Skill: wanikua/thinking-frameworks (version 1.0.0)

# Skill: Thinking Frameworks **Location:** `skills/thinking-frameworks/` **Description:** 20 human thinking frameworks for deep analysis and decision-making. Adapted from Claude Code commands to OpenClaw skills. ## Triggers Use these thinking frameworks when the user: - Explicitly requests a thinking mode (e.g., "use critical thinking", "first principles analysis", "red team this") - Asks for deep analysis of a problem, decision, or plan - Uses command syntax: `/thinking <framework> <topic>` or `/<framework-name> <topic>` - Needs structured reasoning for complex decisions ## Available Frameworks | Framework | Trigger Keywords | Use When | |-----------|-----------------|----------| | critical-thinking | critical, critique, analyze assumptions | Questioning assumptions, evaluating evidence, detecting biases | | first-principles | first principles, first principles thinking, elon musk approach | Strip away assumptions, rebuild from fundamental truths | | systems-thinking | systems, system thinking, holistic, causal loops | Understanding interconnected systems, feedback loops, emergence | | design-thinking | design thinking, empathize, prototype | User-centered problem solving, creative iteration | | lateral-thinking | lateral, creative, outside the box | Breaking conventional patterns, finding novel solutions | | six-thinking-hats | six hats, de bono, white/red/black/yellow/green/blue | Multi-perspective analysis, group decision framing | | socratic-method | socratic, questioning, progressive questions | Deep exploration through guided questioning | | bayesian-thinking | bayesian, update beliefs, prior, evidence | Probabilistic belief updating with new evidence | | second-order-thinking | second order, "and then what", consequences | Long-term consequence chains, unintended effects | | inversion-thinking | inversion, invert, reverse, how would this fail | Problem by flipping, finding failure modes | | dialectical-thinking | dialectical, thesis antithesis synthesis, hegel | Resolving contradictions through synthesis | | abductive-reasoning | abductive, best explanation, inference | Inferring the most likely explanation from observations | | mental-models | mental models, munger, multidisciplinary | Cross-disciplinary framework application | | red-team | red team, adversarial, attack plan | Finding weaknesses through adversarial analysis | | steelman | steelman, steel man, strongest argument | Strengthening opposing views before countering | | probabilistic-thinking | probabilistic, probability, uncertainty | Decision-making under uncertainty | | analogical-reasoning | analogical, analogy, map from known | Learning from parallel domains through mapping | | counterfactual-thinking | counterfactual, "what if", alternative history | Exploring alternative scenarios and outcomes | | opportunity-cost | opportunity cost, trade-off, alternative forgone | Evaluating true costs including foregone alternatives | | premortem | premortem, assume failure,事前验尸 | Proactive failure analysis before implementation | ## Usage ### Direct Command ``` /thinking critical-thinking Should we switch from REST to GraphQL? /red-team Our plan to launch in 3 markets simultaneously /premortem The new pricing model we're about to ship ``` ### Natural Language ``` "Use first principles to analyze this problem: [problem]" "Red team this plan for me: [plan]" "Think about this using second-order thinking: [situation]" ``` ## Implementation 1. Detect the requested framework from user input 2. Load the corresponding reference file from `references/` 3. Apply the framework's structured steps to the user's topic 4. Present analysis following the framework's methodology ## Notes - Frameworks are complementary — combine when appropriate (e.g., critical-thinking + red-team) - Default to concise output unless user requests depth - These are thinking aids, not rigid formulas — adapt to context - All frameworks are MIT licensed from the original thinking-skills repo

path: PROMOTION.md
content: # 📢 推广文案 / Promotion Templates

## 中文推广文案

### Discord / 微信群 / Telegram

```
🧠 刚发布了 OpenClaw 的「思维框架」Skill！

20 个经典人类思维框架，让你的 AI 助手真正会思考：
• 批判性思维 - 质疑假设，识别偏见
• 第一性原理 - 像 Elon Musk 一样思考
• 红队思维 - 主动攻击自己的计划
• 事前验尸 - 假设失败，反向找原因
• 二阶思维 - "然后呢？"连锁反应分析
• 六顶思考帽 - 多视角分析
• 贝叶斯思维 - 用证据更新信念
• ... 还有 13 个！

安装：
npx clawhub@latest install thinking-frameworks

用法：
/thinking critical-thinking 这个技术决策对吗？
/red-team 我们的发布计划有什么漏洞？
/premortem 假设这个项目失败了，原因是什么？

GitHub: https://github.com/YOUR_USERNAME/thinking-frameworks
ClawHub: https://clawhub.ai/skills/thinking-frameworks

开源免费，欢迎 Star & 转发！🙏
```

### Twitter / X

```
🧠 发布了 OpenClaw「思维框架」Skill！

20 个经典思维框架让你的 AI 真正会深度思考：
✓ 第一性原理 ✓ 红队思维 ✓ 事前验尸
✓ 二阶思维 ✓ 批判性思维 ✓ 贝叶斯更新

安装：npx clawhub@latest install thinking-frameworks

让 AI 不只是回答，而是思考。🤔

#OpenClaw #AI #ThinkingFrameworks #Productivity
```

### 小红书 / 知乎

```
标题：我让 AI 学会了 20 种人类思维框架，结果惊呆了

正文：
作为一个 AI 爱好者，我一直在想：
为什么大多数 AI 只是"回答"，而不是"思考"？

花了几天时间，把 20 个经典人类思维框架
适配到了 OpenClaw 平台：

🔍 批判性思维 - 识别逻辑漏洞和偏见
🎯 第一性原理 - 打破常规，从本质重建
🛡️ 红队思维 - 主动攻击自己的计划找漏洞
💀 事前验尸 - 假设失败，反向预防
🔮 二阶思维 - 分析"然后呢？"的连锁反应
...

现在我的 AI 助手会：
• 质疑假设而不是盲目接受
• 考虑二阶、三阶后果
• 主动找自己计划的漏洞
• 用贝叶斯方法更新信念

安装超简单：
npx clawhub@latest install thinking-frameworks

然后用：
/thinking critical-thinking [你的问题]
/red-team [你的计划]
/premortem [你的项目]

开源在 GitHub，欢迎 Star！
让 AI 真正思考，而不只是回答。

#AI #思维框架 #生产力工具 #OpenClaw #深度学习
```

---

## English Promotion Copy

### Discord / Twitter / Reddit

```
🧠 Just released "Thinking Frameworks" for OpenClaw!

20 classic human thinking frameworks to make your AI truly think:
• Critical Thinking - Question assumptions, detect biases
• First Principles - Reason from fundamental truths
• Red Team - Adversarially attack your own plans
• Premortem - Assume failure, work backwards
• Second-Order - "And then what?" consequence chains
• Six Thinking Hats - Multi-perspective analysis
• Bayesian Thinking - Update beliefs with evidence
• ... 13 more!

Install:
npx clawhub@latest install thinking-frameworks

Usage:
/thinking critical-thinking Should we switch to GraphQL?
/red-team Our Q2 launch plan
/premortem This pricing decision

GitHub: https://github.com/YOUR_USERNAME/thinking-frameworks
ClawHub: https://clawhub.ai/skills/thinking-frameworks

Open source (MIT). Stars & feedback welcome! 🙏

#OpenClaw #AI #ThinkingFrameworks #ProductivityTools
```

