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Agent Skill · 研究检索

lateral-thinking(横向思维)

lateral-thinking 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

lateral-thinking 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 58,252。

总安装 419周安装 18GitHub Stars 6下载 147核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:lateral-thinking(横向思维)
来源仓库:https://github.com/andurilcode/skills
仓库路径:skills/lateral-thinking
安装命令:
npx skills add https://github.com/andurilcode/skills --skill lateral-thinking
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/andurilcode/skills --skill lateral-thinking

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

横向移动思维方向,跳出垂直深挖范式,通过侧向联想与模式转换寻找新解法。适用于僵局突破、创意生成与组织变革,典型方法包括强制关联、随机刺激与逆向思考。当垂直思考陷入重复失败时启动,目标是创造“不同地点的洞”而非更深的旧洞。需营造非评判环境,鼓励非常规答案与跨界类比。不适合需要严谨推导或高风险决策的场景,更多用于启发阶段。

来源仓库内路径:skills/lateral-thinking

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/andurilcode/skills --skill lateral-thinking;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

lateral-thinking 怎么安装?

在终端执行 npx skills add https://github.com/andurilcode/skills --skill lateral-thinking。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

lateral-thinking 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

lateral-thinking 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

lateral-thinking 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

lateral-thinking 用的人多吗?

公开数据是累计安装 419 次,近一周安装 18 次,下载 147 次,GitHub 6 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

lateral-thinking 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
lateral-thinking

横向思维 · 本页

4196需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Lateral Thinking

Core principle: Most thinking is vertical — digging deeper in the same direction, refining what already exists. Lateral thinking moves *sideways* — deliberately escaping the dominant pattern to find a different place to dig. The goal is not a better version of the current solution. It's a different solution entirely.

*"You cannot dig a hole in a different place by digging the same hole deeper."* — Edward de Bono

When Vertical Thinking Fails

Vertical thinking is efficient when the problem is well-defined and the solution space is known. It fails when:

  • The problem keeps recurring despite fixes (the frame is wrong)
  • All solutions feel like variations of the same idea
  • The best available option is "least bad"
  • Everyone in the room agrees on the approach (groupthink + vertical thinking)
  • The problem is genuinely novel

When any of these are true, stop digging. Move laterally.


Core Techniques

1. Random Entry

Introduce a completely unrelated stimulus — a random word, image, or object — and force a connection to the problem.

Process:

  1. Pick a random word (or use one: *bridge / fog / anchor / seed / mirror / friction*)
  2. List properties or associations of that word
  3. Force-connect each property to the problem
  4. Don't filter — connections that seem absurd often lead somewhere real

Why it works: The random stimulus activates parts of the solution space the brain wouldn't reach through logical progression.

Example: Problem: *how do we reduce agent pipeline errors?* Random word: *filter* Associations: removes impurities, selective, layered, passive → Idea: add a passive validation layer between agents that only flags, never blocks — removes errors without slowing the flow


2. Provocation (Po)

Make a deliberately absurd, impossible, or reversed statement about the problem — then extract useful ideas from it.

Provocations use the operator "Po" (signals it's a provocation, not a claim):

  • *Po: the agent has no memory at all*
  • *Po: users pay us to make mistakes*
  • *Po: the bottleneck is the solution*
  • *Po: we do the opposite of what we're doing now*

Process:

  1. State the provocation (make it extreme — mild provocations produce mild ideas)
  2. Ask: *"What would have to be true for this to work?"*
  3. Ask: *"What intermediate ideas does this generate?"*
  4. Extract the usable concepts, even if the provocation itself is impossible

Example: Problem: *onboarding takes too long* Po: *users onboard themselves before they meet us* → Idea: pre-onboarding flow that users complete asynchronously, so the first live interaction starts mid-process, not at the beginning


3. Challenge

Question every assumption about *why* things are done the way they are — not to criticize, but to open alternatives.

Challenge questions:

  • *"Why is it done this way?"*
  • *"Does it have to be done at all?"*
  • *"Does it have to be done in this order?"*
  • *"Does it have to be done by this person/system?"*
  • *"Does it have to be done at this point in the process?"*

Every "because that's how it's done" is a candidate for lateral escape.

