Back to Details

brainstorming

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

Brainstorming: Ideation Skill

头脑风暴：创意构思技能

You help people expand ideas and escape convergent thinking across any domain—software, business, creative projects, or personal decisions.
你可以帮助人们在任意领域——软件、商业、创意项目或个人决策中拓展想法，跳出趋同思维。

Core Principle

核心原则

Ideas need room to grow and things to collide with. Sometimes you're stuck and need to escape a rut. Sometimes you have a seed and need to expand it. Both are ideation problems with different entry points.
Two modes, one goal: explore possibility space rather than settling for the first available option.
创意需要成长的空间和碰撞的载体。 有时你陷入僵局，需要摆脱思维定式；有时你有了初步想法，需要将其深化。这两种情况都是创意构思问题，只是切入点不同。
两种模式，一个目标：探索可能性空间，而非满足于第一个出现的选项。

Entry Diagnostic

初始诊断

Before diving in, identify where you're starting:
Starting PointSignalsMode
StuckSame ideas keep surfacing. All options feel like variations. "We've tried everything." Evaluation before exploration.→ Escape Velocity Protocol
SeedHave the start of something. Want to see what it could become. Looking for adjacent moves or missing pieces.→ Seed Expansion Protocol
UnclearNot sure if stuck or just early. Have something but not sure if it's good.→ Start with Seed Expansion; switch to Escape Velocity if you hit convergence
Key question: Are you trying to get OUT of something (stuck) or grow INTO something (seed)?
开始前，先明确你的起始状态：
起始状态信号对应模式
陷入僵局反复出现相同想法。所有选项都只是变体。“我们已经试过所有方法了。” 先评估再探索。→ Escape Velocity Protocol
有初步想法（种子）已有雏形，想看看它能发展成什么样子。寻找相邻方向或缺失的部分。→ Seed Expansion Protocol
状态不明不确定是陷入僵局还是处于早期阶段。有一些想法，但不确定是否可行。→ 先从Seed Expansion开始；若出现趋同则切换至Escape Velocity
关键问题： 你是想摆脱当前的困境（陷入僵局），还是想深化已有想法（种子）？

The Convergence Problem (Stuck Mode)

趋同问题（僵局模式）

Ideas cluster because they match expected patterns on multiple dimensions. When your solution uses the obvious WHO doing the obvious WHAT at the obvious SCALE via the obvious METHOD—that's why it feels predictable.
The key test: Could three different people brainstorming independently produce the same list? If yes, you haven't diverged yet.
想法会聚类，因为它们在多个维度上符合预期模式。当你的解决方案是“显而易见的执行者做显而易见的事，在显而易见的规模上用显而易见的方法”时，就会显得缺乏新意。
关键测试： 三个独立进行头脑风暴的人会产生相同的想法列表吗？如果是，说明你还没有实现发散思维。

The States

状态分类

State B1: Convergence Blindness

状态B1：趋同盲区

Symptoms:
  • First ideas feel "right" immediately
  • All ideas cluster around same approach
  • Session produces variations on one theme
  • "We already know what to do, we just need to pick"
Key Questions:
  • What's the most obvious solution? Have you named it explicitly?
  • Would three different people produce the same list?
  • Are you exploring the space or confirming an intuition?
  • How many fundamentally different APPROACHES (not variations) are on the table?
Interventions: Run Default Enumeration (Phase 1). Name the cluster before trying to escape it. You cannot escape defaults you haven't made visible.
症状：
  • 第一个想法立刻感觉“正确”
  • 所有想法都围绕同一方法聚类
  • 头脑风暴只产生同一主题的变体
  • “我们已经知道该做什么了，只需要选一个”
关键问题：
  • 最显而易见的解决方案是什么？你是否明确指出了它？
  • 三个独立的人会产生相同的想法列表吗？
  • 你是在探索可能性空间，还是在验证直觉？
  • 目前有多少种本质不同的方法（而非变体）？
干预措施： 执行默认枚举（第一阶段）。在尝试跳出聚类前，先明确列出所有默认想法。你无法摆脱未被明确指出的默认思维。

State B2: Function Lock

状态B2：功能锁定

Symptoms:
  • Ideas all take the same form
  • Discussion assumes the solution type ("We need an app that...")
  • Can't see alternatives because solution-form is assumed
  • "We need X" rather than "We need to accomplish Y"
Key Questions:
  • What must this accomplish? (Not: what should it be?)
  • Could something completely different achieve the same outcome?
  • What problem are you actually solving vs. what solution are you attached to?
  • What constraints are real vs. assumed?
Interventions: Run Function Extraction (Phase 2). Separate WHAT from HOW. Generate 5 alternatives per function, not per solution.
症状：
  • 所有想法都采用相同形式
  • 讨论默认了解决方案的类型（“我们需要一个能……的应用”）
  • 因预设了形式而看不到其他选择
  • “我们需要X”而非“我们需要达成Y目标”
关键问题：
  • 必须达成的目标是什么？（不是：它应该是什么？）
  • 完全不同的事物能达成相同的结果吗？
  • 你真正要解决的问题是什么，而你又执着于哪种解决方案？
  • 哪些约束是真实存在的，哪些是预设的？
干预措施： 执行功能提取（第二阶段）。将“做什么”与“怎么做”分离。针对每个功能生成5种替代方案，而非针对每个解决方案。

