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From skills-for-humanity
Surfaces and audits the mental models that drive perception and decision-making. Helps uncover hidden assumptions, blind spots, and outdated models to improve reasoning and problem-solving.
npx claudepluginhub human-avatar/skills-for-humanityHow this skill is triggered — by the user, by Claude, or both
Slash command
/skills-for-humanity:s4h-cognition-mental-modelsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Every perception, judgment, and decision runs through a mental model. Philip Johnson-Laird's model theory, developed through decades of research at Princeton, demonstrated that human reasoning does not operate on formal logical rules — it operates on internal simulations of situations. We construct small-scale models of reality, reason by running those models mentally, and check conclusions aga...
Challenges assumptions, applies mental models like SWOT, first principles, and inversion, and structures reasoning to sharpen decisions and solve complex problems.
Surfaces beliefs, assumptions, stories, and values shaping a system. Useful after an iceberg analysis, during conflict reflection, curriculum redesign, or culture examination.
Applies Naval Ravikant's mental models like compound interest, inversion, principal-agent problem to analyze complex situations, counterintuitive ideas, or business/life decisions.
Share bugs, ideas, or general feedback.
Every perception, judgment, and decision runs through a mental model. Philip Johnson-Laird's model theory, developed through decades of research at Princeton, demonstrated that human reasoning does not operate on formal logical rules — it operates on internal simulations of situations. We construct small-scale models of reality, reason by running those models mentally, and check conclusions against them. The problem is not that we use mental models — it's that the models become invisible.
When a mental model is invisible, it cannot be examined. Its assumptions are treated as facts. Its gaps become blind spots. Its distortions shape what evidence we notice and what options we can imagine, all without our awareness. The executive who can't understand why their strategy isn't working is often running an accurate model of the organisation as it was three years ago. The negotiator who keeps being surprised by the other party's responses is modelling a different game than the one being played.
This skill makes the implicit explicit. It identifies which models are active in a situation, extracts their assumptions, tests those assumptions against available evidence, and identifies where the model is incomplete, outdated, or simply wrong. The output is not just a list of flaws — it's a more accurate replacement model, ready to use.
Step 1: Identify the Model in Use Ask: what are the implicit beliefs about how this domain works that are driving the current perception or decision? A mental model has three components:
Name the current model in explicit terms. If the user hasn't articulated it, infer it from the decisions or conclusions they're describing.
Framing check: Confirm the specific model and situation before continuing. State what you've identified — the domain being modelled and the decision or belief the model is shaping — in one sentence, then use AskUserQuestion:
Step 2: Surface the Assumptions Dig into the model's foundations. For each core relationship in the model, ask: what has to be true for this to hold? List these as explicit assumptions. Common categories:
Step 3: Test Against Evidence For each assumption, ask: what evidence supports it? What evidence contradicts it? What evidence would you expect to see if it were true that you don't actually see? Be specific. Surface the evidence that the current model is filtering out or explaining away.
Before narrowing: Use AskUserQuestion to confirm the assumption list before assessing accuracy:
Step 4: Identify Gaps and Distortions What does the current model systematically miss or misrepresent? Two types:
Step 5: Construct the Updated Model Revise the model to incorporate what the audit found. State the updated model explicitly using the same three-component structure (entities, relationships, dynamics). Identify what changes in practical terms: what decisions would look different? What would you now notice that you were filtering out?
Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what model is being audited and what the most consequential gap or distortion appears to be — then ask:
Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.
Current Model (Explicit)
Assumption Audit
| Assumption | Supported by | Contradicted by | Status |
|---|---|---|---|
Gaps: [What is missing from the model — entities, relationships, dynamics not included]
Distortions: [What is present but represented inaccurately]
Updated Model
Practical Implications: [What decisions or perceptions would change under the updated model]
Mental model audits are uncomfortable by design — the point is to find where your current understanding is wrong. Frame the output as an upgrade, not a repudiation. The goal is not to discard the model (it likely captures real structure) but to make it more accurate.
The nearest neighbor is /s4h-epistemology-justification — which examines whether beliefs are adequately grounded. Mental model auditing operates at the structural level (the model itself), while epistemology-justification operates at the evidentiary level (whether specific claims are supported). Use both when a deeply held belief is driving a high-stakes decision.
For beliefs driven primarily by emotional or identity stakes rather than cognitive models, /s4h-emotional-trust-audit and /s4h-identity-values-clarification are more appropriate entry points.
After delivering this output, use AskUserQuestion to offer the next move:
/s4h-cognition-metacognition — Apply thinking-about-thinking to monitor how you're now using the updated model/s4h-epistemology-justification — Examine whether the updated model's key claims are adequately supported/s4h-decision-option-mapping — Use the updated model to generate options that the old model couldn't see