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meta-mental-models

meta-mental-models

github.com/asgard-ai-platformProductivity24340 installsUpdated 21w ago
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About meta-mental-models

A cross-disciplinary toolkit for applying multiple mental models to important decisions. It helps users avoid relying on one perspective and use disagreements between models to identify key trade-offs.

Key features

  1. 01Covers models from four disciplines
  2. 02Selects models suited to a problem
  3. 03Compares agreement and conflict across models
  4. 04Uses disagreement to surface trade-offs

Use cases

  1. 01Assess an important decision from several angles
  2. 02Add risk and consequence analysis to strategy work
  3. 03Compare judgments under uncertainty

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/asgard-ai-platform/skills --skill meta-mental-models

After installing, type/meta-mental-modelsin Claude Code to confirm it loaded.

Bundled filesexamples/sample_scenario.mdreferences/mental-models-catalog.md

Details

From the project README

Mental Models Toolkit

Framework

IRON LAW: Use Multiple Models, Not Just Your Favorite

"To a man with a hammer, everything looks like a nail." (Munger)
A single mental model creates blind spots. Apply 2-3 models from
DIFFERENT disciplines to any important decision. Where models agree,
confidence is high. Where they disagree, the disagreement reveals
the most important dimension of the decision.
Core Mental Models (Cross-Disciplinary)

From Physics/Engineering

ModelPrincipleApplication
InversionInstead of "how do I succeed?", ask "how would I fail?" Then avoid that.Risk management, pre-mortem
Second-order effectsEvery action has consequences, which have consequences. Think two steps ahead.Policy design, strategy
EntropySystems tend toward disorder without energy input. Things decay by default.Maintenance, quality, relationships

From Biology

ModelPrincipleApplication
Evolution/natural selectionWhat survives is what's adapted, not what's "best" in absolute terms.Market competition, product-market fit
Red Queen effectYou must keep improving just to stay in the same place (because competitors improve too).Competitive strategy
Niche specializationGeneralists and specialists coexist because they serve different niches.Market positioning, career strategy

From Mathematics/Statistics

ModelPrincipleApplication
Pareto principle (80/20)~80% of effects come from ~20% of causes.Prioritization, resource allocation
Regression to the meanExtreme results tend to be followed by more average ones.Performance evaluation, forecasting
Bayes' theoremUpdate beliefs based on new evidence, weighted by prior probability.Decision-making under uncertainty

From Psychology

ModelPrincipleApplication
Incentive-caused biasPeople do what they're incentivized to do, not what you ask them to do.Compensation design, policy design
Circle of competenceKnow what you know and what you don't. Stay within your expertise for high-stakes decisions.Self-awareness, delegation
Hanlon's razorNever attribute to malice what is adequately explained by ignorance or incompetence.Conflict resolution, workplace dynamics
Application Method
  1. State the decision or problem
  2. Select 2-3 relevant models from different disciplines
  3. Apply each model to the situation — what does it suggest?
  4. Compare conclusions — where do they agree? Where do they disagree?
  5. Synthesize — the disagreement reveals the key trade-off to resolve

Output Format

# Multi-Model Analysis: {Decision}

## Models Applied
| Model | Discipline | Insight |
|-------|-----------|---------|
| {model 1} | {field} | {what this model says about the situation} |
| {model 2} | {field} | {what this model says} |
| {model 3} | {field} | {what this model says} |

## Convergence
{Where models agree — high confidence}

## Divergence
{Where models disagree — key trade-off to resolve}

## Synthesis
{Recommended decision based on multi-model analysis}

Gotchas

  • Models are simplifications: Every model omits something. The map is not the territory. Use models as lenses, not as truth.
  • Model inventory grows over time: Start with 10-15 core models. Add new ones as you encounter new domains. Quality of application matters more than quantity of models.
  • Some models conflict by design: Inversion says "avoid failure." Evolution says "failure is how you learn." The conflict is resolved by context: avoid catastrophic failure, embrace recoverable failure.
  • Don't force-fit: Not every model applies to every situation. If a model doesn't naturally illuminate the problem, skip it — don't stretch it to fit.

References

  • For expanded mental models catalog (50+), see references/mental-models-catalog.md

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