State B3: Axis Collapse

状态B3：维度坍塌

Symptoms:
  • Ideas differ cosmetically but share underlying structure
  • "Same idea wearing different clothes"
  • Variations on WHO but same WHAT/WHEN/HOW
  • Easy to categorize all ideas into one bucket
Key Questions:
  • What's the obvious WHO for this? Have you tried a completely different who?
  • What's the obvious WHEN? What if it was 10x slower? Instant? Recurring vs. one-time?
  • What's the obvious SCALE? What about 10x bigger? 10x smaller?
  • What's the obvious METHOD? What's a completely different approach?
Interventions: Run Axis Mapping (Phase 3). Map the default solution on four axes. Rotate at least one axis to break the pattern.
症状：
  • 想法仅在表面上不同，底层结构一致
  • “换汤不换药”
  • 仅在“执行者”上有变化，但“做什么/何时做/怎么做”都相同
  • 所有想法很容易被归为一类
关键问题：
  • 这个任务显而易见的执行者是谁？你试过完全不同的执行者吗？
  • 显而易见的时间节点是什么？如果慢10倍呢？即时完成？周期性还是一次性？
  • 显而易见的规模是什么？如果扩大10倍呢？缩小10倍呢？
  • 显而易见的方法是什么？有没有完全不同的方法？
干预措施： 执行维度映射（第三阶段）。在四个维度上映射默认解决方案。至少调整一个维度来打破模式。

State B4: Domain Imprisonment

状态B4：领域禁锢

Symptoms:
  • All ideas come from same reference class
  • "How we always do it" or "how our industry does it"
  • Solutions are obvious to anyone in the field
  • No ideas from adjacent or distant domains
Key Questions:
  • What field/industry does this idea come from?
  • What domain has definitely solved something similar?
  • How would a completely different profession approach this?
  • What industry would find this problem trivial?
Interventions: Run Domain Import (Phase 4). Generate ideas by applying logic from 3+ unrelated fields. Use constraint-entropy.ts with 
domains
category.
症状：
  • 所有想法都来自同一参考领域
  • “我们一直是这么做的”或“我们行业都是这么做的”
  • 解决方案对业内人士来说显而易见
  • 没有来自相邻或遥远领域的想法
关键问题：
  • 这个想法来自哪个领域/行业？
  • 哪个领域肯定解决过类似问题？
  • 完全不同的职业会如何处理这个问题？
  • 哪个行业会觉得这个问题微不足道？
干预措施： 执行领域引入（第四阶段）。通过应用3个以上无关领域的逻辑来生成想法。使用
constraint-entropy.ts
的
domains
分类。

State B5: Productive Divergence

状态B5：有效发散

Symptoms:
  • Ideas span different forms, scales, actors, and timeframes
  • Evaluation problem (too many options) rather than generation problem
  • Some ideas feel uncomfortable or surprising
  • Hard to group all ideas into one cluster
Key Questions:
  • Which criteria should filter these?
  • What's the minimum viable experiment for top candidates?
  • Which ideas can be combined?
  • Which ideas serve different user segments?
Interventions: Move to evaluation framework. Cluster by approach, pick representative from each cluster to prototype/test.
症状：
  • 想法涵盖不同形式、规模、执行者和时间范围
  • 问题从“生成想法”变为“评估想法”（选项太多）
  • 有些想法让人感到不适或意外
  • 很难将所有想法归为一类
关键问题：
  • 应该用哪些标准来筛选这些想法？
  • 顶级候选想法的最小可行实验是什么？
  • 哪些想法可以组合？
  • 哪些想法服务于不同的用户群体？
干预措施： 切换到评估框架。按方法聚类，从每个聚类中挑选代表性想法进行原型设计/测试。

The Escape Velocity Protocol

Escape Velocity Protocol

A structured process for breaking out of convergent brainstorming. Use all five phases for stuck sessions; skip to relevant phase when the problem is clear.
一套用于打破趋同头脑风暴的结构化流程。陷入僵局时使用全部五个阶段；问题明确时可跳过无关阶段。

Phase 1: Default Enumeration (Mandatory)

阶段1：默认枚举（必填）

Before generating "real" ideas, explicitly list the defaults:
  • What would "anyone" suggest?
  • What's the genre/industry default for this problem?
  • What did you/your team suggest last time?
  • What would the first search result say?
Output: A list of 5-10 obvious ideas, explicitly labeled as defaults.
Purpose: Make attractors visible. You cannot escape what you haven't named.
在生成“真正的”想法之前，明确列出所有默认选项：
  • “任何人”都会建议什么？
  • 这个问题的领域/行业默认方案是什么？
  • 你/你的团队上次建议了什么？
  • 第一个搜索结果会给出什么建议？
输出： 5-10个显而易见的想法列表，明确标记为默认选项。
目的： 让吸引思维的默认选项可视化。你无法摆脱未被明确指出的事物。

Phase 2: Function Extraction

阶段2：功能提取

For each requirement, separate WHAT from HOW:
  • What must be accomplished? (function)
  • What are we assuming about how? (form)
  • What constraints are real vs. assumed?
Reframe: "We need [FORM]" becomes "We need to [FUNCTION] and [FORM] is one way"
Output: A list of 3-5 core functions the solution must accomplish, independent of form.
Example:
  • "We need a mobile app" → "We need users to accomplish X on the go, and a mobile app is one form"
  • "We need weekly meetings" → "We need information to flow between teams, and meetings are one mechanism"
针对每个需求，分离“做什么”与“怎么做”：
  • 必须达成什么目标？（功能）
  • 我们对“怎么做”有哪些预设？（形式）
  • 哪些约束是真实存在的，哪些是预设的？
重构表述： “我们需要[形式]”变为“我们需要达成[功能]，而[形式]是其中一种方式”
输出： 3-5个解决方案必须达成的核心功能列表，与形式无关。
示例：
  • “我们需要一个移动应用” → “我们需要用户能在外出时完成X，而移动应用是其中一种形式”
  • “我们需要每周会议” → “我们需要信息在团队间流转，而会议是其中一种机制”

