elder players will read the same feature differently.

Overview

PublisherStanestane
Repositorygame-design-skills-bundle
Skill namegame-design-player-segment-perception-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 Player Segment Perception 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-player-segment-perception-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-player-segment-perception-audit .claude/skills/game-design-player-segment-perception-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Design Player Segment Perception 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 Player Segment Perception 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 Player Segment Perception 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 Player Segment Perception Audit

Check not only who the feature is for, but who will see it, misunderstand it, resent it, or feel left out by it.

Use this skill to audit a feature through the eyes of different player segments. The goal is not to please every segment equally. The goal is to understand who the feature is actually serving, what other groups will perceive, and where update visibility, access, and expectation can create disappointment or wasted communication.

Read references/segment-layers.md when mapping the main audience groups. Read references/perception-failures.md when diagnosing common update-perception mistakes. Read references/recommendation-patterns.md when deciding how to adjust targeting, access, or messaging.

What to produce

Produce:

  1. Feature read - what the feature is and who it seems intended for
  2. Target segment read - which cohort is supposed to care most and why
  3. Cross-segment perception - how other player groups are likely to interpret it
  4. Access and visibility mismatch - who will hear about it versus who can actually use it
  5. Risk diagnosis - what disappointment, alienation, or wasted hype may result
  6. Recommendation - how to retarget, reframe, gate, soften, or broaden the feature rollout

Process

1. Clarify the feature and its intended audience

Ask:

  • what is being added or changed?
  • which player segment is the real target?
  • why would that segment care?
  • what specific value does the feature create for them?

2. Map the main player segments

At minimum consider:

  • new players
  • midgame players
  • elder/endgame players

Also consider when relevant:

  • free players
  • spenders
  • competitive players
  • collectors
  • social players
  • lapsed returners

3. Audit visibility versus access

Check:

  • who will see this feature in patch notes, ads, art, store pages, or UI surfaces?
  • who can actually access it soon?
  • who must wait, grind, pay, or reach a threshold first?
  • is the feature headline content for players who cannot meaningfully touch it?

4. Audit perception by segment

For each important segment ask:

  • will they care?
  • will they understand why it matters?
  • will they feel invited, excluded, indifferent, or misled?
  • does the update strengthen their sense of future aspiration or create disappointment?

5. Diagnose mismatch risk

Look for patterns such as:

  • excitement generated for the wrong audience
  • feature aimed at elder players but marketed broadly to everyone
  • midgame players ignored even though they are the pipeline to elder status
  • access friction turning aspiration into resentment
  • feature value obvious only to one cohort while others see noise

6. Recommend a better segment strategy

Possible moves:

  • target communications more narrowly
  • create smaller companion value for non-target segments
  • adjust unlock timing or access path
  • reduce visible hype for inaccessible content
  • provide aspiration framing without overpromising immediate relevance

Response structure

Feature Read

  • ...

Target Segment Read

  • ...

Cross-Segment Perception

  • ...

Access and Visibility Mismatch

  • ...

Risk Diagnosis

  • ...

Recommendation

  • ...

Fast mode

  • Who is this really for?
  • Who else will see it?
  • Who will care, who will not, and who will feel disappointed?
  • Is the visibility broader than the access?
  • What is the best fix: targeting, framing, or access?

Style rules

  • Do not assume one feature must serve all players equally.
  • Distinguish target audience from visible audience.
  • Pay special attention to middle cohorts, not just new and elder players.
  • Be concrete about disappointment mechanics, not just vague sentiment.
  • Treat aspiration carefully; it can motivate or alienate depending on access.

Working principle

A feature is not experienced only by the players who use it. It is also experienced by the players who see it, anticipate it, misunderstand it, or discover they are excluded from it.


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 Player Segment Perception Audit AI skill do?

Audit a game feature, live update, roadmap item, event, or content drop by how different player segments are likely to perceive it. Use when a feature is aimed at one cohort but visible to others, when update messaging may create excitement for players who cannot meaningfully access the feature, or when you need to understand how new, mid, and elder players will read the same feature differently.

Why use Game Design Player Segment Perception Audit on TypingMind?

Because you install it once and use it with any model. Game Design Player Segment Perception 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 Player Segment Perception 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-player-segment-perception-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 Player Segment Perception 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 Player Segment Perception Audit?

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

Is the Game Design Player Segment Perception 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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