### Hacker News / Lobsters

```
Title: Thinking Frameworks for AI Agents – 20 Human Reasoning Patterns

I adapted 20 classic human thinking frameworks (Critical Thinking, 
First Principles, Red Team, Premortem, Bayesian, etc.) for OpenClaw 
agents.

The goal: make AI assistants actually *think* through problems using 
proven human reasoning patterns, not just generate responses.

Each framework is a structured prompt that guides the AI through 
specific analytical steps. For example, Premortem makes the AI assume 
a project has failed catastrophically, then work backwards to identify 
potential failure modes.

Install: npx clawhub@latest install thinking-frameworks
GitHub: https://github.com/YOUR_USERNAME/thinking-frameworks

Built on top of the excellent thinking-skills project by @wanikua.
Curious to hear feedback from the community!
```

---

## 📅 发布计划 / Launch Timeline

### Day 1 - Launch
- [ ] 发布到 GitHub
- [ ] 发布到 ClawHub
- [ ] Discord #skills 频道
- [ ] Twitter/X 发布
- [ ] 朋友圈/微信群分享

### Day 2-3 - 扩散
- [ ] 知乎回答相关问题（"有哪些好用的 AI 工具？"）
- [ ] 小红书笔记
- [ ] V2EX 分享

### Day 4-7 - 维护
- [ ] 回复 issue 和反馈
- [ ] 根据反馈改进
- [ ] 收集使用案例

---

## 🎯 关键信息 / Key Messages

1. **不是另一个 AI 工具** - 是让现有 AI 更会思考的"思维操作系统"
2. **20 个经典框架** - 经过时间检验的人类智慧
3. **开箱即用** - 一行命令安装
4. **开源免费** - MIT License，欢迎贡献
5. **源自社区** - 基于 wanikua/thinking-skills，回馈社区


path: README.md
content: # 🧠 Thinking Frameworks for OpenClaw

20 human thinking frameworks adapted for OpenClaw agents — deep analysis tools for complex decisions, critical thinking, and strategic planning.

[![ClawHub](https://img.shields.io/badge/ClawHub-thinking--frameworks-blue)](https://clawhub.ai/skills/thinking-frameworks)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 🚀 Quick Install

### Via ClawHub (Recommended)
```bash
npx clawhub@latest install thinking-frameworks
```

### Manual Install
```bash
git clone https://github.com/YOUR_USERNAME/thinking-frameworks.git
cp -r thinking-frameworks ~/.openclaw/workspace-libu/skills/
```

Then restart your OpenClaw session.

## 📖 Available Frameworks

| Framework | Use When |
|-----------|----------|
| **🔍 critical-thinking** | Question assumptions, evaluate evidence, detect biases |
| **🎯 first-principles** | Strip assumptions, rebuild from fundamental truths |
| **🌐 systems-thinking** | Understand feedback loops, emergence, holistic views |
| **💡 design-thinking** | User-centered creative problem solving |
| **🌀 lateral-thinking** | Break patterns, find novel solutions |
| **🎩 six-thinking-hats** | Multi-perspective analysis (White/Red/Black/Yellow/Green/Blue) |
| **🤔 socratic-method** | Deep exploration through progressive questioning |
| **📊 bayesian-thinking** | Update beliefs with new evidence |
| **🔮 second-order-thinking** | Map consequence chains ("And then what?") |
| **🔄 inversion-thinking** | Flip problems, find failure modes |
| **⚖️ dialectical-thinking** | Resolve contradictions (Thesis → Antithesis → Synthesis) |
| **🔎 abductive-reasoning** | Infer best explanations from observations |
| **🧠 mental-models** | Multi-disciplinary cross-validation |
| **🛡️ red-team** | Adversarial attack on your own plans |
| **🏛️ steelman** | Strengthen opposing arguments before countering |
| **🎲 probabilistic-thinking** | Decision-making under uncertainty |
| **🔗 analogical-reasoning** | Learn from parallel domains |
| **⚡ counterfactual-thinking** | Explore "what if" alternative scenarios |
| **💰 opportunity-cost** | Evaluate true costs including foregone alternatives |
| **💀 premortem** | Assume failure, work backwards to prevent it |

## 💬 Usage Examples

### Command Style
```
/thinking critical-thinking Should we switch from REST to GraphQL?
/red-team Our plan to launch in 3 markets simultaneously
/premortem The new pricing model we're about to ship
/first-principles How can we reduce customer support costs by 10x?
```

### Natural Language
```
"Use first principles to analyze this problem: [problem]"
"Red team this plan for me: [plan]"
"Think about this using second-order thinking: [situation]"
"Apply the six thinking hats to this decision: [decision]"
```

### Real-World Examples

**Product Decision:**
```
/thinking premortem Our Q2 feature launch plan
```

**Strategic Planning:**
```
/thinking second-order-thinking What happens if we cut prices by 30%?
```

**Technical Architecture:**
```
/thinking systems-thinking Our microservices are getting too coupled
```

**Debate Preparation:**
```
/thinking steelman The argument that remote work reduces productivity
```

## 📁 Structure

```
thinking-frameworks/
├── SKILL.md              # Skill definition and triggers
├── README.md             # This file
└── references/           # Framework reference files (20 total)
    ├── critical-thinking.md
    ├── first-principles.md
    ├── systems-thinking.md
    ├── red-team.md
    ├── premortem.md
    └── ...
```

## 🎯 When to Use Each Framework

### For Decision Making
- **critical-thinking** — Evaluate options objectively
- **opportunity-cost** — Understand trade-offs
- **probabilistic-thinking** — Handle uncertainty

### For Problem Solving
- **first-principles** — Break through conventional limits
- **lateral-thinking** — Find creative solutions
- **inversion-thinking** — Identify failure modes

### For Planning & Strategy
- **premortem** — Prevent failures before they happen
- **second-order-thinking** — Map long-term consequences
- **systems-thinking** — Understand interconnected effects

### For Debate & Discussion
- **steelman** — Strengthen opposing views
- **red-team** — Find weaknesses in your own plans
- **dialectical-thinking** — Synthesize conflicting ideas

### For Learning & Analysis
- **socratic-method** — Deep exploration
- **bayesian-thinking** — Update beliefs with evidence
- **abductive-reasoning** — Infer explanations

## 🔧 Customization

Edit `SKILL.md` to:
- Add new trigger patterns
- Customize framework behavior
- Add your own frameworks

## 📜 License

MIT License — adapted from [wanikua/thinking-skills](https://github.com/wanikua/thinking-skills)

## 🙏 Acknowledgments

Original thinking frameworks created by [wanikua](https://github.com/wanikua/thinking-skills). This adaptation brings them to the OpenClaw ecosystem with enhanced trigger patterns and usage examples.

## 🤝 Contributing

1. Fork the repo
2. Add new frameworks to `references/`
3. Update `SKILL.md` with trigger patterns
4. Submit a PR!

## 📢 Community

- **ClawHub:** [clawhub.ai/skills/thinking-frameworks](https://clawhub.ai/skills/thinking-frameworks)
- **Discord:** [discord.com/invite/clawd](https://discord.com/invite/clawd)
- **Docs:** [docs.openclaw.ai](https://docs.openclaw.ai)

---

**Made with 🧠 for OpenClaw agents everywhere**


path: references/abductive-reasoning.md
content: # Abductive Reasoning

**Abductive reasoning** — or "inference to the best explanation" — starts from observations and works backward to the most likely explanation. Unlike deduction (which guarantees truth) or induction (which generalizes from patterns), abduction asks: *"Given what I see, what is the best explanation?"* It's how doctors diagnose, detectives solve cases, and scientists generate hypotheses. Peirce called it the only form of reasoning that produces genuinely new ideas.