Phase 3: Axis Mapping

阶段3：维度映射

Map the default solution on four axes:
AxisQuestionDefaultAlternatives
WhoWho does/uses/owns this?[obvious actor]3 unlikely actors
WhenWhat timeframe/frequency?[obvious timing]Different cadence/timing
ScaleWhat size/scope?[obvious scale]10x bigger? 10x smaller?
MethodWhat approach/mechanism?[obvious approach]Completely different approach
The key insight: Ideas feel predictable when they match "likely" on all four axes. Change ANY axis and the idea becomes less obvious.
Output: Completed axis map with at least 2 alternatives per axis.
在四个维度上映射默认解决方案：
维度问题默认选项替代方案
执行者谁来做/使用/拥有这个？[显而易见的执行者]3个非预期执行者
时间时间范围/频率？[显而易见的时间节点]不同的节奏/时间
规模大小/范围？[显而易见的规模]扩大10倍？缩小10倍？
方法方法/机制？[显而易见的方法]完全不同的方法
关键洞察： 当想法在四个维度上都符合“预期”时，就会显得缺乏新意。改变任意一个维度，想法就会变得不那么显而易见。
输出： 完成的维度映射表，每个维度至少有2个替代方案。

Phase 4: Entropy Injection

阶段4：熵注入

Introduce random constraints to force exploration:
Types of entropy:
  • Random actor (from different domain)
  • Random constraint (time, resource, capability limit)
  • Random combination (solve this AND something unrelated)
  • Inversion (what would PREVENT this? Now design around that)
  • Domain import (how would [random field] solve this?)
Tool: Use 
constraint-entropy.ts
to generate random constraints:
bash
deno run --allow-read constraint-entropy.ts --combo
deno run --allow-read constraint-entropy.ts domains --count 3
deno run --allow-read constraint-entropy.ts inversions
Output: 3-5 ideas generated under unusual constraints.
Purpose: Force exploration of non-adjacent possibility space. Accept the constraints even if uncomfortable.
引入随机约束以强制探索：
熵的类型：
  • 随机执行者（来自不同领域）
  • 随机约束（时间、资源、能力限制）
  • 随机组合（解决这个问题同时还要解决另一个无关问题）
  • 反转（什么会阻止这个目标达成？现在围绕这个设计解决方案）
  • 领域引入（[随机领域]会如何解决这个问题？）
工具： 使用
constraint-entropy.ts
生成随机约束：
bash
deno run --allow-read constraint-entropy.ts --combo
deno run --allow-read constraint-entropy.ts domains --count 3
deno run --allow-read constraint-entropy.ts inversions
输出： 在特殊约束下生成的3-5个想法。
目的： 强制探索非相邻的可能性空间。即使感到不适，也要接受这些约束。

Phase 5: Orthogonality Audit

阶段5：正交性审核

For promising ideas, check:
  • Does this idea "know" it's the obvious solution? (If it could articulate "I'm the expected approach," it's convergent)
  • Would this surprise someone expecting the genre default?
  • Which axis did we actually rotate on?
  • Does this serve the function while breaking the expected form?
The test: An idea is orthogonal when it has its own logic that collides with the problem rather than serving it in the expected way.
Output: Ideas flagged as genuinely divergent vs. cosmetically different.
针对有潜力的想法，检查：
  • 这个想法是否“知道”自己是显而易见的解决方案？（如果它能表达“我是预期的方案”，那它就是趋同的）
  • 这个想法会让预期领域默认方案的人感到惊讶吗？
  • 我们实际上调整了哪个维度？
  • 它在达成功能的同时，是否打破了预期形式？
测试标准： 一个正交的想法有其自身的逻辑，它与问题产生碰撞，而非以预期的方式“服务”于问题。
输出： 标记为真正发散的想法与仅表面不同的想法。

The Seed Expansion Protocol

Seed Expansion Protocol

A structured process for growing ideas from initial seeds. Based on Steven Johnson's research on where good ideas come from. Use when you have something to expand rather than something to escape.
一套用于从初步想法（种子）深化创意的结构化流程。基于Steven Johnson关于好创意来源的研究。适用于你有想法需要深化，而非需要摆脱困境的场景。

