Game Design Review logo

Game Design Review

Community
Yuki001
game-design-review

Review, critique, compare, improve, or stress-test an existing game concept, GDD, mechanic, puzzle, economy, level, theme, emotional arc, interface, narrative, social feature, prototype, pitch, or playtest result. Use when the user asks what is weak, risky, unclear, unbalanced, contradictory, insufficiently engaging, or worth validating next in a game design. Produce evidence-based findings, explicit tradeoffs, and the smallest useful prototype or playtest experiment.

Overview

PublisherYuki001
Repositorygame-dev-skills
Skill namegame-design-review
Stars
104
Forks
13
Bundled files
11
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.

  • 11 bundled files

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

  • Open source

    Published by Yuki001 on GitHub. Read the source before you install it.

Installation

Install the Game Design Review 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/Yuki001/game-dev-skills/tree/main/skills/game-design-review
  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/Yuki001/game-dev-skills.git /tmp/game-dev-skills
mkdir -p .claude/skills
cp -r /tmp/game-dev-skills/skills/game-design-review .claude/skills/game-design-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Design Review 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 Review 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 Review 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 Review

Review a design by tracing how rules and presentation are likely to shape player decisions and experience. Treat the review as a decision aid, not a universal checklist or a substitute for playtesting.

Responsibility boundary

  • Use this skill when a design object or decision already exists and needs examination.
  • This skill is self-contained. Do not assume any other skill, workflow, repository layout, document set, or game engine exists.
  • When the user needs a new formal design document rather than a review, return the clarified direction, constraints, open questions, and validation criteria needed to author it.
  • Do not convert a design recommendation directly into code structure, engine APIs, or implementation tasks.

Review modes

Every review shares the same spine:

  1. Name the review object. The whole game, one loop, one mechanic, a level, an interface flow, a narrative beat, a social feature, or a production choice.
  2. State the decision at stake. A choice or testable question; if none exists, the most consequential unresolved assumption.
  3. Build an evidence baseline. Audience, rules, constraints, prototype behavior, and playtest observations, separating repeated patterns from anecdotes; mark each material statement as confirmed, assumed, or unknown.
  4. Close the review. Prioritized findings, the smallest useful intervention, and a validation experiment.

The modes differ in how the examination between these ends is executed. Pick the narrowest mode that fits; when in doubt, use the comprehensive review.

Comprehensive review (default)

Use when the direction is chosen, formal materials exist to examine (documents, prototypes, telemetry, or playtest records), and the goal is a full-coverage review for weaknesses, risks, and contradictions.

  1. Exclude, then cover. Exclude only the references the review object clearly makes irrelevant, keeping at least 6; record each exclusion and its reason, and state the unreviewed domains in the final report.
  2. Batch and delegate. Group the rest into batches of 2 to 4 related domains, one batch per read-only subagent as described in "Delegating a review to subagents" below; work through the batches sequentially yourself when subagents are unavailable.
  3. Trace effects. Each batch reader connects each design choice to player information, decision, action, feedback, and consequence, distinguishing symptom from cause.
  4. Merge and reconcile. Merge duplicates that share a root cause, normalize severity, and run a cross-domain contradiction pass, treating each contradiction as a finding of its own; do not emit one undifferentiated checklist.

Focused review

Use when everything to be reviewed fits in the conversation: the design or the question is short, or exists only as notes or keywords in the user's message, with no formal documents to read.

  1. Read 2 or 3 relevant references from the routing table yourself.
  2. Trace effects as in the comprehensive review and return only the findings that could change the decision.

Exploratory review

Use when no direction has been chosen yet: the design is too immature for fault-finding, and the goal is to outline candidate directions plus a test that can distinguish them.

  1. Identify the player promise and the central uncertainty.
  2. Outline 2 or 3 coherent directions.
  3. Define a small test that can distinguish them, and stop before expanding into a full design document.

Comparative review

Use when the goal is to choose between 2 or more defined options, whatever their material volume.

  1. Apply the same evidence and criteria to every option; do not let one option receive a stricter review.
  2. When the comparison spans several domains, you may delegate one subagent per option, each applying the same reference set.
  3. Merge the per-option findings as in the comprehensive review.

Delegating a review to subagents

Subagents do not share this conversation, so brief each one with everything it needs:

  • the review object and where to find the design materials;
  • the decision at stake and the evidence baseline, so it does not re-open settled facts;
  • its assigned reference paths, with instruction to read them fully before analyzing;
  • the finding-quality bar below, so findings carry enough substance to merge;
  • the boundary: return design findings and a recommended smallest intervention per batch, not implementation tasks.