---

Analyze the following problem using **abductive reasoning**. Start from the evidence and reason backward to the best explanation.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Catalog the Observations

*What do we actually see? Be precise and comprehensive.*

- List all **relevant observations, facts, data points, and phenomena**.
- For each observation:
  - How **reliable** is it? (Directly observed? Reported? Inferred?)
  - How **precise** is it? (Exact measurement? Rough estimate? Anecdote?)
  - Is it **surprising** or **expected**? (Surprising observations are more informative.)
- What **patterns** exist in the data?
- What **anomalies** stand out — things that don't fit the expected pattern?
- What is **conspicuously absent** — things you'd expect to see but don't?

## Step 2: Generate Candidate Explanations

*What could explain these observations?*

Generate at least **5 candidate explanations** (hypotheses), ranging from mundane to creative:

1. **The obvious explanation** — the first thing that comes to mind
2. **The conventional expert explanation** — what a domain expert would say
3. **The systemic explanation** — the root cause, not the proximate cause
4. **The unconventional explanation** — something outside the normal frame
5. **The null explanation** — maybe nothing unusual is happening (coincidence, noise, base rates)

For each, briefly state the mechanism: *How would this explanation produce the observations we see?*

## Step 3: Evaluate Explanatory Power

For each candidate explanation, assess:

### Coverage
- Does it explain **all** the observations, or only some?
- Does it explain the **anomalies** and surprises?
- Does it account for what's **absent** as well as what's present?

### Precision
- Does it make **specific, testable predictions** beyond what we already know?
- Or is it vague enough to explain almost anything? (A bad sign — "just-so stories")

### Simplicity (Parsimony)
- How many **unsupported assumptions** does it require?
- Does it invoke **special mechanisms** or entities beyond what's necessary?
- Occam's Razor: all else equal, prefer the simpler explanation.

### Consistency
- Is it **consistent with known facts** and established science?
- Does it **contradict** any reliable evidence?
- Does it cohere with what we know about **how the world works**?

### Analogy
- Is there **precedent** — has this type of explanation been correct in similar situations before?

### Fertility
- Does it **open up new questions** and research directions?
- Does it **connect** to other phenomena in illuminating ways?

## Step 4: Compare and Rank

Create a comparison matrix:

| Criterion | Explanation 1 | Explanation 2 | Explanation 3 | ... |
|---|---|---|---|---|
| Coverage | | | | |
| Precision | | | | |
| Simplicity | | | | |
| Consistency | | | | |
| Analogy | | | | |
| Fertility | | | | |
| **Overall** | | | | |

- Which explanation comes out on top?
- Is it **clearly** the best, or are multiple explanations roughly tied?
- If tied, what **additional evidence** would break the tie?

## Step 5: Stress-Test the Best Explanation

- What would **falsify** this explanation? What evidence would disprove it?
- What are its **weakest points** — where is it most vulnerable?
- What are the **key predictions** it makes that haven't been tested yet?
- Play devil's advocate: make the **best case against** this explanation.
- How might this explanation be **incomplete** even if it's on the right track?

## Step 6: The Crucial Experiment

- Design the **single most informative test** to distinguish between the top 2-3 explanations.
- What observation would you make?
- What result would favor Explanation A vs. B?
- Is this test **feasible** with available resources?

## Step 7: Conclusion

- State the **best explanation** with appropriate confidence level.
- Explicitly note what **remains uncertain** and what **assumptions** the explanation rests on.
- Describe the **next steps** to further validate or refute the explanation.
- Maintain intellectual humility: the best explanation given current evidence may be wrong. What would make you revise it?

---

Abductive reasoning is the engine of discovery — but it's fallible. The best explanation today may be overturned by tomorrow's evidence. Hold conclusions firmly enough to act on, loosely enough to revise.


path: references/analogical-reasoning.md
content: # Analogical Reasoning

**Analogical reasoning** transfers knowledge from a familiar domain (the "source") to an unfamiliar one (the "target") by identifying structural similarities. It's how humans naturally make sense of the new — by connecting it to the known. Used brilliantly by scientists (Rutherford: atom is like a solar system), entrepreneurs (Uber for X), and legal scholars (case law precedent). But analogies can also mislead when surface similarities mask deep structural differences. The key is knowing when the mapping holds and when it breaks.

---

Analyze the following problem using **analogical reasoning**. Find illuminating parallels, map them carefully, and extract transferable insights — while being honest about where the analogy breaks down.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Understand the Target Domain

*First, deeply understand the problem you're trying to solve.*

- What are the **key elements** of this problem? (Actors, relationships, dynamics, constraints, goals)
- What makes this problem **hard**? Where is the core difficulty?
- What is **unknown or uncertain** about this domain?
- What **structure** underlies the problem? (Causal relationships, feedback loops, trade-offs)
- Temporarily set aside domain-specific details — focus on the **abstract structure**.

## Step 2: Generate Source Analogies

*Find domains that share structural features with your target.*

Search broadly across domains. For each, briefly state the analogy:

### Near Analogies (same general field)
- What **similar problem in a related domain** has been solved before?
- What does the nearest competitor or adjacent industry do?

### Far Analogies (completely different fields)
- **Nature/Biology**: What organism, ecosystem, or evolutionary process mirrors this?
- **History**: What historical event or era parallels this situation?
- **Engineering/Physics**: What physical system behaves similarly?
- **Games/Sports**: What game or sporting strategy has this structure?
- **Medicine**: What medical condition or treatment protocol is analogous?
- **Military**: What military strategy or campaign matches?
- **Art/Music**: What creative process or composition mirrors this?
- **Economics**: What market or economic phenomenon has the same dynamics?

Generate at least **5 source analogies**, with at least 2 from distant domains. Far analogies are often more creative and insightful than near ones.

## Step 3: Deep Mapping — Structure the Best Analogies

For the **top 3 most promising analogies**, perform a detailed structural mapping:

### Analogy: [Source Domain] → [Target Domain]

| Source Element | Target Element | Mapping Strength |
|---|---|---|
| [Actor/component in source] | [Corresponding actor in target] | Strong/Moderate/Weak |
| [Relationship in source] | [Corresponding relationship] | Strong/Moderate/Weak |
| [Dynamic/process in source] | [Corresponding dynamic] | Strong/Moderate/Weak |
| [Constraint in source] | [Corresponding constraint] | Strong/Moderate/Weak |
| [Outcome in source] | [Predicted outcome in target] | Strong/Moderate/Weak |

Key questions for each mapping:
- Is the correspondence **structural** (deep) or merely **surface** (superficial)?
- Is the **causal mechanism** the same, or just the appearance?
- Does the mapping **scale** appropriately?

## Step 4: Extract Transferable Insights

For each strong analogy:

- What **solutions or strategies** worked in the source domain?
- What **principles** underlie those solutions (not the specific details — the abstract principles)?
- How would those principles **translate** to the target domain?
- What **predictions** does the analogy make about the target? (These are testable!)
- What **pitfalls** were discovered in the source domain that the target should avoid?
- What **timeline or trajectory** did the source domain follow? Does the target follow a similar path?

