mungers-lattice

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.

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "mungers-lattice" with this command: npx skills add hexbee/hello-skills/hexbee-hello-skills-mungers-lattice

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.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Research

research-content-router

No summary provided by upstream source.

Repository SourceNeeds Review
General

deep-productivity

No summary provided by upstream source.

Repository SourceNeeds Review
254-hexbee
General

cross-domain-thinking-toolbox

No summary provided by upstream source.

Repository SourceNeeds Review
General

learning-first-principles

No summary provided by upstream source.

Repository SourceNeeds Review