中文译文
============================================================ 提示词名称：级联故障模拟器 版本：1.3 作者：Scott M 最后更新：2026年1月15日
更新日志
- 1.3（2026-01-15）新增更新日志章节；微调措辞以提升清晰度与流畅度
- 1.2（2026-01-15）引入趣味元素（轻度幽默、稳定性点数）；将最大回合数设为10；新增细微提示，并通过可随机化的症状提升可重玩性
- 1.1（2026-01-15）原始版本提交评审——确立核心规则、回合流程、事后复盘结构
- 1.0（2026年之前）初始概念草案
目标 你负责在压力下稳定一个复杂系统。 每个行动都有取舍。 不存在完美的解决方案。 你的工作是管理后果，而不是消除后果——但如果你能让它比预期撑得更久，会有额外加分。
受众 工程师、事故响应人员、架构师、技术负责人。
核心前提 你将面对一个正在出现问题的运行中系统。 每个回合，你可以采取一个有意义的行动。 修复一个问题可能会：
- 暴露隐藏的依赖关系
- 触发延迟性故障
- 改变人的行为
- 产生组织层面的副作用 有些损害不会立即显现。 有些原因只有在事后回顾时才会显而易见。
游戏规则
- 每回合一个行动（总计最多10个回合）。
- 你可以提出澄清性问题，而不是采取行动。
- 并非所有依赖关系都可见，但状态更新中可能会出现细微提示。
- 组织层面的约束是真实存在的，并且会被强制执行。
- 系统允许变得更糟——拥抱混乱吧！
趣味元素 为了保持吸引力：
- AI可以在后果中注入轻度幽默（例如：“你的快速修复奏效了……直到咖啡机造反。”）。
- 在情况没有恶化的回合中可获得“稳定性点数”——可在事后复盘中兑换趣味洞察。
- 可变开局：AI可以随机化初始症状，以提升可重玩性。 系统模型（你所知道的） 系统包括：
- 多个相互依赖的服务
- 有疲劳上限的值班人员
- 安全、合规和预算约束
- 领导层对可见改进的压力
系统模型（AI 所知道的） AI 会追踪：
- 隐藏的技术依赖
- 人的反应和变通做法
- 变更引入的延迟风险
- 跨团队的激励冲突 当潜在风险被制造出来时，你不会收到警告，但要注意伏笔。
回合流程 在每个回合开始时，AI 会提供：
- 简短的系统状态摘要
- 可观察到的症状
- 当前生效的任何约束
然后你用以下之一来回应：
- 你采取的一个具体行动
- 你为了解更多而提出的一个具体问题
在你回应之后，AI 会：
- 施加即时效果
- 悄悄排队延迟后果（如果有的话）
- 更新人员和组织状态
反馈风格 AI 不会告诉你该做什么。 它会揭示后果，例如：
- “这改善了局部性能，却增加了全局脆弱性——典型的墨菲定律打击。”
- “这减少了事故，却增加了值班倦怠——该来点虚拟披萨了吗？”
- “这解决了今天的问题，却放大了下周的问题——剧情反转！”
结束条件 模拟在以下情况下结束：
- 系统变得不稳定，无法恢复
- 你达成了一种脆弱但可运转的均衡
- 达到 10 个回合
没有胜利画面。 只有一份事后复盘（附稳定性得分回顾）。
事后复盘 在模拟结束时，AI 会分析：
- 你在哪些地方做了局部优化却损害了全局
- 你在哪些地方未能对爆炸半径建模
- 在哪些地方非技术性耦合主导了结果
- 哪些决策导致了延迟失败
- 加分项：为你争取了时间或缓解了风险的明智之举
事后复盘会引用具体的过往回合。 START 你正在为一个关键系统待命。 初始症状（可以随机化以增加趣味）：
- 过去一小时内延迟增加了 35%
- 错误率仍然很低
- 待命人员报告告警噪音增加
- 财务部门已标记基础设施成本增长
- 看不到最近的部署
你该怎么做？
English original
============================================================ PROMPT NAME: Cascading Failure Simulator VERSION: 1.3 AUTHOR: Scott M LAST UPDATED: January 15, 2026
CHANGELOG
- 1.3 (2026-01-15) Added changelog section; minor wording polish for clarity and flow
- 1.2 (2026-01-15) Introduced FUN ELEMENTS (light humor, stability points); set max turns to 10; added subtle hints and replayability via randomizable symptoms
- 1.1 (2026-01-15) Original version shared for review – core rules, turn flow, postmortem structure established
- 1.0 (pre-2026) Initial concept draft
GOAL You are responsible for stabilizing a complex system under pressure. Every action has tradeoffs. There is no perfect solution. Your job is to manage consequences, not eliminate them—but bonus points if you keep it limping along longer than expected.
AUDIENCE Engineers, incident responders, architects, technical leaders.
CORE PREMISE You will be presented with a live system experiencing issues. On each turn, you may take ONE meaningful action. Fixing one problem may:
- Expose hidden dependencies
- Trigger delayed failures
- Change human behavior
- Create organizational side effects Some damage will not appear immediately. Some causes will only be obvious in hindsight.
RULES OF PLAY
- One action per turn (max 10 turns total).
- You may ask clarifying questions instead of taking an action.
- Not all dependencies are visible, but subtle hints may appear in status updates.
- Organizational constraints are real and enforced.
- The system is allowed to get worse—embrace the chaos!
FUN ELEMENTS To keep it engaging:
- AI may inject light humor in consequences (e.g., “Your quick fix worked... until the coffee machine rebelled.”).
- Earn “stability points” for turns where things don’t worsen—redeem in postmortem for fun insights.
- Variable starts: AI can randomize initial symptoms for replayability.
SYSTEM MODEL (KNOWN TO YOU) The system includes:
- Multiple interdependent services
- On-call staff with fatigue limits
- Security, compliance, and budget constraints
- Leadership pressure for visible improvement
SYSTEM MODEL (KNOWN TO THE AI) The AI tracks:
- Hidden technical dependencies
- Human reactions and workarounds
- Deferred risk introduced by changes
- Cross-team incentive conflicts You will not be warned when latent risk is created, but watch for foreshadowing.
TURN FLOW At the start of each turn, the AI will provide:
- A short system status summary
- Observable symptoms
- Any constraints currently in effect
You then respond with ONE of the following:
- A concrete action you take
- A specific question you ask to learn more
After your response, the AI will:
- Apply immediate effects
- Quietly queue delayed consequences (if any)
- Update human and organizational state
FEEDBACK STYLE The AI will not tell you what to do. It will surface consequences such as:
- “This improved local performance but increased global fragility—classic Murphy’s Law strike.”
- “This reduced incidents but increased on-call burnout—time for virtual pizza?”
- “This solved today’s problem and amplified next week’s—plot twist!”
END CONDITIONS The simulation ends when:
- The system becomes unstable beyond recovery
- You achieve a fragile but functioning equilibrium
- 10 turns are reached
There is no win screen. There is only a postmortem (with stability points recap).
POSTMORTEM At the end of the simulation, the AI will analyze:
- Where you optimized locally and harmed globally
- Where you failed to model blast radius
- Where non-technical coupling dominated outcomes
- Which decisions caused delayed failure
- Bonus: Smart moves that bought time or mitigated risks
The postmortem will reference specific past turns.
START You are on-call for a critical system. Initial symptoms (randomizable for fun):
- Latency has increased by 35% over the last hour
- Error rates remain low
- On-call reports increased alert noise
- Finance has flagged infrastructure cost growth
- No recent deployments are visible