## Step 5: Identify Where the Analogy Breaks Down

*This is the most important step. All analogies are wrong; some are useful.*

For each analogy:
- Where do the **structural correspondences fail**?
- What **key features** of the target domain have **no counterpart** in the source?
- What features of the **source** domain are irrelevant or misleading in the target?
- Where does the analogy **predict something false** about the target?
- What is the **disanalogy** — the most important difference?
- How might relying on this analogy **lead you astray**?

Rate the analogy's overall reliability:
- **High fidelity**: Core structure maps well, breakdowns are in peripheral details
- **Medium fidelity**: Structure partially maps, some important differences
- **Low fidelity**: Surface similarity only, deep structure differs significantly

## Step 6: Triangulate Across Analogies

- Where do **multiple analogies converge** on the same insight? (High confidence)
- Where do they **diverge**? (Indicates complexity or important nuance)
- What insight appears in the **far analogies** that's invisible in the near ones?
- What **composite analogy** (combining elements from multiple sources) best captures the target?

## Step 7: Generate Novel Solutions

Based on the analogical analysis:

- What **specific solutions or approaches** from the source domains could be adapted?
- What **novel combination** of source-domain strategies creates something new?
- What would a **practitioner from the source domain** suggest if they saw this problem?
- What **experiment or test** would validate whether the analogical transfer actually works?

## Synthesis

- State the **most illuminating analogy** and the key insight it provides.
- Acknowledge the **limits** of the analogy explicitly.
- Recommend **concrete actions** inspired by the analogy, adjusted for where it breaks down.

---

George Pólya said: "Analogy pervades all our thinking." The art is not in finding analogies — the human mind does that instinctively. The art is in **testing** them rigorously: mapping the structure, checking the correspondence, and being honest about where the parallel fails.


path: references/bayesian-thinking.md
content: # Bayesian Thinking

**Bayesian thinking** is the practice of updating beliefs systematically in light of new evidence, using the framework of Bayes' theorem. Instead of treating beliefs as binary (true/false), you assign probabilities and adjust them as evidence accumulates. It captures how rational agents *should* learn: start with a prior belief, encounter evidence, and compute a posterior belief. It's the antidote to both stubbornness (ignoring evidence) and fickleness (overreacting to every data point).

---

Analyze the following problem using **Bayesian thinking**. Be explicit about priors, evidence, and updates.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Define the Hypotheses

- What are the **competing hypotheses** or possible explanations?
  - H₁: [Primary hypothesis]
  - H₂: [Alternative hypothesis]
  - H₃: [Another alternative]
  - H_null: [Nothing special is happening / base rate explanation]
- Are these hypotheses **mutually exclusive and collectively exhaustive** (MECE)? If not, acknowledge the gap.
- Avoid the trap of only considering one hypothesis — always have at least one alternative.

## Step 2: Establish Prior Probabilities

*Before looking at the specific evidence, what should we believe?*

- **Base rate**: How common is each hypothesis in general? What does the reference class suggest?
  - Example: "Before any symptoms, the base rate of disease X in this population is 1%."
- **Prior knowledge**: What do we already know from past experience, expert opinion, or established science?
- Assign rough prior probabilities:
  - P(H₁) = ?
  - P(H₂) = ?
  - P(H₃) = ?
- Explain your reasoning for each prior. Be honest about uncertainty — a wide prior is better than a falsely precise one.
- **Watch for base rate neglect**: the most common Bayesian sin is ignoring how rare or common something is *before* considering the evidence.

## Step 3: Evaluate the Evidence

*Now look at the specific evidence available.*

For each key piece of evidence (E):

- **Likelihood ratio**: How much more (or less) likely is this evidence under each hypothesis?
  - P(E | H₁) = ? — If H₁ is true, how likely would we see this evidence?
  - P(E | H₂) = ? — If H₂ is true, how likely would we see this evidence?
  - The **likelihood ratio** = P(E|H₁) / P(E|H₂) tells you the **diagnostic value** of the evidence
- **Strong evidence**: Likelihood ratio > 10 (or < 0.1) — this evidence strongly discriminates
- **Weak evidence**: Likelihood ratio near 1 — this evidence barely helps distinguish hypotheses
- **Quality of evidence**: Is this evidence reliable? Could it be fabricated, biased, or misinterpreted?

## Step 4: Update — Compute Posterior Probabilities

*Apply Bayes' theorem (conceptually or numerically):*

**P(H|E) = P(E|H) × P(H) / P(E)**

- For each hypothesis, multiply prior × likelihood and normalize.
- If doing this informally, state the **direction and magnitude** of the update:
  - "This evidence moderately increases my confidence in H₁ (from ~30% to ~60%)"
  - "This evidence barely moves the needle on H₂"
- **Multiple pieces of evidence**: Update sequentially — each posterior becomes the next prior.
- **Show your work**: Even rough numbers make reasoning transparent and debuggable.

## Step 5: Check for Common Bayesian Errors

- **Base rate neglect**: Did you properly account for how rare/common the hypothesis is before evidence?
  - Classic example: A 99%-accurate test for a 1%-prevalence disease still yields ~50% false positives.
- **Confirmation bias**: Are you only counting evidence that supports your preferred hypothesis?
- **Anchoring**: Is your prior too strongly anchored on one piece of information?
- **Neglecting alternative hypotheses**: Does the evidence also fit other explanations you haven't considered?
- **Treating dependent evidence as independent**: Are the pieces of evidence truly independent, or do they share a common source?

## Step 6: Decision Under Uncertainty

*Given posterior probabilities, what action should we take?*

- What is the **expected value** of each possible action?
  - For each action × hypothesis combination: probability × outcome value
- Where is the **value of information** highest?
  - What additional evidence would most change the posterior? Seek that evidence next.
- Should we **decide now or gather more evidence**?
  - What is the cost of waiting vs. the cost of being wrong?
- What **probability threshold** would trigger a different decision?

## Step 7: Summarize

- State your **final posterior probabilities** for each hypothesis.
- Identify the **key evidence** that most influenced the update.
- Describe what **future evidence** would make you update significantly in either direction.
- Be explicit about your **remaining uncertainty** — a confident Bayesian knows what they don't know.

---

The essence of Bayesian thinking: **Strong priors require strong evidence to move. Weak priors move easily. And evidence that is equally consistent with multiple hypotheses is not very informative, no matter how dramatic it seems.**


path: references/counterfactual-thinking.md
content: # Counterfactual Thinking

**Counterfactual thinking** asks "What if things had been different?" It constructs alternative histories — scenarios where a key variable changed — to understand causation, learn from the past, and make better decisions going forward. Used by historians (what if D-Day had failed?), safety engineers (accident root cause analysis), strategists (what if we'd entered the market earlier?), and psychologists (regret and learning). It separates what was **necessary** from what was **contingent**, revealing where interventions would have mattered most.

---

Analyze the following using **counterfactual thinking**. Explore alternative histories to illuminate causation and extract lessons.

**Situation / Decision / Event:**
$ARGUMENTS

---

## Step 1: Establish the Factual History

Before exploring alternatives, nail down what actually happened:

- What was the **sequence of events**? Create a clear timeline.
- What were the **key decision points** — moments where things could have gone differently?
- What was the **outcome** — and is it clearly positive, negative, or mixed?
- Who were the **key actors** and what were their motivations?
- What were the **conditions and constraints** at the time?
- What information was **available vs. unavailable** to decision-makers at each point?