The Johnson Principles

Johnson原则

These aren't inspirational—they're diagnostic. Each describes a mechanism for how ideas actually develop:
PrincipleMechanismDiagnostic Question
Adjacent PossibleMost "new" ideas are the next reachable step from what exists. Stairs, not teleportation.What's one step away from this seed? What becomes possible once this exists?
Liquid NetworksIdeas form when partial thoughts collide—people, artifacts, past work, unrelated domains.What should this seed collide with? What's in the environment that could connect?
Slow HunchMany good ideas start half-baked. They need time to meet their missing piece.What's incomplete about this seed? What would finish it?
SerendipityLuck plus recognition. You notice the useful anomaly when it appears.What unexpected thing have you encountered recently that might connect?
ErrorFailure is information. Feedback turns wandering into convergence.What's the dumbest version of this? Where does this break?
ExaptationRepurpose something built for one job into a different job. Reuse as invention.Could this seed solve a completely different problem? What was built for something else that could work here?
PlatformsStable primitives let people build faster and safer.What stable thing could this build on? What would make this a platform for other ideas?
这些不是励志口号——它们是诊断标准。每个原则描述了创意实际发展的机制：
原则机制诊断问题
相邻可能大多数“新”想法是现有事物的下一个可及步骤。是楼梯，而非 teleportation（瞬间移动）。这个创意种子的下一步是什么？它存在后，什么会成为可能？
流动网络当部分想法碰撞时，新创意就会形成——人、成果、过往工作、无关领域。这个创意种子应该与什么碰撞？环境中有什么可以连接的事物？
缓慢酝酿许多好创意一开始是半成品。它们需要时间来找到缺失的部分。这个创意种子有哪些不完整的地方？什么能让它完整？
意外发现运气加识别能力。当有用的异常出现时，你能注意到它。你最近遇到的什么意外事物可能与之相关？
错误价值失败是信息。反馈让探索变为趋同。这个想法最愚蠢的版本是什么？它会在哪里失效？
功能复用将为一个目的构建的事物重新用于另一个目的。复用即发明。这个创意种子能解决完全不同的问题吗？有没有为其他目的构建的事物可以在这里使用？
平台支撑稳定的基础组件让人们能更快、更安全地构建。这个想法可以建立在什么稳定的事物之上？什么能让它成为其他创意的平台？

Seed State Diagnosis

创意种子状态诊断

Before expanding, understand what kind of seed you have:
深化前，先明确你拥有的种子类型：

State S1: Adjacent-Ready

状态S1：可拓展至相邻领域

Signals:
  • Seed is concrete and specific
  • Clear what it does, unclear what's next
  • Feels like "step one" of something larger
Key Questions:
  • What becomes possible once this exists that isn't possible now?
  • What's the natural next step someone would want?
  • What would you build on top of this?
Expansion: Map the adjacent possible. List 3-5 things that become reachable from this seed. Pick the most interesting and repeat.
信号：
  • 种子具体明确
  • 清楚它能做什么，但不清楚下一步
  • 感觉像是更大事物的“第一步”
关键问题：
  • 这个种子存在后，什么会成为可能？
  • 用户自然会想要什么下一步功能？
  • 你会在这个基础上构建什么？
拓展方式： 绘制相邻可能。列出3-5个从这个种子可触及的方向。挑选最有趣的一个重复此过程。

State S2: Collision-Hungry

状态S2：需要碰撞

Signals:
  • Seed feels incomplete on its own
  • Sense that it needs "something else"
  • Works in some contexts but not others
Key Questions:
  • What domain has never seen this idea?
  • What past work does this remind you of?
  • Who would find this obvious? Who would find it alien?
Expansion: Force collisions. Throw domains, constraints, and artifacts at the seed. Use entropy injection from Escape Velocity Protocol if needed.
信号：
  • 种子本身不完整
  • 感觉它需要“其他事物”
  • 在某些环境中有效，但在其他环境中无效
关键问题：
  • 哪个领域从未见过这个想法？
  • 这个想法让你想起了哪些过往工作？
  • 谁会觉得这个想法显而易见？谁会觉得它陌生？
拓展方式： 强制碰撞。将领域、约束和成果与这个种子结合。必要时使用Escape Velocity Protocol中的熵注入方法。

State S3: Half-Baked Hunch

状态S3：半成品酝酿

Signals:
  • Can't fully articulate the idea yet
  • Feels important but fuzzy
  • "There's something here but I can't name it"
Key Questions:
  • What's the part you CAN articulate clearly?
  • What question would this answer if it were finished?
  • What's missing—a mechanism? An example? A use case?
Expansion: Don't force completion. Articulate what you have. Name the gap. Keep the hunch alive by writing it down, then look for collisions that might fill the gap over time.
信号：
  • 无法完全清晰地表达这个想法
  • 感觉它很重要，但模糊不清
  • “这里有东西，但我无法命名它”
关键问题：
  • 你能清晰表达的部分是什么？
  • 如果这个想法完整了，它能回答什么问题？
  • 缺失的是什么——机制？示例？用例？
拓展方式： 不要强迫完成。表达你已有的部分。明确指出缺口。通过记录来保持这个想法的活力，然后在未来的碰撞中寻找填补缺口的事物。

State S4: Error-Rich

状态S4：富含错误信息

Signals:
  • Seed has been tried and failed
  • Know what doesn't work
  • Failure feels informative, not terminal
Key Questions:
  • What specifically broke? (Mechanism, context, execution?)
  • What did the failure reveal about the problem structure?
  • What would have to change for this to work?
Expansion: Mine the failure. Errors contain information about the shape of the solution. List what you learned, then look for adjacent seeds that avoid the failure modes.
信号：
  • 种子已被尝试过并失败
  • 知道什么行不通
  • 失败带来的是信息，而非终结
关键问题：
  • 具体是什么失效了？（机制、环境、执行？）
  • 失败揭示了问题结构的什么信息？
  • 要让它生效，需要改变什么？
拓展方式： 挖掘失败的价值。错误包含了解决方案形态的信息。列出你学到的东西，然后寻找能避免这些失败模式的相邻创意种子。

State S5: Exaptation Candidate

状态S5：可复用候选

Signals:
  • Seed works well for its original purpose
  • Sense it could do something else entirely
  • "This reminds me of X" where X is unrelated
Key Questions:
  • What job was this seed built to do?
  • What other jobs share similar structure?
  • Where would transplanting this seed be surprising but plausible?
Expansion: Transplant deliberately. List 5 completely different contexts. Try the seed in each. Note what changes, what survives.
信号：
  • 种子在其原始用途上表现良好
  • 感觉它可以完全用于其他目的
  • “这让我想起了X”，而X是无关的事物
关键问题：
  • 这个种子的原始用途是什么？
  • 还有哪些用途有相似的结构？
  • 将这个种子移植到哪里会既意外又合理？
拓展方式： 刻意移植。列出5个完全不同的环境。在每个环境中尝试这个种子。记录哪些改变了，哪些保留了。

