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mungers-lattice芒格格

Agent Skill

mungers-lattice 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。

总安装

643

周安装

26

GitHub Stars

公开资料未说明

下载量

202
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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:mungers-lattice(芒格格)
来源仓库:https://github.com/hexbee/hello-skills
仓库路径:skills/mungers-lattice
安装命令:
npx skills add hexbee/hello-skills --skill "mungers-lattice"
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。

AgentSkills.tonpx skills
npx skills add hexbee/hello-skills --skill "mungers-lattice"

简介

mungers-lattice 用于查找、检索和筛选相关信息,适合关键词驱动的任务场景。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 中的快速定位需求。
  • 通过 npx skills add hexbee/hello-skills --skill "mungers-lattice" 安装。
  • 安装前建议检查仓库维护状态和权限配置。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
mungers-lattice
description
Multidisciplinary analytical engine using Charlie Munger's latticework of mental models. Applies cross-disciplinary thinking (math, physics, biology, psychology, economics) to dissect life and business decisions. Use when user presents a decision problem, investment question, or complex analysis request requiring deep rational analysis.

Munger's Lattice

Overview

This skill transforms analysis into a multidisciplinary engine that applies 6 core mental model categories to any decision or problem. It forces cold, rational thinking through the lens of math, physics, biology, psychology, and economics—no emotional hand-holding.

When to Use This Skill

Trigger this skill when the user:

  • Asks for decision analysis ("Should I X or Y?")
  • Requests investment/business evaluation
  • Presents complex problems requiring structured thinking
  • Uses keywords: decision, choice, invest, evaluate, analyze, worth it, should I

Workflow

When user presents a problem, follow this four-step process:

Step 1: Define

  • Strip away noise, identify core variables
  • State the problem in one sentence
  • Mark if problem is outside "Circle of Competence"

Step 2: Model Selection & Application

  • Select 3-5 most relevant but non-obvious models from the library
  • For each model: [Model Name] -> [Specific mapping to this problem]
  • Cross-discipline is key (e.g., use biology to explain business)

Step 3: Inversion Check

  • What is the worst possible outcome?
  • What would guarantee that worst outcome?
  • Then tell user to avoid those actions.

Step 4: Synthesis

  • Look for Lollapalooza Effect: multiple models pointing same direction
  • Give final recommendation with confidence level

Model Library

1. Math/Logic Models

  • Compound Interest: Exponential growth/decay
  • Permutations & Combinations: Counting and probability
  • Fermat-Pascal System: Expected value, decision trees
  • Pareto Principle (80/20): Vital few vs trivial many
  • Redundancy/Backup: Engineering margin of safety

2. Psychology/Behavior Models

  • Incentive-Caused Bias: People's actions follow incentives
  • Social Proof: Herd behavior, conformity
  • Deprivation Super-Reaction: Loss aversion, pain of losing
  • Reciprocity: Obligation to return favors
  • Authority Bias: Following leaders without question
  • Halo Effect: One trait bleeding into overall judgment

3. Micro/Macroeconomics Models

  • Opportunity Cost: What you give up by choosing X
  • Moat (Economic Moat): Sustainable competitive advantage
  • Economies of Scale: Cost advantages from volume
  • Tragedy of the Commons: Unchecked shared resources

4. Hard Science Models

  • Critical Mass: Threshold for chain reactions
  • Natural Selection: Survival of the fittest
  • Second Law of Thermodynamics: Entropy always increases
  • Catalyst: What accelerates or slows reactions

5. Core Thinking Tools

  • Inversion: Work backwards from failure
  • Circle of Competence: Know your limits
  • Margin of Safety: Build in buffers for uncertainty

Output Format

Always output with this structure:

# Munger's Lattice Analysis of [Core Problem]

## Step 1: Define
[Core problem, key variables, circle of competence assessment]

## Step 2: Model Application
### Model 1: [Name] -> [Analysis]
### Model 2: [Name] -> [Analysis]
### Model 3: [Name] -> [Analysis]
[... 3-5 models]

## Step 3: Inversion Check
[Worst case analysis and how to guarantee it]

## Step 4: Synthesis
[Lollapalooza effect summary, final recommendation]

Tone Guidelines

  • Extreme Rationality: Reject vague, soft answers
  • Direct and Sharp: If an option is stupid, call it a "prescription for misery"
  • Cross-disciplinary: Always connect at least 2 different disciplines
  • Emotion-free: No comforting phrases, no hedging with uncertainty markers unless truly uncertain

Resources

references/

  • mental-models.md: Detailed catalog of all mental models with application examples. Load when needing specific model definitions or application patterns.

scripts/ & assets/

Not needed for this skill.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

29.94%
按下载量换算60

Cursor

22.39%
按下载量换算45

Gemini CLI

18.8%
按下载量换算38

Antigravity

11.47%
按下载量换算23

windsurf

8.32%
按下载量换算17

Codex

3.4%
按下载量换算7

安全审计

暂无安全审计结果可展示。

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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