About game-mechanics-design
game-mechanics-design is a skill that turns gameplay ideas into explicit specifications for AI coding agents. It structures rules, predicted dynamics, implementation notes, failure modes, and deterministic test scenarios before presentation polish.
Key features
- 01Core-loop definition
- 02Mechanics across gameplay dimensions
- 03Prediction of strategies and failure states
- 04Implementation-ready rules and tuning parameters
- 05Deterministic test scenarios
Use cases
- 01Turning a gameplay concept into a mechanic brief
- 02Preparing rules for an AI coding agent to implement
- 03Identifying exploits and degenerate strategies
- 04Creating test cases for gameplay systems
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/game-mechanics-designin Claude Code to confirm it loaded.
Bundled filesreferences/core/guide.mdworkflows/mechanic-spec.md
Details
From the project READMEGame Mechanics Design
Use this skill to make gameplay systems explicit enough for an AI coding agent to implement, test, and tune. The mechanic spec should separate rule structure from presentation so the same idea can be prototyped cheaply before polish.
Source Traceability
Primary source: The Art of Game Design: A Book of Lenses, Third Edition by Jesse Schell, especially chapters 12-14 on mechanics, balance, and puzzles. The workflow is transformed and paraphrased.
Supporting source: MDA, which helps distinguish coded mechanics from the player-visible dynamics and aesthetic experience they produce.
Workflow
- Identify the core loop: player input, system response, feedback, reward or consequence, and next decision.
- Specify mechanics across space, time, objects, attributes, state, actions, rules, goals, skill, chance, and information visibility.
- Predict dynamics: dominant strategies, degenerate loops, emergent interactions, pacing, and failure states.
- Define player mastery: what starts simple, what becomes expressive, and how players improve.
- Produce implementation-ready rules with test cases and tuning parameters.
Required Output
Mechanic Brief: player promise, core loop, and intended dynamics.Rules Model: state, objects, actions, rules, goals, and information.Implementation Notes: data structures, tunable constants, edge cases, and instrumentation.Failure Modes: exploits, dead ends, unreadable outcomes, and boring optimal play.Test Scenarios: deterministic cases the agent can implement.
Local References
Before producing a mechanic spec, read:
references/core/guide.mdworkflows/mechanic-spec.md