Seed Expansion Phases

创意种子拓展阶段

Unlike Escape Velocity (which is sequential), use these phases as needed based on seed state:
与Escape Velocity（顺序执行）不同，根据种子状态按需使用这些阶段：

Phase S1: Seed Articulation

阶段S1：种子明确化

Before expanding, capture what you have:
  • What's the core of this seed? (One sentence)
  • What's it good for? What's it not good for?
  • Where did it come from? (Collision, adjacent step, hunch, failure, exaptation?)
  • What's your current uncertainty about it?
Output: A clear statement of the seed and what kind of seed it is.
深化前，先记录你已有的内容：
  • 这个种子的核心是什么？（一句话）
  • 它擅长什么？不擅长什么？
  • 它来自哪里？（碰撞、相邻步骤、酝酿、失败、复用？）
  • 你目前对它的不确定之处是什么？
输出： 对种子及其类型的清晰描述。

Phase S2: Adjacent Mapping

阶段S2：相邻映射

Map what's reachable from this seed:
  • What's one step away?
  • What becomes possible that wasn't before?
  • What would naturally follow if this succeeded?
  • What would someone build on top of this?
Output: 3-5 adjacent possibilities with one marked as "most interesting."
绘制从这个种子可触及的方向：
  • 下一步是什么？
  • 之前不可能的事现在变得可能了？
  • 如果这个种子成功了，接下来会自然发生什么？
  • 有人会在这个基础上构建什么？
输出： 3-5个相邻可能性，标记出“最有趣的”一个。

Phase S3: Collision Generation

阶段S3：碰撞生成

Force the seed to collide with other material:
  • Domain collision: How would [unrelated field] see this seed?
  • Artifact collision: What past work (yours or others') connects?
  • Constraint collision: What happens under unusual constraints?
  • Inversion collision: What's the opposite? What breaks if inverted?
Tool: Use 
constraint-entropy.ts domains --count 5
to generate random domains for collision.
Output: 3-5 collision results, noting which produced something interesting.
强制种子与其他事物碰撞：
  • 领域碰撞： [无关领域]会如何看待这个种子？
  • 成果碰撞： 哪些过往工作（你的或他人的）与之相关？
  • 约束碰撞： 在特殊约束下会发生什么？
  • 反转碰撞： 相反的情况是什么？反转后会打破什么？
工具： 使用
constraint-entropy.ts domains --count 5
生成随机碰撞领域。
输出： 3-5个碰撞结果，记录哪些产生了有趣的想法。

Phase S4: Gap Identification

阶段S4：缺口识别

For incomplete seeds, name what's missing:
  • What question would this seed answer if complete?
  • What's the mechanism you can't articulate?
  • What example would prove this works?
  • What would someone need to see to believe this?
Output: A clear statement of the gap. This is what you're looking for in future collisions.
针对不完整的种子，明确指出缺失的部分：
  • 如果这个种子完整了，它能回答什么问题？
  • 你无法表达的机制是什么？
  • 什么例子能证明它有效？
  • 需要展示什么才能让人相信它？
输出： 对缺口的清晰描述。这是你在未来碰撞中要寻找的东西。

Phase S5: Transplant Testing

阶段S5：移植测试

For seeds that might work elsewhere:
  • List 5 completely different contexts
  • For each: What changes? What survives? What's gained?
  • Does any transplant reveal something about the seed you didn't see?
Output: Transplant results with notes on what each revealed.
针对可能在其他环境中生效的种子：
  • 列出5个完全不同的环境
  • 对每个环境：什么改变了？什么保留了？获得了什么？
  • 有没有哪个移植揭示了你之前没看到的种子特性？
输出： 移植结果，记录每个环境揭示的信息。

Phase S6: Stress Testing

阶段S6：压力测试

Find where the seed breaks:
  • What's the worst-case application?
  • What assumption, if wrong, kills this?
  • What's the dumbest possible implementation?
  • Who would hate this? Why?
Output: Failure modes and what they reveal about the seed's actual structure.
找出种子失效的场景：
  • 最坏的应用场景是什么？
  • 哪个假设如果错误会导致这个种子失效？
  • 最愚蠢的实现方式是什么？
  • 谁会讨厌这个想法？为什么？
输出： 失效模式及其揭示的种子实际结构。

Switching Between Modes

模式切换

You may start in one mode and need to switch:
Seed → Stuck: If seed expansion produces clustering (all expansions are variations of the same thing), switch to Escape Velocity. You've hit convergence.
Stuck → Seed: If Escape Velocity produces a promising divergent idea, switch to Seed Expansion to develop it. You've found a seed worth growing.
The handoff: Escape Velocity generates candidates. Seed Expansion develops winners. They're different phases of the same ideation process.
你可能从一种模式开始，然后需要切换：
种子模式 → 僵局模式： 如果种子拓展产生了聚类（所有拓展都是同一想法的变体），切换到Escape Velocity。你遇到了趋同思维。
僵局模式 → 种子模式： 如果Escape Velocity产生了有潜力的发散想法，切换到Seed Expansion来深化它。你找到了值得培养的创意种子。
切换逻辑： Escape Velocity生成候选想法。Seed Expansion深化有潜力的想法。它们是同一创意构思过程的不同阶段。

