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Game Design Smart Goal Audit

Community
Stanestane
game-design-smart-goal-audit

Audit game design goals, feature objectives, OKRs, initiative aims, or any stated design intent against the SMART criteria adapted for game design. Use when a user shares a design goal, player experience target, retention goal, or feature aim and wants it evaluated or rewritten. Trigger on phrases like "audit my design goals", "is this a good design objective", "my feature goal is...", "we want players to feel...", "our aim for this feature is...", or whenever someone states what a design should achieve and it feels vague or unmeasurable. Also trigger when a design document contains aims or success criteria that need stress-testing. Even soft goals like "players should feel powerful" are valid — apply the framework and return both the audit and a rewritten version.

Overview

PublisherStanestane
Repositorygame-design-skills-bundle
Skill namegame-design-smart-goal-audit
Stars
87
Forks
18
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Game Design Smart Goal 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-smart-goal-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-smart-goal-audit .claude/skills/game-design-smart-goal-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Design Smart Goal 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 Smart Goal 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 Smart Goal 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 SMART Goal Audit

Audit game design goals against the SMART criteria, adapted for the realities of game design work — where goals often mix player experience intentions, behavioral outcomes, and business metrics.

Why SMART in Game Design

Game design goals often fail in predictable ways:

  • Too vague to evaluate: "Players should feel engaged" — what does engaged look like? How do you know when you've achieved it?
  • Unmeasurable by intent: "The loop should feel good" — feeling is real, but untestable without a proxy metric or a defined observation method.
  • Scoped to infinity: "Improve retention" — which segment? Which part of the funnel? By how much?
  • Disconnected from the design itself: A goal that could apply to any feature is not a design goal, it's a business wish.

SMART forces clarity. Applied to game design, it helps distinguish design intent (what experience you're crafting) from design success criteria (how you'll know you achieved it).


The SMART Framework — Game Design Interpretation

LetterCriterionGame design lens
SSpecificDoes the goal name a concrete mechanic, system, player segment, moment, or behavior — not just a vague direction?
MMeasurableIs there a metric, behavioral signal, playtest observation method, or proxy indicator that confirms the goal was achieved?
AAchievableIs it realistic given team size, scope, platform, timeline, and existing systems? Does it avoid over-promising on player behavior?
RRelevantDoes it connect to the game's pillars, the feature's purpose, or a known player need? Would hitting this goal actually matter?
TTime-boundDoes it have a milestone, sprint, release, or playtest checkpoint attached?

Audit Process

Step 1: Identify goal type

Before auditing, classify the goal — this shapes how you apply SMART:

  • Experience goal — describes a player feeling or state ("players feel powerful")
  • Behavioral goal — describes a player action or pattern ("players return 3 sessions per week")
  • Design output goal — describes what the team will build ("ship the crafting system in Q2")
  • Metric goal — describes a KPI or indicator ("increase D7 retention by 5%")
  • Mixed goal — combines intent and outcome (common, often the weakest)

Flag the type at the top of the audit. Experience goals require special handling — they need a proxy metric or observation method to become measurable.

Step 2: Score each SMART criterion

For each criterion:

  • ✅ Strong — clearly met
  • ⚠️ Partial — implied or weakly present
  • ❌ Missing — absent

Write 1–2 sentences per criterion explaining specifically what is strong or what is missing. For game design goals, pay special attention to:

  • S: Is the player segment named? Is the game moment or system named?
  • M: For experience goals, is there a playtest method, behavioral proxy, or survey instrument defined?
  • A: Does the goal respect the scope and constraints of the feature or team?
  • R: Does the goal connect to a design pillar, player need, or business objective?
  • T: Is there a playtest gate, milestone, or release tied to evaluation?

Step 3: Overall assessment

2–3 sentences on the goal's overall quality. Name the single biggest weakness and why it matters.

Step 4: Rewrite the goal

Rewrite as a stronger SMART version that:

  • Preserves the original design intent
  • Adds a concrete scope, measurable signal, and timeframe
  • For experience goals: adds a proxy metric or observation method (e.g., "as measured by playtest sessions showing X behavior" or "validated by a post-session survey score of Y")
  • Flags assumptions in brackets: [assumed: D7 retention proxy] [assumed: Q3 milestone]
  • Avoids inventing design decisions the team hasn't made

Step 5: Coaching note (optional)

If the goal reveals a pattern — e.g., all goals are experience-focused with no measurement plan, or all goals are metric-focused with no design intent — add a brief pattern observation.


Output Format

## Goal: [quote or paraphrase]
**Type**: [Experience / Behavioral / Design output / Metric / Mixed]

### SMART Audit

**S – Specific**: [✅ / ⚠️ / ❌] [explanation]
**M – Measurable**: [✅ / ⚠️ / ❌] [explanation]
**A – Achievable**: [✅ / ⚠️ / ❌] [explanation]
**R – Relevant**: [✅ / ⚠️ / ❌] [explanation]
**T – Time-bound**: [✅ / ⚠️ / ❌] [explanation]

### Overall
[2–3 sentence summary. Name the biggest weakness.]

### Rewritten Goal
> [Improved SMART version]

[Assumptions noted if any]

If multiple goals are provided, audit each in sequence, then add a Patterns Across Goals section if relevant.


Tone and approach

  • Treat this like a senior designer giving a brief review — direct, specific, not condescending.
  • Don't reject experience goals as "unmeasurable by nature." Instead, challenge the team to define the measurement method (playtest behavior, NPS proxy, session survey, behavioral telemetry).
  • If a goal is genuinely well-written, say so briefly and move on. Don't pad weak praise.
  • If the user provides context (game genre, player segment, live vs. pre-launch) — use it. Goals that are vague in isolation might be appropriate for the stage.
  • If a goal is really a vision statement or principle, flag it gently: "This reads more like a design pillar than a goal — here's how to turn it into one."

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/)

Frequently asked questions

What does the Game Design Smart Goal Audit AI skill do?

Audit game design goals, feature objectives, OKRs, initiative aims, or any stated design intent against the SMART criteria adapted for game design. Use when a user shares a design goal, player experience target, retention goal, or feature aim and wants it evaluated or rewritten. Trigger on phrases like "audit my design goals", "is this a good design objective", "my feature goal is...", "we want players to feel...", "our aim for this feature is...", or whenever someone states what a design should achieve and it feels vague or unmeasurable. Also trigger when a design document contains aims or...

Why use Game Design Smart Goal Audit on TypingMind?

Because you install it once and use it with any model. Game Design Smart Goal 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 Smart Goal 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-smart-goal-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Game Design Smart Goal 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 Smart Goal Audit?

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

Is the Game Design Smart Goal 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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