smithery.ai

mental-models

Apply Charlie Munger's latticework of mental models to any problem. Use when user requests decision analysis, says "help me think", "apply mental model", mentions model names (inversion, bottlenecks, second-order thinking), or needs structured thinking frameworks.

First seen Apr 30, 2026

Installation

$ npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,859 B
  • docs SUMMARY.md 285 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 3 installs

SKILL.md

Mental Models

Apply 98 cognitive frameworks from multiple disciplines to analyze problems, make decisions, and think more clearly.

This skill is backed by the mental-models CLI — a single command that does model selection, lookup, and structured application. The CLI is the fast path; the fallback is reading files directly. Both work. Prefer the CLI.

When to Activate

  • User names a specific model ("apply inversion", "use bottlenecks")
  • User asks "help me think through X" or "what model fits X"
  • User requests decision analysis, trade-off evaluation, or structured reasoning
  • User describes a complex/ambiguous problem and wants a framework

Preflight: is the CLI available?

Run once per session:

mental-models doctor --json

If it returns {"ok": true, ...} → use the CLI workflow below.

If the command is not found → try uvx mental-models doctor --json (runs from PyPI without install). If that also fails, fall back to the File Fallback section at the bottom of this doc — you can still do everything by reading files directly from models/, REFERENCE.md, and PATTERNS.md.

CLI Workflow (preferred)

Step 1 — Select models for the problem

mental-models select "<user's question or paraphrased problem>" -k 5 --json

Returns a JSON object with a models array. Each entry has slug, name, category, description, keywords, path. Pick 2–3 that best fit — prefer cross-category coverage (that's the latticework).

Step 2 — Get structured guidance for each chosen model

mental-models apply <slug> --problem "<user's problem>" --json

Returns:

  • description — what the model is
  • thinking_steps — the sequential framework (walk these verbatim, don't paraphrase)
  • coaching_questions — prompts to deepen the analysis
  • whentoavoid — failure modes (always check and surface if relevant)

Step 3 — Synthesize

  • Walk each model's thinking_steps against the user's facts
  • Show where the models agree, where they disagree
  • End with 3–5 concrete, actionable next steps
  • Name any "when to avoid" conditions that apply to this case

Other useful CLI commands

mental-models get <slug>                 # full markdown for deep reading
mental-models get <slug> --field keywords
mental-models list --category "Human Nature"
mental-models categories
mental-models which                      # resolve data path

All commands support --json. Exit codes: 0 ok, 2 not found, 3 bad args.

Discovery Heuristics (before calling select)

Match the problem's shape to bias your query terms:

  • Risk / uncertainty / reversibility → inversion, probabilistic thinking, margin of safety
  • Stuck / can't see options → first principles, second-order thinking, reframing
  • Conflict / negotiation / competition → incentives, asymmetric warfare, trade-offs
  • Complex system / unintended effects → feedback loops, emergence, bottlenecks, leverage
  • Performance / optimization → bottlenecks, diminishing returns, efficiency
  • People / team / behavior → incentives, social proof, biases
  • Communication / persuasion → framing, audience, contrast

Full decision trees: PATTERNS.md. Per-category deep walkthroughs: REFERENCE.md. Worked examples: examples/.

Core Guidelines

  1. Max 3 models per analysis — quality over quantity
  2. Follow thinking_steps verbatim — don't paraphrase the framework away
  3. Always check whentoavoid — warn the user if the model misfits
  4. Latticework: show how chosen models connect and where they disagree
  5. Be actionable: end with concrete next steps, not theory
  6. Name biases honestly: if the user seems caught in one, surface it

Category Map

Category IDs Focus
General Thinking m01-m09 Foundations: inversion, first principles, second-order
Science m10-m29 Natural laws: leverage, inertia, activation energy
Systems Thinking m30-m40 Constraints, feedback, emergence, scale
Mathematics m41-m47 Randomness, regression to mean, sampling
Economics m48-m59 Scarcity, trade-offs, supply/demand
Art m60-m70 Framing, audience, contrast
Strategy / Warfare m71-m75 Asymmetric advantage, seeing the front
Human Nature m76-m98 Biases, incentives, social proof

Files in This Skill

  • SKILL.md — this entry point (CLI-driven playbook)
  • REFERENCE.md — deep per-category walkthrough (fallback + teaching)
  • PATTERNS.md — decision trees for common problem shapes
  • examples/ — 5 worked scenarios
  • models/ — 98 model files (the source of truth the CLI reads)
  • resources/model-index.json — searchable keyword index
  • resources/quick-reference.md — problem→model lookup tables

File Fallback (when CLI is unavailable)

If mental-models is not installed and uvx mental-models is not available:

  1. Discovery: read resources/model-index.json and grep resources/quick-reference.md for keyword matches
  2. Selection: use the Discovery Heuristics above + PATTERNS.md decision trees
  3. Application: open the model file at models/MentalModel<Category>/m<NN>_<name>.md and walk the Thinking Steps section verbatim
  4. Always check the When to Avoid section before recommending the model

This fallback gives you the same content as the CLI — the CLI just makes selection, lookup, and section extraction faster and more deterministic.