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Game Design Big Five Personality Audit

Community
Stanestane
game-design-big-five-personality-audit

Audit a game, feature, progression system, social system, live-ops loop, onboarding flow, monetization surface, or multiplayer space through the lens of the Big Five personality traits (OCEAN): Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Use when evaluating which personality-style preferences a design feels comfortable for, which kinds of players it energizes or exhausts, how much structure, novelty, social intensity, conflict, or emotional pressure it creates, or when you need a personality-fit lens that is different from archetype or motivation-segment frameworks.

Overview

PublisherStanestane
Repositorygame-design-skills-bundle
Skill namegame-design-big-five-personality-audit
Stars
87
Forks
18
Bundled files
1
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.

  • 1 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 Big Five Personality 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-big-five-personality-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-big-five-personality-audit .claude/skills/game-design-big-five-personality-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Design Big Five Personality 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 Big Five Personality 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 Big Five Personality 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 Big Five Personality Audit

Audit a design by asking which OCEAN trait expressions it feels comfortable for, rewards, strains, or quietly repels.

Use this skill to evaluate how a game, feature, flow, or system fits different personality-style preferences using the Big Five: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism.

Treat these as fit lenses, not diagnostic labels.

Core principle

This is not about pretending a game can be reverse-engineered into a clinical personality test.

The useful question is simpler:

  • what kind of novelty appetite does this design reward?
  • what kind of structure tolerance does it demand?
  • what level of social intensity does it assume?
  • how cooperative or adversarial does it feel?
  • how emotionally rough, stressful, or safe is it?

Those questions map surprisingly well onto OCEAN-style differences.

Start with the reference notes

Before auditing, read references/big-five-notes.md.

That file contains the trait summaries, game-design interpretation rules, and anti-pattern warnings for using OCEAN responsibly.

What to produce

Generate:

  1. Audit target - what is being reviewed and what it is supposed to do
  2. Trait-fit readout - how the design lands for each Big Five trait dimension
  3. Comfort and strain profile - who is likely to feel naturally at home versus overloaded or alienated
  4. Cross-trait tensions - where serving one trait expression harms another
  5. Design implications - what the design is really asking players to tolerate or enjoy
  6. Recommendations - what to strengthen, soften, separate, or clarify

Process

1. Define the audit target

Clarify:

  • what is being audited
  • what role it plays in the product
  • whether it is core, optional, onboarding, progression, monetization, social, competitive, or endgame
  • whether the design needs broad accessibility or can be narrow and demanding

Write:

  • Design being audited
  • Intended job
  • Scope
  • Breadth requirement

2. Audit for Openness

Treat this as the novelty, curiosity, ambiguity, and imagination lens.

Ask:

  • Does the design reward curiosity, experimentation, or unusual combinations?
  • Is there room for self-expression, aesthetic appreciation, or discovery?
  • Does it support multiple paths, interpretations, or play styles?
  • Does it punish deviation from the obvious route?
  • Is novelty meaningful, or just noisy and confusing?

Look for:

  • discovery
  • emergent interactions
  • expressive customization
  • aesthetic richness
  • non-obvious strategies
  • mystery and experimentation

Interpretation:

  • High-openness fit usually means the design feels rich, curious, and rewarding to explore
  • Lower-openness fit usually means the design is more familiar, explicit, stable, and convention-friendly

3. Audit for Conscientiousness

Treat this as the structure, planning, discipline, and completion lens.

Ask:

  • Does the design reward planning, consistency, optimization, and order?
  • Are goals, tasks, and systems legible enough to support disciplined play?
  • Does the design create satisfying structure or oppressive maintenance burden?
  • Are checklists and routines rewarding or exhausting?
  • How much does the experience punish inconsistency or sloppy play?

Look for:

  • routine loops
  • optimization pressure
  • collection/completion structures
  • schedules and timers
  • ordered progression
  • maintenance burden

Interpretation:

  • High-conscientiousness fit often means the design rewards planning and follow-through
  • Lower-conscientiousness fit often means the design is more improvisational, loose, forgiving, or anti-obligation

4. Audit for Extraversion

Treat this as the social intensity, energy, visibility, and stimulation lens.

Ask:

  • Does the design come alive around other people?
  • Does it reward visibility, talkativeness, presence, or leadership?
  • Is there a lot of social energy, noise, or activity?
  • Does it over-demand participation from players who would rather engage quietly or solo?
  • Can introversion-style players engage meaningfully without feeling socially crowded?

Look for:

  • multiplayer dependence
  • group rituals
  • chat and voice usage
  • social visibility
  • team roles
  • high-arousal action

Interpretation:

  • High-extraversion fit often means lively, visible, socially energizing play
  • Lower-extraversion fit often means quieter autonomy, optional social contact, or asynchronous sociality

5. Audit for Agreeableness

Treat this as the cooperation, trust, harmony, and interpersonal harshness lens.