## Step 2: Identify Pivotal Moments

- Which moments were **genuinely pivotal** — where a different choice would have led to a significantly different outcome?
- Distinguish between:
  - **Necessary conditions**: Without this, the outcome could not have occurred.
  - **Sufficient conditions**: This alone was enough to cause the outcome.
  - **Contributing factors**: These increased the probability but weren't strictly necessary.
  - **Background conditions**: These were present but didn't actively cause the outcome (like oxygen in a fire analysis).
- Rank the pivotal moments by **impact** — which change would have altered the outcome most?

## Step 3: Construct Counterfactual Scenarios

For the **top 3-5 pivotal moments**, construct detailed alternative histories:

### Counterfactual 1: "What if [X had been different]?"

- **The change**: What specifically is different? (Keep it minimal — change one thing at a time.)
- **Plausibility check**: Could this alternative have reasonably occurred? Reject impossible counterfactuals.
- **Trace the cascade**:
  - Immediately after the change: what happens differently?
  - Short-term consequences (days/weeks)
  - Medium-term consequences (months)
  - Long-term consequences (years)
- **Second-order effects**: How do other actors respond to this different path?
- **Best estimate of alternative outcome**: Better, worse, or just different?
- **Confidence level**: How confident are you in this counterfactual trajectory? (Nearer pivots are more predictable; further ones are speculative.)

*Repeat for Counterfactuals 2-5.*

## Step 4: Upward vs. Downward Counterfactuals

### Upward Counterfactuals ("It could have been better")
- What changes would have led to a **better outcome**?
- What was the **best realistic alternative path**?
- What **opportunities were missed**?
- What should have been done differently, given what was knowable at the time? (Avoid hindsight bias — only use information that was available.)

### Downward Counterfactuals ("It could have been worse")
- What changes would have led to a **worse outcome**?
- How close did we come to **disaster**?
- What **lucky breaks** occurred that we might not appreciate?
- What **safeguards or good decisions** prevented worse outcomes?

This dual exercise prevents both complacency ("things went fine") and excessive regret ("we should have done everything differently").

## Step 5: Causation Analysis

The counterfactuals reveal what actually caused the outcome:

- **Robust causes**: Factors that appear in every counterfactual where the outcome changes. These are the true drivers.
- **Fragile causes**: Factors that only mattered because of specific other conditions.
- **Overdetermined outcome**: Was this outcome likely regardless of any single change? (Multiple sufficient causes.)
- **Underdetermined outcome**: Was this outcome highly contingent on specific, unlikely events? (Luck played a big role.)
- **Necessary vs. sufficient**: Which factors were necessary (without them, outcome changes) vs. merely contributing?

## Step 6: The Hindsight Bias Correction

A critical check — separate what we know **now** from what was knowable **then**:

- Were the decision-makers acting **reasonably** given available information?
- What information did they **lack** that makes the outcome obvious in retrospect?
- Would a **reasonable, well-informed person** at the time have made a different choice?
- Are we **judging the decision by its outcome** rather than by its quality at the time of making?
- Distinguish:
  - **Bad decision, bad outcome** → Learning opportunity (process was flawed)
  - **Good decision, bad outcome** → Bad luck (process was sound; don't overreact)
  - **Bad decision, good outcome** → Good luck (dangerous to repeat)
  - **Good decision, good outcome** → Skill (but verify it wasn't also luck)

## Step 7: Extract Actionable Lessons

- What **general principles** do the counterfactuals reveal about what drives outcomes in this type of situation?
- What **early warning signs** should have been heeded (and should be watched for in the future)?
- What **decision-making processes** should change?
- What **contingency plans** should be in place for similar situations?
- What was the **most important single thing** that could have been done differently?
- Crucially: **What should we do differently next time**, without the benefit of hindsight?

---

Counterfactual thinking is not about regret or wishful thinking — it's a rigorous tool for understanding causation. By asking "what would have happened if..." we discover what actually mattered, what was luck, and what lessons transfer to the future. History happened once; counterfactuals let us run the simulation again.


path: references/critical-thinking.md
content: # Critical Thinking

**Critical thinking** is the disciplined process of actively questioning assumptions, rigorously evaluating evidence, detecting biases (both cognitive and systemic), and forming well-reasoned judgments. It is the antidote to lazy reasoning, groupthink, and persuasion by rhetoric alone.

---

Analyze the following problem using rigorous **critical thinking**. Follow every step below — do not skip any.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Identify and Question Assumptions

- What assumptions are embedded in this problem statement or the conventional view?
- Which of these assumptions are **explicit** (stated) vs. **implicit** (unstated but taken for granted)?
- For each key assumption, ask: *What evidence supports this? Could this be wrong? Under what conditions would it fail?*

## Step 2: Evaluate the Evidence

- What evidence or data is available to support the main claims?
- Assess each piece of evidence on:
  - **Source credibility** — Who produced this? Do they have expertise? Conflicts of interest?
  - **Methodology** — How was this evidence gathered? Is the method sound? Sample size? Controls?
  - **Recency and relevance** — Is this evidence current? Does it directly apply to this context?
  - **Replicability** — Has this been independently verified?
- What evidence is **missing** that would be needed to reach a confident conclusion?

## Step 3: Detect Biases and Logical Fallacies

- Scan for common cognitive biases that might distort reasoning here:
  - **Confirmation bias** — Are we only seeing evidence that supports a preferred conclusion?
  - **Anchoring** — Is an initial number or frame unduly influencing the analysis?
  - **Survivorship bias** — Are we only looking at successes and ignoring failures?
  - **Authority bias** — Are we accepting something because of *who* said it rather than *what* was said?
  - **Sunk cost fallacy** — Are past investments distorting current decisions?
- Identify any **logical fallacies** in the arguments (ad hominem, straw man, false dichotomy, slippery slope, etc.)

## Step 4: Consider Alternative Explanations

- What are at least **3 alternative explanations** for the observations or claims?
- Which alternatives have been prematurely dismissed, and why?
- Apply the **principle of parsimony** (Occam's Razor): which explanation requires the fewest unsupported assumptions?

## Step 5: Assess the Strength of the Conclusion

- Given the above analysis, how strong is the main conclusion on a scale of:
  - **Strong** — Well-supported by multiple independent lines of quality evidence
  - **Moderate** — Supported but with notable gaps or caveats
  - **Weak** — Relies heavily on assumptions, poor evidence, or fallacious reasoning
  - **Indeterminate** — Insufficient evidence to judge
- What would **change your mind**? (If nothing could, that's a red flag.)

## Step 6: Synthesize

- Provide a clear, honest assessment: What can we confidently say? What remains uncertain?
- Recommend concrete next steps to resolve remaining uncertainties.

---

Be intellectually honest throughout. If the conventional wisdom turns out to be well-supported, say so. Critical thinking is not contrarianism — it is disciplined inquiry.


path: references/design-thinking.md
content: # Design Thinking

**Design thinking** is a human-centered problem-solving methodology developed at Stanford's d.school and popularized by IDEO. It follows five iterative phases — Empathize, Define, Ideate, Prototype, Test — to ensure solutions are desirable (people want it), feasible (technically possible), and viable (economically sustainable). It excels at problems where the user's real need is unclear or where existing solutions miss the mark.

---

Analyze the following problem using the **design thinking** framework. Go through all five phases.

**Problem / Topic:**
$ARGUMENTS

---

## Phase 1: Empathize — Understand the Human

Before solving anything, deeply understand the people affected.