Subagent reports may take any form that carries that substance. Never let a subagent's severity or priority pass through unreviewed; merging is your job, not theirs.

Reference routing

Review concernReadFocus
Audience, intended experience, motivation, comprehensionreferences/experience-and-audience.mdPlayer promise, motivation chain, pleasure inventory, learning assumptions, experience gaps
Core concept, theme, emotion, fantasy, aesthetic identity, curiosity and noveltyreferences/concept-theme-and-emotion.mdConcept clarity, imaginative space, thematic coherence, emotional causes, expressive unity, memorable identity
Actions, goals, rules, information, agency, complexity and strategyreferences/actions-rules-and-agency.mdGoal hierarchy, decision quality, performance judgment, rule legibility, structure and state, dominant behavior, systemic character
Challenge, skill profile, risk, punishment, rewards, progression, economy and replay depthreferences/challenge-progression-and-economy.mdDifficulty shape, skill demand, fairness, expected return, reward meaning, time structure, sources/sinks, viable paths
Puzzles, deductions, hints, bottlenecks and visible progressreferences/puzzles-and-problem-solving.mdProblem representation, reasoning chain, solution structure, recovery and hint escalation
Level space, encounters, navigation and pacingreferences/space-level-and-pacing.mdRoute choice, readability, rhythm, recovery, spatial pressure
Attention curve, memorable moments, indirect guidance and presencereferences/attention-guidance-and-presence.mdInterest over time, voluntary guidance, freedom, interruption, projection and retelling
Controls, UI, feedback and practical accessibilityreferences/interface-feedback-and-accessibility.mdIntention-to-action loop, information timing, feedback richness, error recovery, input and perception barriers
Narrative, world, characters and player-authored storiesreferences/narrative-world-and-characters.mdPlayable story delivery, story structure, world causality, role, agency, status and character function
Competition, cooperation, friendship, community and harmful behaviorreferences/social-community-and-safety.mdIncentives between players, trust, relationship continuity, expression, moderation and spectator clarity
Scope, prototypes, playtests, technology, market, distribution, business and responsibilityreferences/production-validation-and-responsibility.mdProduction fit, evidence quality, risk retirement, market viability, sustainability and player welfare

Finding quality

A useful finding contains:

  • Observation: what the current design or evidence shows;
  • Mechanism: how it may alter player understanding, choice, behavior, or feeling;
  • Evidence status: confirmed fact, supported inference, assumption, or unknown;
  • Impact: what fails if the issue is real;
  • Recommendation: the smallest change worth trying;
  • Validation: what result would support or reject the recommendation.

Avoid findings based only on personal taste. When evidence is missing, write a hypothesis and a test rather than presenting a prediction as fact.

Report structure

Use the shortest report that resolves the request. For a formal review, use:

markdown
# Design Review: [object]

## Decision at stake
[The choice or uncertainty this review should resolve.]

## Evidence baseline
- Confirmed: ...
- Assumed: ...
- Unknown: ...

## Priority findings
### [Severity] [Finding]
- Observation:
- Mechanism:
- Evidence status:
- Recommendation:
- Validation:

## Tradeoffs
[What improves, what may get worse, and who is affected.]

## Next experiment
[Smallest build, simulation, or playtest; participants; observable signals; decision rule.]

Use critical, major, or minor only when severity helps prioritization:

  • critical: undermines the core promise or prevents meaningful validation;
  • major: materially harms a common player path or creates expensive downstream rework;
  • minor: local friction or polish issue that does not invalidate the current direction.

Guardrails

  • Do not fabricate player reactions, telemetry, market facts, or playtest evidence.
  • Do not hide conflicting goals. State the tradeoff and identify who makes the final decision.
  • Do not expand the feature set merely to answer a design weakness; removal, clarification, or a smaller rule change may be better.
  • Do not prescribe technical architecture. Express confirmed design constraints and validation criteria so a later technical process can consume them without requiring a particular tool or workflow.

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 Review AI skill do?

Review, critique, compare, improve, or stress-test an existing game concept, GDD, mechanic, puzzle, economy, level, theme, emotional arc, interface, narrative, social feature, prototype, pitch, or playtest result. Use when the user asks what is weak, risky, unclear, unbalanced, contradictory, insufficiently engaging, or worth validating next in a game design. Produce evidence-based findings, explicit tradeoffs, and the smallest useful prototype or playtest experiment.

Why use Game Design Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yuki001/game-dev-skills/tree/main/skills/game-design-review. 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 Review?

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 Review?

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

Is the Game Design Review AI skill free?

It is published on GitHub by Yuki001. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