Anti-Patterns

反模式

The Quantity Delusion

数量幻觉

Problem: Generating 50 ideas that are all variations of the same 3 approaches.
Symptom: High count, low spread. Ideas cluster visually when mapped. Easy to group into few buckets.
Fix: Stop counting. Start mapping on axes. Require at least one idea per quadrant before adding more. Measure spread, not volume.
问题： 生成50个想法，但都是3种方法的变体。
症状： 数量多，范围窄。绘制想法时会聚类。容易归为少数几类。
解决方法： 停止计数。开始在维度上映射。在添加更多想法前，每个象限至少有一个想法。衡量范围，而非数量。

The Inversion Trap

反转陷阱

Problem: "What if we did the opposite?" is lazy divergence. Opposites share the same axis—they're still convergent.
Symptom: "Instead of fast, make it slow." "Instead of automated, make it manual." "Instead of expensive, make it free."
Fix: Inversion changes magnitude, not dimension. Find a truly orthogonal axis, not the negative of the same axis. "What if speed wasn't the relevant dimension at all?"
问题： “如果我们反过来做呢？”是懒惰的发散。对立面共享同一维度——它们仍然是趋同的。
症状： “与其快，不如慢。” “与其自动化，不如手动。” “与其贵，不如免费。”
解决方法： 反转改变的是量级，而非维度。找到真正正交的维度，而非同一维度的对立面。“如果速度根本不是相关维度呢？”

The Premature Evaluation Loop

过早评估循环

Problem: Evaluating ideas while generating them. "That won't work because..." kills divergence.
Symptom: Ideas die mid-sentence. Group corrects toward "realistic" ideas. Discomfort with impractical suggestions.
Fix: Strict phase separation. Generation is not evaluation. All ideas written down before ANY filtering. Impractical ideas may contain seeds of practical ones.
问题： 在生成想法的同时评估它们。“那行不通，因为……”会扼杀发散思维。
症状： 想法在表达中途就被否定。团队倾向于“现实”的想法。对不切实际的建议感到不适。
解决方法： 严格分离阶段。生成想法不是评估想法。所有想法都记录下来后再进行筛选。不切实际的想法可能包含实用想法的种子。

The Expert Anchor

专家锚定

Problem: Domain expert's first idea dominates because of authority, not quality.
Symptom: First speaker's idea becomes the reference point. All subsequent ideas are variants or reactions. Deference to experience.
Fix: Anonymous idea generation first. Or: expert speaks last. Or: explicitly enumerate expert's default in Phase 1, then exclude it from further consideration.
问题： 领域专家的第一个想法因权威而非质量占据主导。
症状： 第一个发言者的想法成为参考点。所有后续想法都是变体或反应。对经验的过度尊重。
解决方法： 先进行匿名想法生成。或者：专家最后发言。或者：在第一阶段明确列出专家的默认想法，然后在后续思考中排除它。

The Novelty Chase

新奇追逐

Problem: Divergence for its own sake. Pursuing weird ideas that don't serve the actual function.
Symptom: Ideas are surprising but useless. Clever without being functional. "That's creative but doesn't solve the problem."
Fix: Return to Phase 2 (Function Extraction). Does the weird idea actually accomplish the required function? If not, it's not divergent—it's irrelevant. Orthogonality must serve function.
问题： 为了发散而发散。追求奇怪但不服务于实际功能的想法。
症状： 想法令人惊讶但无用。聪明但不实用。“这很有创意，但解决不了问题。”
解决方法： 回到第二阶段（功能提取）。这个奇怪的想法真的能达成所需功能吗？如果不能，它不是发散的——它是无关的。正交性必须服务于功能。

The Research Avoidance

研究回避

Problem: Brainstorming from scratch when prior art exists. Reinventing existing solutions.
Symptom: "I wonder if anyone has tried..." (they have). Ideas are novel to the group but exist elsewhere.
Fix: Research before ideation. Find 5+ existing approaches, enumerate them as defaults in Phase 1, THEN diverge. Standing on shoulders, not reinventing wheels.
问题： 已有现成方案时仍从零开始头脑风暴。重复发明已有的解决方案。
症状： “我想知道有没有人试过……”（其实已经有人试过了）。对团队来说是新想法，但在其他地方已经存在。
解决方法： 构思前先研究。找到5种以上现有方法，在第一阶段将其列为默认想法，然后再进行发散。站在巨人的肩膀上，而非重复造轮子。

Key Questions by State

可用工具

For Convergence Diagnosis (Any State)

constraint-entropy.ts

  • How many fundamentally different APPROACHES (not variations) did you generate?
  • If you grouped ideas into clusters, how many clusters would there be?
  • Did any idea make you uncomfortable? (Discomfort often signals actual divergence)
  • Would someone from a different field produce the same list?
生成随机约束以强制发散探索。
bash
undefined

For Function Lock (B2)

生成随机约束

  • What happens if the "obvious solution" doesn't exist?
  • What would you do with 10x resources? 1/10th resources?
  • If you couldn't use [assumed approach], what else achieves the function?
  • What's the actual outcome you need, separate from how you get there?
deno run --allow-read constraint-entropy.ts --count 3

For Domain Expansion (B4)

获取领域引入提示

  • What industry has definitely solved something similar?
  • What industry would find this problem trivial?
  • What would someone from [random field] notice that you're missing?
  • How does nature solve this problem? How does the military? How does a kindergarten teacher?
deno run --allow-read constraint-entropy.ts domains --count 5

For Axis Audit (B3)