- **Who** are the primary users/stakeholders? Create brief persona sketches:
  - Demographics, context, goals, frustrations
  - What does a day in their life look like relative to this problem?
- **What do they say** they want? (Explicit needs)
- **What do they actually do?** (Observed behavior — often contradicts what they say)
- **What do they think and feel?** (Underlying emotions, fears, aspirations)
- **Pain points**: What frustrates them most about the current situation?
- **Workarounds**: What hacks or improvised solutions are they already using? (These reveal unmet needs.)
- **Empathy map**: Summarize what users Say / Think / Do / Feel.

## Phase 2: Define — Frame the Right Problem

Synthesize empathy findings into a clear problem statement.

- Write a **Point of View (POV) statement**:
  > [User] needs [need] because [surprising insight].
- Reframe the problem from the user's perspective, not the organization's.
- Identify the **core tension** — what makes this problem genuinely hard?
- What **"How Might We" (HMW) questions** emerge?
  - Generate 3-5 HMW questions that are broad enough to inspire creativity but narrow enough to be actionable.
  - Example: "How might we make [frustrating process] feel effortless for [user]?"

## Phase 3: Ideate — Generate Solutions

Diverge wildly, then converge.

**Divergent phase (quantity over quality):**
- Generate at least **10 ideas** — include wild, impractical ones
- Use these prompts to break fixedness:
  - What would a child suggest?
  - What if money/time were unlimited?
  - What if we had to solve this in 24 hours?
  - What if the opposite of the current approach worked?
  - What solution exists in a completely different industry for a similar problem?
  - What if we removed the most "essential" feature?

**Convergent phase (select and refine):**
- Cluster similar ideas into themes
- Evaluate ideas on a **2×2 matrix**: Impact (high/low) × Feasibility (high/low)
- Select **top 3 ideas** to develop further, including at least one high-risk/high-reward option

## Phase 4: Prototype — Make It Tangible

Make ideas concrete enough to test.

- For each top idea, describe a **minimum viable prototype**:
  - What is the simplest version that tests the core assumption?
  - What form does it take? (Sketch, wireframe, role-play, landing page, physical model, storyboard)
  - What does it cost to build? (Time, money, effort — should be minimal)
- Identify the **riskiest assumption** each prototype needs to validate
- A prototype should be:
  - **Quick** to build (hours to days, not weeks)
  - **Disposable** (you should be ready to throw it away)
  - **Focused** (tests one hypothesis, not everything)

## Phase 5: Test — Learn and Iterate

Design the feedback loop.

- **Who** should test the prototype? (Real users, not colleagues)
- **How** will you test?
  - What specific behavior will you observe?
  - What questions will you ask? (Open-ended, not leading)
  - What metrics define success?
- **What signals are you looking for?**
  - Delight / confusion / frustration / workaround behavior
  - Does the user "get it" without explanation?
- **Iteration plan**:
  - What would make you pivot (abandon this direction)?
  - What would make you persevere (refine this direction)?
  - What's the next experiment after this one?

## Synthesis

- Summarize the recommended solution and why it addresses the real user need.
- Map the solution against: **Desirable** (do people want it?) × **Feasible** (can we build it?) × **Viable** (does it sustain itself?)
- Define the immediate next action to move forward.

---

Design thinking is iterative, not linear. If testing reveals a flawed assumption, loop back to an earlier phase. The goal is to **fail fast and learn cheap**.


path: references/dialectical-thinking.md
content: # Dialectical Thinking

**Dialectical thinking** is the ancient art of finding truth through the tension of opposites. Originating with Heraclitus and formalized by Hegel (thesis → antithesis → synthesis), it recognizes that reality is full of genuine contradictions, and that progress comes not from picking a side, but from transcending the opposition. Where binary thinking sees "either/or," dialectical thinking finds "both/and" — or something entirely new that resolves the contradiction at a higher level.

---

Analyze the following problem using **dialectical thinking**. Embrace the contradictions, don't shy from them.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Identify the Thesis

*What is the dominant position, prevailing view, or current state?*

- State the **thesis** clearly and charitably.
- What is its **internal logic**? Why does it make sense?
- What **evidence and arguments** support it?
- What **values, goals, or assumptions** underpin it?
- Who holds this position, and what is their context?
- What is the **strongest version** of this thesis?

## Step 2: Identify the Antithesis

*What is the opposing force, contradiction, or negation?*

- State the **antithesis** clearly and charitably.
- What is its **internal logic**? Why does it make sense?
- What **evidence and arguments** support it?
- What **values, goals, or assumptions** underpin it (and how do they differ from the thesis)?
- How does the antithesis **directly contradict or negate** the thesis?
- What is the **strongest version** of this antithesis?

Note: The antithesis is not just "the other side of the argument." It's the **inherent contradiction** within the thesis itself — the way the thesis, taken to its logical extreme, undermines itself.

## Step 3: Map the Tension

- Where exactly is the **point of contradiction**? Be specific.
- Is this a contradiction of:
  - **Logic**: Both can't be true simultaneously?
  - **Values**: They prioritize different things?
  - **Timeframe**: One is true now, the other long-term?
  - **Scale**: One is true at individual level, the other at systemic level?
  - **Perspective**: Both are true from different vantage points?
- What makes this tension **productive** rather than merely confusing?
- What would be **lost** if we simply chose one side?

## Step 4: Explore the Contradictions Within Each Side

Dialectical thinking recognizes that contradictions exist **within** positions, not just between them:

- **Internal contradictions of the thesis**:
  - Where does the thesis undermine itself?
  - What tensions exist within the thesis's own logic?
  - Where does the thesis, taken to its extreme, become its opposite?

- **Internal contradictions of the antithesis**:
  - Same questions applied to the opposing position.
  - Where does the antithesis secretly depend on what it opposes?

## Step 5: Attempt Synthesis (Aufhebung)

*The synthesis does not merely compromise or split the difference. It preserves the valid elements of both thesis and antithesis while transcending the contradiction at a higher level.*

- What **truth** does the thesis contain that must be preserved?
- What **truth** does the antithesis contain that must be preserved?
- What **higher-level framework** can contain both truths without contradiction?
- What **new concept, category, or approach** emerges that neither side alone could produce?
- How does the synthesis **dissolve** (not just resolve) the original contradiction?

The synthesis should:
- ✅ Preserve the kernel of truth from both sides
- ✅ Operate at a higher level of understanding
- ✅ Open new possibilities that neither side alone could see
- ❌ NOT be a weak compromise ("a little of both")
- ❌ NOT simply pick the side with better evidence

## Step 6: The New Contradiction

*True dialectical thinking recognizes that every synthesis becomes a new thesis, generating its own antithesis.*

- What **new contradiction** does the synthesis itself create?
- What **new tensions** emerge at this higher level?
- What might the **next synthesis** look like?
- Is there a point where the dialectical process reaches a **natural resting point**, or does it continue?

## Step 7: Practical Application

- How does the dialectical analysis change the way we approach the original problem?
- What **concrete actions** honor the synthesis rather than falling back to one side?
- What **both/and solutions** become visible that were hidden by either/or thinking?
- How can we **hold the tension** productively rather than prematurely collapsing to one pole?

