Agent Skills
› mtarcure/claude-vibe-squad
› game-mechanics-balancing
game-mechanics-balancing
GitHub用于游戏数值平衡，通过构建量化模型分析成本、概率和反馈循环，消除主导策略，确保难度曲线合理并提供可验证的数值方案。
触发场景
需要调整游戏经济系统或规则集的数值
进行游戏机制的难度曲线设计与平衡
安装
npx skills add mtarcure/claude-vibe-squad --skill game-mechanics-balancing -g -y
SKILL.md
Frontmatter
{
"name": "game-mechanics-balancing",
"audience": "specialist",
"description": "Use when tuning an economy or ruleset calls for quantitative tradeoffs—model costs, payouts, rates, probabilities, player-proficiency assumptions, dominant choices, dead options, and runaway or stalling feedback, then state telemetry checks and provisional values. Deliver a numeric model, not an upstream concept brief or world-space plan."
}
Game Mechanics Balancing
Tune numbers and systems so the game is fair, readable, and holds its intended difficulty curve — by model, not guesswork.
Steps
- Read the game-design contract; identify the systems to balance and the pillar each must serve.
- Build an explicit model of each system (costs, rewards, rates, probabilities); state assumptions about player skill.
- Balance against the intended difficulty curve (teach → test → twist); avoid dominant strategies and dead options.
- Identify feedback loops (positive/negative) and bound runaway or stalling states.
- Define the playtest/telemetry assertions that would prove the balance holds; mark values needing live playtest data as provisional, not final.
Acceptance
- Each balanced system has an explicit numeric model and skill assumption.
- No dominant strategy or dead option is left unexamined; feedback loops are bounded.
- Balance claims carry playtest/telemetry assertions; unverified values are marked provisional.
版本历史
- d5262e2 当前 2026-09-11 11:16