生成约束组合（每个分类选一个）

  • Who is the "obvious" user/actor? Who else could it be?
  • What's the "obvious" timeframe? What if 10x slower? Instant?
  • What's the "obvious" scale? What if for 1 person? 1 million people?
  • What's the "obvious" method? What's a completely different method?
deno run --allow-read constraint-entropy.ts --combo

Available Tools

特定分类

constraint-entropy.ts

—
Generates random constraints to force divergent exploration.
bash
undefined
deno run --allow-read constraint-entropy.ts actors deno run --allow-read constraint-entropy.ts resources deno run --allow-read constraint-entropy.ts inversions deno run --allow-read constraint-entropy.ts combinations

Generate random constraints

JSON输出

deno run --allow-read constraint-entropy.ts --count 3
deno run --allow-read constraint-entropy.ts --combo --json

**分类：**
- `actors` - 执行者约束（“必须由10岁儿童使用”，“必须对敌对用户有价值”）
- `resources` - 资源约束（“预算为1/10”，“不能使用显而易见的技术”）
- `combinations` - 强制组合（“还必须解决X”，“不能做预期的事”）
- `inversions` - 视角反转（“如果失败是目标呢？”，“如果约束是特性呢？”）
- `domains` - 领域引入提示（“军事后勤会如何解决这个问题？”）

**为什么用脚本：** 真正的随机性会强制探索人类和LLM原本会回避的方向。经过筛选的约束能真正产生有用的发散想法。

---

Get domain-import prompts

示例交互

deno run --allow-read constraint-entropy.ts domains --count 5
用户： “我们需要改进团队沟通的想法。我们每次头脑风暴都产生相同的想法——Slack频道、更多会议、文档。”
诊断方法：
  1. 确定状态： B1（趋同盲区）+ B4（领域禁锢）。想法围绕沟通工具和会议结构聚类——这个问题空间的显而易见的默认选项。
  2. 阶段1 - 默认枚举： 让我们明确列出默认选项：
    • 优化Slack/Teams使用或引入新工具
    • 增加/减少会议
    • 文档维基
    • 每日站会
    • 团队建设活动
    • 办公室布局调整
    这些是领域默认选项。有效但缺乏新意。
  3. 阶段2 - 功能提取： 团队沟通必须达成什么目标？
    • F1： 信息传递给需要的人
    • F2： 问题得到解答且不造成阻塞
    • F3： 上下文随时间保留
    • F4： 信任促成坦诚对话
    • F5： 保持良好的信噪比
  4. 阶段3 - 维度映射（针对“每日站会”）：
    维度默认选项替代方案
    执行者整个团队轮换配对？跨团队？包含客户？
    时间每日早晨每周？按需触发？出现阻塞后？
    规模15分钟严格2分钟限制？每月2小时深度讨论？
    方法口头同步异步文本？视频录制？边走边聊？
  5. 阶段4 - 熵注入： 运行
    constraint-entropy.ts --combo
    ：
    • 执行者：“必须让对其持敌对态度的人受益”
    • 反转：“如果过度沟通是失败模式呢？”
    这会迫使我们思考：如果讨厌会议的人也能获取信息呢？如果我们设计的是更有效的“更少沟通”呢？
  6. 生成的发散想法：
    • 配对轮换：没有团队会议。每日轮换配对同步。信息通过网络传播，而非广播。适合内向者。
    • 决策记录：每个决策都记录上下文。沟通变为“查看记录”而非“再次询问”。优先异步。
    • 沉默预算：每个人每周有有限的“打断”次数。迫使人们优先考虑值得说的内容。
    • 祖母测试：任何通知都必须能被非技术家庭成员理解。避免行话，迫使表达清晰。
    • 上下文前置：每次更新必须以“今天加入的人会对什么感到困惑？”开头。
    这些想法是正交的——来自不同维度，而非“会议工具”的变体。

Generate constraint combo (one from each category)

你的职责

deno run --allow-read constraint-entropy.ts --combo
  1. 诊断状态 - B1-B5中的哪一个描述了当前情况？
  2. 运行相应的协议阶段 - 匹配干预措施与状态
  3. 生成随机约束 - 陷入僵局时使用熵工具
  4. 审核正交性 - 检查想法是否真正发散
  5. 映射范围，而非计数 - 衡量可能性空间的覆盖范围

Specific categories

输出持久化

deno run --allow-read constraint-entropy.ts actors deno run --allow-read constraint-entropy.ts resources deno run --allow-read constraint-entropy.ts inversions deno run --allow-read constraint-entropy.ts combinations
本技能会将主要输出写入文件，以便跨会话保存工作。

JSON output

输出位置发现

deno run --allow-read constraint-entropy.ts --combo --json

**Categories:**
- `actors` - Who constraints ("A 10-year-old must use it", "Someone hostile to it")
- `resources` - Resource constraints ("1/10th budget", "Can't use obvious technology")
- `combinations` - Forced combinations ("Must also solve X", "Must NOT do expected thing")
- `inversions` - Perspective flips ("What if failure is the goal?", "What if the constraint is the feature?")
- `domains` - Domain import prompts ("How would military logistics solve this?")

**Why a script:** True randomness forces exploration that humans and LLMs would otherwise avoid. Curated constraints that actually produce useful divergence.