Ask:

  • Does the design reward helping, cooperation, support, or prosocial behavior?
  • Does it create conflict, betrayal, humiliation, or harsh comparison?
  • How safe does social interaction feel?
  • Does the design assume players enjoy adversarial pressure?
  • Are players encouraged to care for others, or to exploit them?

Look for:

  • co-op
  • gifting/helping
  • supportive roles
  • trust structures
  • betrayal mechanics
  • griefing potential
  • dominance incentives

Interpretation:

  • High-agreeableness fit often means warm, cooperative, low-cruelty design
  • Lower-agreeableness fit may mean sharper rivalry, cutthroat play, or socially aggressive energy

6. Audit for Neuroticism

Treat this as the stress sensitivity, punishment tolerance, and emotional volatility lens.

Ask:

  • How punishing, humiliating, uncertain, or stressful is the design?
  • How much loss aversion, FOMO, or social evaluation pressure does it create?
  • Are mistakes recoverable?
  • Does failure teach calmly or slap the player emotionally?
  • Does the design offer safety, reassurance, clarity, or soothing routines where needed?

Look for:

  • punishment severity
  • scarcity anxiety
  • public failure exposure
  • opaque systems
  • recovery speed
  • emotional safety nets
  • comfort-loop potential

Interpretation:

  • Higher-neuroticism fit usually requires more reassurance, predictability, and survivable failure
  • Lower-neuroticism fit can tolerate harsher setbacks, ambiguity, and pressure without emotional collapse

7. Build the trait-fit profile

For each trait dimension, describe:

  • who is more comfortable
  • who is more strained
  • what exact design property causes that fit or strain

Use this format:

TraitHigh-expression fitLow-expression fitMain evidenceMain risk
Openness............
Conscientiousness............
Extraversion............
Agreeableness............
Neuroticism............

8. Identify cross-trait tensions

Look for cases where serving one trait expression harms another.

Common tensions:

  • Novelty vs clarity - openness-serving depth creates confusion for lower-openness players
  • Structure vs freedom - conscientiousness-serving order suffocates players who want looser improvisation
  • Social energy vs quiet autonomy - extraversion-serving visibility crowds lower-extraversion players
  • Competition vs harmony - lower-agreeableness conflict energy poisons high-agreeableness comfort
  • Pressure vs safety - low-neuroticism challenge intensity crushes high-neuroticism players

Ask:

  • Which tension is intentional?
  • Which tension is accidental?
  • Does the feature need to choose a side, or should it separate modes more cleanly?

9. Convert the audit into design implications

Translate findings into practical design language.

Examples:

  • if the design strongly favors high conscientiousness, ask whether maintenance burden is too high for broader audiences
  • if the design strongly favors high openness, ask whether onboarding gives enough grounding for less exploratory players
  • if the design strongly favors extraversion, ask whether solo or asynchronous alternatives exist
  • if the design strongly favors low agreeableness, ask whether toxicity or social cruelty is becoming part of the product identity
  • if the design strongly disfavors higher neuroticism, ask whether failure, ranking, or FOMO pressure is harsher than necessary

10. Recommend changes

For each major issue, specify:

  • Trait dimension affected
  • Current issue
  • Design cause
  • Suggested change
  • Expected effect

Prefer recommendations that:

  • preserve the intended audience
  • reduce accidental exclusion
  • separate incompatible needs when one system cannot serve both
  • make emotional cost visible instead of pretending the design is universally comfortable

Response structure

Audit Target

  • ...

OCEAN Readout

  • Openness: ...
  • Conscientiousness: ...
  • Extraversion: ...
  • Agreeableness: ...
  • Neuroticism: ...

Trait-Fit Profile

  • ...

Cross-Trait Tensions

  • ...

Design Implications

  • ...

Recommendations

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

Fast mode

  • What kind of novelty appetite does this design assume?
  • How much structure and maintenance burden does it demand?
  • How socially loud is it?
  • How cooperative versus adversarial is it?
  • How emotionally harsh or safe is it?
  • Which trait expression is most strongly favored?
  • Which trait expression is most likely to bounce?

Working principle

Some games fail not because they are bad, but because they quietly assume the wrong kind of person will enjoy their pressure, noise, ambiguity, or obligations.

Use OCEAN to make those assumptions visible.


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 Big Five Personality Audit AI skill do?

Audit a game, feature, progression system, social system, live-ops loop, onboarding flow, monetization surface, or multiplayer space through the lens of the Big Five personality traits (OCEAN): Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Use when evaluating which personality-style preferences a design feels comfortable for, which kinds of players it energizes or exhausts, how much structure, novelty, social intensity, conflict, or emotional pressure it creates, or when you need a personality-fit lens that is different from archetype or motivation-segment frame...

Why use Game Design Big Five Personality Audit on TypingMind?

Because you install it once and use it with any model. Game Design Big Five Personality 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 Big Five Personality 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-big-five-personality-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 Big Five Personality 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 Big Five Personality Audit?

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

Is the Game Design Big Five Personality 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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