Imported from kcchien/model-thinking (SKILL.md). Install upstream with npx skills add kcchien/model-thinking. Copyright stays with the author.

Model Thinking

Name: model-thinking
Author: kcchien

Response Modes

Mode	Trigger	Output
Guided	Ambiguous problem	Diagnostic questions → model recommendations
Direct	Clear problem or specific model requested	Structured multi-model analysis
Teaching	Wants to learn models	Model explanation + example + practice

Workflow

Classify: Decision? System? Strategy? Data? Learning?
Select mode: Ambiguous → Guided | Clear → Direct | Learning → Teaching
Apply 2-3 models: Primary insight + complementary views + blind spot check
Deliver: Key insights → Recommendations → Caveats

Reference File Selection

Problem Pattern	Primary	Also Consider
Choosing between options	decisions.md	economics.md, psychology.md
Understanding complex behavior	systems.md	networks.md
Interpreting data, prediction	statistics.md	algorithms.md, risk.md
Competition, negotiation	strategy.md	psychology.md, economics.md
Human behavior, bias	psychology.md	economics.md
Connections, influence, platforms	networks.md	economics.md, systems.md
Computational problem-solving	algorithms.md	statistics.md
Uncertainty, tail events	risk.md	statistics.md, psychology.md
Acquiring knowledge, skills	learning.md	psychology.md
Markets, incentives	economics.md	psychology.md, strategy.md
Cross-domain synthesis, model pairing	combinations.md	All domain files as needed

Guided Mode: Diagnostic Questions

When problem is ambiguous, ask 2-3 from relevant domain:

Domain	Key Questions
Decisions	Reversibility? (能不能反悔？) Time horizon? (影響多久？) Stakes? (賭注多大？) Stakeholders? (誰會受影響？)
Systems	Linear/non-linear? (結果跟投入成正比嗎？) Feedback loops? (有沒有自我強化或抑制的循環？) Delays? (行動到看見結果要多久？) Boundary? (問題的邊界畫在哪？)
Strategy	Players? (有哪些參與者？) Game type? (零和還是共贏？) Info asymmetries? (誰知道得比較多？) Incentives? (各方動機是什麼？)
Data	Sample size? (資料量夠嗎？) Base rate? (一般情況下機率多少？) Selection bias? (取樣有偏差嗎？) Signal vs noise? (訊號還是雜訊？)
Risk	Fat tail or thin tail? (極端事件常見嗎？) Reversible? (損害能恢復嗎？) Ruin possible? (有沒有全軍覆沒的可能？)

Direct Application Template

When applying models directly:

## Analysis: [Problem Summary]

### Model Applied: [Model Name]
**Core Insight**: [One-sentence key takeaway]

**Application**:
[2-4 bullet points applying the model to the specific situation]

### Complementary View: [Second Model]
[Brief application showing different angle]

### Synthesis
- **Recommendation**: [Specific action]
- **Key Risk**: [What could go wrong]
- **Next Step**: [Immediate action to take]

Teaching Mode Template

## [Model Name]
**One-liner**: [Memorable summary]

**Core Concept**: [2-3 sentences]

**Example**: [Concrete scenario]

**When to Use**: [Situations]

**Common Mistake**: [Key pitfall to avoid]

**Practice Prompt**: [A question for the user to apply this model to their own situation]

Multi-Model Synthesis Example

Problem: Should I accept this job offer?

Model	Insight
Regret Minimization	At 80, would I regret not trying this path?
Opportunity Cost	What salary/growth/learning am I giving up?
Reversibility	One-way door or can I return to current field?
Second-Order	How does this affect family, health, skills in 5 years?

Synthesis: High regret potential + acceptable opportunity cost + reversible → Accept

Use 2-3 models from different domains to triangulate. Agreement = confidence. Disagreement = complexity worth exploring.

Critical Checks

Before finalizing any analysis:

Inversion: What would make this analysis wrong?
Base Rate: What typically happens in similar situations?
Incentives: Who benefits from each outcome?
Second-Order Effects: What happens next after the first-order effect?
Falsifiability: How would we know if we're wrong?

Quick Reference: 10 Universal Models

Detailed explanations and application examples for each model are in the reference files listed in the Reference File Selection table above.

Model	One-liner	Apply When
Inversion	Avoid stupidity rather than seek brilliance	Any decision
Second-Order Thinking	Then what?	Evaluating consequences
Opportunity Cost	What are you giving up?	Resource allocation
Base Rates	Prior probability matters	Any prediction
Feedback Loops	Effects become causes	System analysis
Margin of Safety	Build in buffers	Risk management
Incentives	Show me incentive, I show you outcome	Analyzing behavior
Map vs Territory	The model isn't reality	Any model use
Sunk Cost	Past costs are irrelevant	Decision-making
Explore/Exploit	Balance new vs known	Resource allocation

For all models organized by domain, load reference files above. For multi-model combination strategies and cross-domain examples, see combinations.md.

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