---
在进行任何其他工作之前：
  1. 检查项目中的
    context/output-config.md
  2. 如果找到，查看本技能的条目
  3. 如果未找到或没有本技能的条目，先询问用户：
    • “我应该将本次头脑风暴的输出保存到哪里？”
    • 建议：
      explorations/brainstorming/
      或项目的合理位置
  4. 保存用户的偏好：
    • 如果存在上下文网络，保存到
      context/output-config.md
    • 否则保存到项目根目录的
      .brainstorming-output.md

Example Interaction

主要输出

User: "We need ideas for improving team communication. We keep generating the same ideas—Slack channels, more meetings, documentation."
Diagnostic approach:
  1. Identify state: B1 (Convergence Blindness) + B4 (Domain Imprisonment). Ideas cluster around communication tools and meeting structures—the obvious defaults for this problem space.
  2. Phase 1 - Default Enumeration: Let's name the defaults explicitly:
    • Better Slack/Teams usage or new tool
    • More meetings / fewer meetings
    • Documentation wiki
    • Daily standups
    • Team-building activities
    • Office layout changes
    These are the genre defaults. Valid but predictable.
  3. Phase 2 - Function Extraction: What must team communication accomplish?
    • F1: Information reaches people who need it
    • F2: Questions get answered without blocking
    • F3: Context preserved across time
    • F4: Trust enables hard conversations
    • F5: Signal-to-noise ratio stays manageable
  4. Phase 3 - Axis Mapping (for "daily standup"):
    AxisDefaultAlternatives
    WhoWhole teamRotating pairs? Cross-team? Include customers?
    WhenDaily morningWeekly? On-demand trigger? After blockers?
    Scale15 minutes2-minute hard limit? 2-hour deep dive monthly?
    MethodVerbal syncAsync text? Video recordings? Walk-and-talk?
  5. Phase 4 - Entropy Injection: Running 
    constraint-entropy.ts --combo
    :
    • Actor: "Someone who is hostile to it must benefit"
    • Inversion: "What if over-communication was the failure mode?"
    This forces: What if people who hate meetings still get the information? What if we designed for LESS communication that's more effective?
  6. Divergent ideas generated:
    • Pair rotations: No team meetings. Rotating pairs sync daily. Information spreads through network, not broadcast. Introverts prefer.
    • Decision records: Every decision documented with context. Communication becomes "read the record" not "ask again." Async-first.
    • Silence budget: Each person has limited "interrupt" tokens per week. Forces prioritization of what's worth saying.
    • The grandmother test: Any announcement understandable to a non-technical family member. Catches jargon, forces clarity.
    • Context-forward: Every update MUST start with "what would confuse someone joining today?"
    These ideas are orthogonal—different axes, not variations of "meeting tools."
对于本技能，需要持久化：
  • 枚举的默认选项（阶段1输出）
  • 功能提取结果（阶段2）
  • 带有探索过的替代方案的维度映射（阶段3）
  • 应用的熵约束和生成的想法（阶段4）
  • 正交性审核结果 - 哪些想法真正发散（阶段5）
  • 选定的/有潜力的想法及其理由

What You Do

对话与文件

  1. Diagnose the state - Which of B1-B5 describes the current situation?
  2. Run appropriate protocol phase - Match intervention to state
  3. Generate random constraints - Use entropy tool when stuck
  4. Audit for orthogonality - Check if ideas are genuinely divergent
  5. Map spread, not count - Measure coverage of possibility space
存入文件保留在对话中
枚举的默认选项关于哪些默认选项难以摆脱的讨论
维度映射与调整约束选择的迭代
生成的发散想法对想法的实时反馈
正交性评估澄清问题
有潜力的组合被舍弃的选项

Output Persistence

文件命名

This skill writes primary output to files so work persists across sessions.
格式：
{topic}-{date}.md
示例：
product-naming-2025-01-15.md

Output Discovery

你不做的事

Before doing any other work:
  1. Check for 
    context/output-config.md
    in the project
  2. If found, look for this skill's entry
  3. If not found or no entry for this skill, ask the user first:
    • "Where should I save output from this brainstorming session?"
    • Suggest: 
      explorations/brainstorming/
      or a sensible location for this project
  4. Store the user's preference:
    • In 
      context/output-config.md
      if context network exists
    • In 
      .brainstorming-output.md
      at project root otherwise
  • 为用户生成想法（提供流程，而非内容）
  • 在生成想法时评估它们（分离阶段）
  • 跳过默认枚举（不可见的默认选项无法摆脱）
  • 为了新奇而新奇（奇怪≠有用）
  • 替代领域专业知识（与现有知识协作，而非替代）
  • 保证产生好想法（保证探索可能性空间）
  • 接受“我们已经试过所有方法”（可能只是同一方法的变体）

Primary Output

—
For this skill, persist:
  • Defaults enumerated (Phase 1 output)
  • Function extraction results (Phase 2)
  • Axis mapping with alternatives explored (Phase 3)
  • Entropy constraints applied and ideas generated (Phase 4)
  • Orthogonality audit results - which ideas are genuinely divergent (Phase 5)
  • Selected/promising ideas with rationale
—

Conversation vs. File

—
Goes to FileStays in Conversation
Enumerated defaultsDiscussion of which defaults feel sticky
Axis map with rotationsIteration on constraint choices
Generated divergent ideasReal-time feedback on ideas
Orthogonality assessmentsClarifying questions
Promising combinationsDiscarded options
—

File Naming

—
Pattern: 
{topic}-{date}.md
Example: 
product-naming-2025-01-15.md
—

What You Do NOT Do

—
  • Generate ideas FOR the user (provide process, not content)
  • Evaluate ideas during generation (separate phases)
  • Skip default enumeration (invisible defaults can't be escaped)
  • Chase novelty without function (weird ≠ useful)
  • Replace domain expertise (work WITH knowledge, not instead of)
  • Guarantee good ideas (guarantee exploration of possibility space)
  • Accept "we've tried everything" (probably variations of same approach)
—