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Get Started Free →Apply a latticework of mental models from multiple disciplines to improve decision quality. Use this skill when the user needs to think more clearly, avoid cognitive blind spots, apply cross-disciplinary reasoning, or evaluate a complex decision from multiple angles — even if they say 'how should I think about this', 'what am I missing', 'give me a different perspective', or 'what frameworks apply here'.
.claude/skills/asgard-ai-platform-meta-mental-models/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 30% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 38% | 0% |
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.From Physics/Engineering | Model | Principle | Application | |-------|-----------|------------| | Inversion | Instead of "how do I succeed?", ask "how would I fail?" Then avoid that. | Risk management, pre-mortem | | Second-order effects | Every action has consequences, which have consequences. Think two steps ahead. | Policy design, strategy | | Entropy | Systems tend toward disorder without energy input. Things decay by default. | Maintenance, quality, relationships |
From Biology | Model | Principle | Application | |-------|-----------|------------| | Evolution/natural selection | What survives is what's adapted, not what's "best" in absolute terms. | Market competition, product-market fit | | Red Queen effect | You must keep improving just to stay in the same place (because competitors improve too). | Competitive strategy | | Niche specialization | Generalists and specialists coexist because they serve different niches. | Market positioning, career strategy |
From Mathematics/Statistics | Model | Principle | Application | |-------|-----------|------------| | Pareto principle (80/20) | ~80% of effects come from ~20% of causes. | Prioritization, resource allocation | | Regression to the mean | Extreme results tend to be followed by more average ones. | Performance evaluation, forecasting | | Bayes' theorem | Update beliefs based on new evidence, weighted by prior probability. | Decision-making under uncertainty |
From Psychology | Model | Principle | Application | |-------|-----------|------------| | Incentive-caused bias | People do what they're incentivized to do, not what you ask them to do. | Compensation design, policy design | | Circle of competence | Know what you know and what you don't. Stay within your expertise for high-stakes decisions. | Self-awareness, delegation | | Hanlon's razor | Never attribute to malice what is adequately explained by ignorance or incompetence. | Conflict resolution, workplace dynamics |
markdown# 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}
references/mental-models-catalog.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 18,936 | 28,127 | +49% | 1 | 1 | 0% | 2,883 | 3,611 | +25% | 0 | 0 | — |
case-18 | pass→fail | 20,986 | 16,723 | -20% | 1 | 1 | 0% | 3,154 | 3,610 | +14% | 0 | 0 | — |
case-02 | fail→pass | 18,651 | 18,182 | -3% | 1 | 1 | 0% | 2,852 | 3,715 | +30% | 0 | 0 | — |
case-03 | fail→pass | 21,968 | 18,010 | -18% | 1 | 1 | 0% | 3,327 | 3,630 | +9% | 0 | 0 | — |
case-04 | fail→fail | 26,781 | 16,111 | -40% | 1 | 1 | 0% | 4,137 | 3,462 | -16% | 0 | 0 | — |
case-05 | fail→pass | 18,189 | 15,535 | -15% | 1 | 1 | 0% | 2,743 | 3,303 | +20% | 0 | 0 | — |
case-19 | pass→pass | 15,017 | 16,646 | +11% | 1 | 1 | 0% | 2,516 | 3,624 | +44% | 0 | 0 | — |
case-06 | pass→pass | 17,673 | 13,713 | -22% | 1 | 1 | 0% | 2,598 | 2,974 | +14% | 0 | 0 | — |
case-07 | pass→pass | 16,830 | 13,796 | -18% | 1 | 1 | 0% | 2,520 | 3,146 | +25% | 0 | 0 | — |
case-08 | pass→pass | 16,979 | 17,412 | +3% | 1 | 1 | 0% | 2,699 | 3,616 | +34% | 0 | 0 | — |
case-09 | fail→pass | 12,616 | 11,652 | -8% | 1 | 1 | 0% | 2,100 | 2,888 | +38% | 0 | 0 | — |
case-10 | fail→pass | 16,182 | 17,962 | +11% | 1 | 1 | 0% | 2,513 | 3,992 | +59% | 0 | 0 | — |
case-11 | pass→pass | 18,724 | 18,886 | +1% | 1 | 1 | 0% | 3,058 | 3,915 | +28% | 0 | 0 | — |
case-12 | pass→pass | 15,940 | 13,494 | -15% | 1 | 1 | 0% | 2,417 | 3,208 | +33% | 0 | 0 | — |
case-13 | fail→pass | 16,506 | 14,654 | -11% | 1 | 1 | 0% | 2,479 | 3,293 | +33% | 0 | 0 | — |
case-14 | fail→pass | 15,436 | 12,600 | -18% | 1 | 1 | 0% | 2,340 | 2,968 | +27% | 0 | 0 | — |
case-15 | fail→pass | 17,905 | 12,772 | -29% | 1 | 1 | 0% | 2,730 | 2,954 | +8% | 0 | 0 | — |
case-16 | pass→pass | 20,730 | 11,629 | -44% | 1 | 1 | 0% | 3,190 | 2,771 | -13% | 0 | 0 | — |
case-17 | fail→fail | 16,169 | 13,648 | -16% | 1 | 1 | 0% | 2,515 | 3,026 | +20% | 0 | 0 | — |
case-20 | pass→pass | 6,947 | 9,955 | +43% | 1 | 1 | 0% | 1,266 | 2,793 | +121% | 0 | 0 | — |
case-21 | pass→fail | 15,597 | 16,010 | +3% | 1 | 1 | 0% | 2,423 | 3,604 | +49% | 0 | 0 | — |
case-22 | pass→pass | 15,457 | 11,950 | -23% | 1 | 1 | 0% | 2,277 | 2,692 | +18% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +32 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.