game design unknown unknowns prototyping
Identify hidden design risks and create high-leverage prototyping plans to resolve game design uncertainty.
$5
Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
GitHub Copilot
Gemini CLI+20 moreClaude CodeCursorCodex CLIGitHub CopilotGemini CLI
OpenClaw
Manus
Grok+17 agentsgame design unknown unknowns prototyping
Example session with this skill installed
We want to add a 'Nemesis' system to our tactical RPG where defeated enemies return with adaptations. The team is worried it might just feel like a chore rather than a challenge.
- Read your context and instructions
- Compiled the game design unknown
- Generated the UI component
I have analyzed the Nemesis system concept. The primary risk is that the mechanic creates friction without a corresponding emotional payoff.
Prototyping Strategy: Nemesis Adaptation System
Concept Framing
Current concept: Defeated enemies reappear in later missions with specific resistances to the player's last-used tactics.
- Why it seems promising: Creates a personal narrative and forces tactical variety.
Why it is still unclear: We don't know if players will feel clever for pivoting or punished for their previous success.
*
Assumptions: Players have enough tactical breadth to pivot easily; the "return" feels like a story beat, not a bug.
Uncertainty Map
| Quadrant | Items |
|---|---|
| Known knowns | We can track enemy death states and modify stat blocks. |
| Known unknowns | The ideal frequency of encounters; how many resistances are "too many." |
| Unknown knowns | We implicitly assume players want a harder game, but they may just want to feel powerful. |
| Unknown unknowns suspects | Emergent "soft-locks" where a player's favorite build becomes unusable across all encounters. |
Prototype Plan
| Prototype Question | Best Prototype Type | Fidelity Needed | Why |
|---|---|---|---|
| Does adaptation feel like a challenge or a chore? | Experience Prototype | Paper/Tabletop | Test the emotional reaction to losing a "best" tactic without coding AI. |
| Can players identify why an enemy adapted? | Interaction Prototype | Static Mockups | Test if the UI clearly communicates the "Why" behind the new resistance. |
| Does this break the long-term economy? | Systems Prototype | Spreadsheet/Python | Simulate 50 hours of adaptations to see if enemies become mathematically invincible. |
Stop Criteria
Stop when: We have a proven UI pattern for "Adaptation Alerts" and a mathematical cap on resistances that prevents soft-locks.
- Continue if: Playtesters still report feeling "cheated" rather than "challenged."
Next steps
- Run a tabletop session using the current resistance logic to observe player frustration levels.
- Build a spreadsheet model to simulate 100 encounters with cumulative adaptations.
- Draft a UI wireframe for the "Nemesis Intel" screen.
game-design-unknown-unknowns-prototyping.tsx
TSX · React component
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Game features often enter full production based on exciting but vague concepts. Teams waste months building the wrong thing because they mistook a visible mechanic for the actual source of player value.
What it does
- Identifies hidden design risks by mapping uncertainty into knowns, unknowns, and "unknown unknowns" suspects.
- Drafts specific prototype questions that prioritize learning objectives over implementation details.
- Selects the lowest-fidelity medium—from Experience to Wizard-of-Oz prototypes—to answer critical questions cheaply.
- Defines clear stop criteria to prevent "prototyping hell" and ensure a smooth transition to preproduction.
Why this beats prompting it yourself
General AI tends to suggest building a vertical slice or a basic MVP. This skill forces a diagnostic approach that separates production tasks from discovery tasks, ensuring you don't build a digital prototype when a paper system or a fake-backend test would expose the risk faster.
Use cases
- Evaluating a new retention mechanic that relies on complex player psychology.
- Testing hybrid UI systems where readability and input pressure might conflict.
- Stress-testing economy loops before committing to a full simulation architecture.
Known limitations
Requires an initial feature concept or design problem to analyze; it cannot invent game ideas from scratch.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
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- 2
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- 3
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