Distinguish:

  • Necessary constraints: Remove them and the goal disappears (challenge won't help here)
  • Arbitrary constraints: Historical, habitual, or inherited — fertile ground for lateral thinking

4. Alternatives (Fixed Point)

Fix the goal but change everything else. List as many alternative ways to achieve the same outcome as possible — without evaluating any of them until the list is complete.

Process:

  1. State the fixed point: *"The goal is [outcome]"*
  2. Generate 10+ routes to that outcome — including the obvious, the strange, and the impractical
  3. Only evaluate after the full list exists (evaluation during generation kills lateral thinking)
  4. Look for hybrids between non-obvious options

Quota thinking: Set a number before you start ("we need 15 alternatives") — it forces you past the first 3–4 obvious answers into genuinely new territory.


5. Concept Extraction

Abstract the current solution to its core concept, then find other ways to deliver the same concept.

Process:

  1. Describe the current solution in one sentence
  2. Extract the underlying concept: *"The concept here is [X]"*
  3. List other implementations of that same concept
  4. Pick the most promising and develop it

Example: Current solution: *weekly sync meeting to align the team* Core concept: *shared awareness of state* Alternative implementations: async status page, ambient dashboard, daily digest message, visual kanban, automated diff reports → Some of these are faster, quieter, and more persistent than a meeting


6. Reversal

Reverse the problem statement entirely. Then ask what that reversed world looks like, and work backwards to generate ideas.

Process:

  1. State the problem: *"How do we get more users to complete onboarding?"*
  2. Reverse it: *"How do we get users to abandon onboarding?"*
  3. List everything that would cause the reversed outcome
  4. Invert those back into ideas for the original problem

Why it works: The reversed problem is often easier to answer — and inverting the answers surfaces ideas that pure forward thinking wouldn't reach.


Output Format

🔀 Dominant Pattern Identification

Before generating alternatives, name the dominant pattern being broken:

  • *"The current thinking is: [frame/assumption/direction]"*
  • *"The rut is: [what keeps pulling solutions back to the same place]"*

💡 Generated Alternatives

Present ideas grouped by technique used. For each:

  • Idea: One-sentence description
  • Origin: Which technique generated it (signals it's deliberate, not random)
  • Kernel: The useful concept inside, even if the idea as stated is impractical

Don't filter during generation. Quantity first, quality second.

🌱 Most Promising Concepts

After generation, select the 2–4 ideas with the most potential:

  • What makes this worth developing?
  • What would need to be true for it to work?
  • What's the next concrete step to test it?

⚠️ Dominant Pattern Traps to Watch

Note any ideas that are actually vertical (refined versions of the existing approach disguised as new ideas). Flag and set aside — they belong in a different conversation.


Rules for Lateral Thinking Sessions

  1. Suspend judgment during generation — evaluation kills divergence
  2. Welcome the absurd — impractical ideas often contain useful kernels
  3. Quantity before quality — the goal is to move past the first 3 obvious answers
  4. Build, don't reject — "yes, and..." before "yes, but..."
  5. Name the dominant pattern first — you can't escape a rut you haven't identified

Thinking Triggers

  • *"What would this look like if we had no history with the problem?"*
  • *"Who solves a completely different problem in a way that could apply here?"*
  • *"What's the most counterintuitive thing we could do?"*
  • *"If we couldn't use the current approach at all, what would we do?"*
  • *"What would a 10-year-old suggest? What about someone from a completely different industry?"*
  • *"What are we not allowed to question — and why?"*

Example Applications

  • "We've tried everything to reduce churn" → Challenge every assumption about what "reducing churn" means, then use Reversal to find what causes people to *want* to leave — inverted back into retention ideas
  • "Our pipeline is slow and we don't know how to speed it up" → Random Entry + Provocation to break out of "optimize each step" thinking
  • "We need a new feature but everything feels incremental" → Concept Extraction to abstract what users are really hiring current features to do, then explore radically different implementations
  • "The team keeps proposing the same solutions in retros" → Fixed Point alternatives with a quota of 15 — forces past the obvious into genuinely new territory

平台分布

Codex

36.46%
按下载量换算54

Claude

28.32%
按下载量换算42

Cursor

20.85%
按下载量换算31

Gemini CLI

8.77%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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