mental-models

name	mental-models
description	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. Use when this capability is needed.
metadata	{"author":"cyperx84"}

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
• when_to_avoid — 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 when_to_avoid — 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/Mental_Model_<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.

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