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.