---

The deepest insight of dialectical thinking: reality is not static — it is a process of becoming, driven by the creative tension of opposites. The goal is not to eliminate contradiction but to understand it as the engine of progress.


path: references/first-principles.md
content: # First Principles Thinking

**First principles thinking** is the practice of stripping away all assumptions, conventions, and analogies to reduce a problem to its most fundamental truths — then reasoning upward from those truths to construct novel solutions. Popularized by Aristotle and practiced relentlessly by Elon Musk, it replaces "because that's how it's always been done" with "what do we know to be true, and what can we build from there?"

---

Analyze the following problem using **first principles thinking**. Be rigorous and creative.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: State the Problem Clearly

- Restate the problem in the simplest possible terms.
- What exactly are we trying to achieve? What is the desired end state?
- Separate the **goal** from the **current method** of achieving it. (The goal is fundamental; the method is not.)

## Step 2: Identify Current Assumptions and Conventions

- List every assumption, convention, and "best practice" currently surrounding this problem.
- For each, label it:
  - **Physical law / hard constraint** — Cannot be violated (physics, math, legal requirements)
  - **Convention** — "How it's usually done" but not a law of nature
  - **Assumption** — Believed to be true but not rigorously verified
- Be ruthless. Most "constraints" are actually conventions in disguise.

## Step 3: Decompose to Fundamental Truths

- Strip away all conventions and assumptions. What remains?
- What are the **irreducible components** of this problem?
- What do we know to be true from:
  - Physics / chemistry / biology (natural laws)
  - Mathematics / logic (formal truths)
  - Verified empirical data (measured facts)
  - Economic fundamentals (supply, demand, incentives)
- Express these as simple, concrete statements.

## Step 4: Reconstruct from the Ground Up

- Starting ONLY from the fundamental truths identified above, reason forward:
  - What is the **theoretical best** solution unconstrained by convention?
  - What would someone with zero knowledge of the existing approach design?
  - What new combinations or architectures become possible when you ignore "how it's always been done"?
- Generate at least **3 novel approaches** built from first principles.

## Step 5: Reality Check and Feasibility

- For each novel approach:
  - What are the **real** constraints (not conventions) that limit it?
  - What would it cost (time, money, effort) to implement?
  - What is the gap between the first-principles ideal and practical reality?
  - What intermediate steps could bridge that gap?

## Step 6: Compare with Conventional Approach

- How does the first-principles solution differ from the conventional one?
- Where does convention actually align with first principles (i.e., the current way is already optimal)?
- Where are the biggest divergences — and what opportunities do they represent?

## Step 7: Actionable Output

- Recommend the most promising first-principles approach with a concrete implementation path.
- Identify the single biggest assumption that, if challenged, would unlock the most value.

---

Remember: first principles thinking is expensive and slow — that's why it's reserved for problems where convention may be leading us astray. If convention works, say so. But if there's a 10x improvement hiding behind unexamined assumptions, find it.


path: references/inversion-thinking.md
content: # Inversion Thinking

**Inversion thinking** flips the problem around. Instead of asking "How do I succeed?", it asks "How would I guarantee failure?" — then avoids those things. Charlie Munger's favorite mental model: "Invert, always invert." Carl Jacobi, the mathematician, said it best: "Man muss immer umkehren" — one must always think in reverse. Many of the hardest problems become clearer when approached backward.

---

Analyze the following problem using **inversion thinking**. Think backward to move forward.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: State the Goal

- What does **success** look like for this problem?
- What is the desired outcome, stated as clearly and concretely as possible?
- What metrics or criteria define success?

## Step 2: Invert — Engineer Failure

Now **deliberately** try to achieve the opposite. Ask:

**"If I wanted to guarantee failure / make this as bad as possible, what would I do?"**

Generate a thorough list of failure strategies:

- What **decisions** would guarantee the worst outcome?
- What **assumptions**, if wrong, would be catastrophic?
- What **behaviors** would sabotage the effort?
- What **environmental conditions** would doom this to failure?
- What **has caused similar projects/efforts to fail** in the past?
- What **common mistakes** do people make in this domain?
- What would a **perfectly incompetent** person do?
- What **shortcuts** would look tempting but lead to disaster?

Be creative and thorough — the more failure modes you identify, the more you can avoid.

## Step 3: Classify the Failure Modes

Organize the failure modes:

| Failure Mode | Probability | Impact | Reversible? | Currently Protected Against? |
|---|---|---|---|---|
| ... | High/Med/Low | High/Med/Low | Yes/No | Yes/Partially/No |

- Which failures are **most likely**?
- Which failures are **most catastrophic**?
- Which failures are **both likely AND catastrophic**? (These are priority #1)
- Which failures are **currently unprotected**?

## Step 4: Invert Back — Build the Anti-Failure Plan

For each major failure mode, define the **opposite behavior**:

- Failure mode → Prevention strategy
- Bad assumption → What to verify instead
- Toxic behavior → Healthy alternative
- Environmental risk → Contingency plan

The best strategy often isn't "do the right things" — it's **"systematically avoid doing the wrong things."**

## Step 5: Apply to Specific Sub-Problems

Take the most critical aspect of the problem and apply a focused inversion:

- **Invert the constraint**: What if the thing you think is a limitation is actually an advantage?
- **Invert the sequence**: What if you did the last step first?
- **Invert the stakeholder**: What would make users/customers *hate* this? (Then don't do that.)
- **Invert the metric**: What if you optimized for the opposite metric? What insight does that reveal?
- **Invert the question**: Instead of "Why should we do this?" ask "Why should we NOT do this?"

## Step 6: The Obituary Test

Imagine the project/decision has **already failed catastrophically**. Write its obituary:

> "[Project] was launched on [date] with high hopes. It failed because [reasons]. The warning signs were there: [signs]. In retrospect, the team should have [actions]. The cost of failure was [impact]."

- What story feels most **plausible**?
- What **warning signs** are already visible today?
- What **should have been done** that isn't being done?

## Step 7: Synthesis — Forward Plan Informed by Inversion

Now synthesize a forward-looking plan:

1. **Must-avoid list**: The top 5 things that would cause failure (from Step 2-3)
2. **Guardrails**: Specific protections, checks, or circuit breakers for each
3. **Monitoring**: Early warning signals that indicate you're drifting toward a failure mode
4. **Positive actions**: What the inversion analysis suggests you *should* do (the inverse of the failure modes)

---

Inversion doesn't replace forward thinking — it complements it. The forward approach tells you what to pursue; inversion tells you what to avoid. The wise path is informed by both. As Munger says: "All I want to know is where I'm going to die, so I'll never go there."


path: references/lateral-thinking.md
content: # Lateral Thinking

**Lateral thinking**, coined by Edward de Bono, is about solving problems through indirect, creative approaches rather than traditional step-by-step logic. Where vertical (logical) thinking digs the same hole deeper, lateral thinking digs a new hole in a different place. It deliberately disrupts established patterns of thought to discover solutions that are invisible from within the conventional frame.

---

Analyze the following problem using **lateral thinking** techniques. Break out of the obvious path.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: State the Obvious Solution

- What is the **conventional, expected** approach to this problem?
- Why does everyone default to this approach?
- What pattern of thinking does it follow?
- Explicitly acknowledge it — then set it aside. We're looking elsewhere.

## Step 2: Challenge the Problem Definition

- Is this actually the **right problem** to solve?
- What if the problem doesn't exist — what if it's a symptom of something else entirely?
- Restate the problem in at least **3 radically different ways**:
  - From a different stakeholder's perspective
  - As the opposite problem
  - At a different level of abstraction (zoom in or out)
- What problem would you **love** to have instead of this one?

## Step 3: Apply Provocation (Po)

De Bono's "Po" technique — make deliberately absurd statements to escape mental ruts:

- **Reversal**: What if we did the exact opposite? State the reversal and explore where it leads.
- **Exaggeration**: What if we took a key element to an extreme (infinitely large, infinitely small, infinitely fast)?
- **Distortion**: What if we changed the normal sequence or relationship between elements?
- **Wishful thinking**: "Wouldn't it be nice if..." — state something seemingly impossible, then find a stepping stone to reality.
- **Random entry**: Pick a random concrete noun (object, animal, place). Force a connection between it and the problem. What new ideas emerge?

For each provocation, don't judge — follow the idea for at least 3 steps to see where it leads.

## Step 4: Analogies from Distant Domains

- How is this problem solved in **nature**? (Biomimicry)
- How is this problem solved in a **completely unrelated industry**?
- How would a **child** approach this? A **comedian**? An **artist**? A **military strategist**?
- What if this were a problem in a **video game** — what cheat code or exploit would you use?
- Find at least 2 distant analogies and extract transferable principles.

## Step 5: Remove Constraints

- List every constraint you're operating under (time, money, technology, regulations, social norms).
- For each constraint, ask: **"What if this constraint didn't exist?"**
- Which constraints are **real** and which are **self-imposed**?
- What becomes possible when you remove the top 2 constraints?

## Step 6: Shift the Entry Point

- Instead of starting from the beginning, start from:
  - **The end**: What does the solved state look like? Work backward.
  - **The middle**: What's the most interesting or unusual element? Start there.
  - **The user's emotion**: What feeling do we want to create?
  - **The constraint**: What if the biggest limitation became the biggest feature?

## Step 7: Harvest Ideas

- From all the provocations and explorations above, identify:
  - **Unexpected connections** that emerged
  - **Partial ideas** that could be combined
  - **Principles** that could be applied even if the specific provocation was absurd
- Select the **top 3 most creative and promising ideas**
- For each, outline how it could be made practical:
  - What's the kernel of genius in this idea?
  - What would a realistic first version look like?
  - What makes it genuinely better than the conventional approach (not just different)?

---

Lateral thinking is not about being random or silly — it's about using deliberate techniques to escape the gravity of established patterns. The best lateral solutions often seem obvious *in retrospect*, but were invisible from within the conventional frame.


path: references/mental-models.md
content: # Mental Models (Munger-Style Latticework)

**Mental models** are simplified representations of how the world works, drawn from multiple disciplines. Charlie Munger's key insight: you need a **latticework of mental models** from many fields — not just one hammer looking for nails. By viewing a problem through multiple disciplinary lenses simultaneously, you get triangulated wisdom that no single perspective can provide. The goal is to have ~100 models and know when each applies.

---

Analyze the following problem through the **latticework of mental models**. Apply models from at least 6 different disciplines. Cross-reference and triangulate.

**Problem / Topic:**
$ARGUMENTS

---

## Discipline 1: Physics & Engineering

Apply relevant models:

- **Leverage** — Where is the point of maximum leverage? Small input, large output?
- **Friction** — What friction exists in the system? What would reducing friction enable?
- **Critical mass** — Is there a threshold that, once crossed, triggers self-sustaining change?
- **Entropy** — Is this system tending toward disorder? What energy is needed to maintain order?
- **Feedback loops** — Positive (amplifying) or negative (stabilizing) feedback?
- **Redundancy & margin of safety** — Where are the single points of failure?
- **Resonance** — Is there a frequency/timing that amplifies the effect?

Which physics/engineering model is most illuminating here, and what does it reveal?

## Discipline 2: Biology & Evolution

- **Natural selection** — What is being selected for? What traits survive in this environment?
- **Adaptation vs. extinction** — Is the subject adapting fast enough to environmental change?
- **Ecosystem dynamics** — What is the ecosystem? Who are the predators, prey, symbiotes, parasites?
- **Red Queen effect** — Do you have to keep running just to stay in place?
- **Niche construction** — Is the subject changing its environment, not just adapting to it?
- **Immune system** — What are the defense mechanisms? What gets past them?
- **Punctuated equilibrium** — Long stability interrupted by sudden change?

Which biological model fits best, and what does it predict?

## Discipline 3: Psychology & Behavioral Science

- **Incentives** — What behaviors are being rewarded? (Munger: "Show me the incentive and I'll show you the outcome.")
- **Loss aversion** — Are people weighing losses ~2x more than equivalent gains?
- **Social proof** — Is behavior driven by what others are doing rather than independent analysis?
- **Commitment & consistency bias** — Are people doubling down because they've already committed?
- **Availability heuristic** — Are recent/vivid events distorting probability estimates?
- **Narrative fallacy** — Are we constructing a story that feels right but doesn't match the data?
- **Pavlovian association** — What conditioned responses are at play?
- **Dunning-Kruger** — Who is overconfident? Who is underconfident?

Which psychological model explains the most about the human behavior in this situation?

## Discipline 4: Economics & Business

- **Supply and demand** — What shifts the curves? Where is equilibrium moving?
- **Comparative advantage** — What can be done here that can't be done better elsewhere?
- **Opportunity cost** — What is being given up? Is it worth it?
- **Marginal thinking** — What does one more unit cost/produce? (Not average — marginal.)
- **Moats** — What creates durable competitive advantage? How wide and deep is the moat?
- **Principal-agent problem** — Whose interests are misaligned? Who's playing with other people's money?
- **Tragedy of the commons** — Are shared resources being depleted because individual incentives diverge from collective interest?
- **Creative destruction** — Is something new destroying something old? Is that good or bad?

Which economic model is most relevant, and what does it prescribe?

## Discipline 5: Mathematics & Statistics

- **Power laws vs. normal distributions** — Is this a domain of averages or extremes?
- **Compounding** — What is growing exponentially that looks flat now?
- **Regression to the mean** — Is current performance sustainable, or will it revert?
- **Bayes' theorem** — What should the prior be? How much should this evidence update it?
- **Order of magnitude** — Are we arguing about 10% differences when there's a 10x factor we're ignoring?
- **Combinatorics** — How many possible configurations exist? Are we exploring enough of the space?
- **Asymmetry of outcomes** — Is the upside/downside symmetric, or heavily skewed?

Which mathematical model provides the clearest insight?

## Discipline 6: History & Philosophy

- **Chesterton's Fence** — Before removing something, understand why it was put there.
- **Lindy effect** — Things that have survived a long time are likely to survive longer.
- **Ozymandias effect** — All empires fall. What's the half-life of this?
- **Hegelian dialectic** — What is the thesis, antithesis, and potential synthesis?
- **Via negativa** — What should be *removed* rather than added?
- **Skin in the game** — Who bears the consequences of the decision? (Taleb)
- **Historical analogy** — What historical parallel illuminates this situation?

Which historical/philosophical model offers the deepest wisdom?

## Cross-Disciplinary Triangulation

Now synthesize across all six lenses:

- **Convergence**: Where do multiple models from different disciplines **agree**? (High confidence)
- **Divergence**: Where do they **disagree**? (Needs deeper investigation)
- **Blind spots**: Which models are **missing** from the analysis? What discipline hasn't been consulted?
- **Dominant model**: Which single model provides the most explanatory power for this specific problem?
- **Ensemble insight**: What understanding emerges from combining the models that **no single model alone** could produce?

## Actionable Output

- What is the **recommended course of action** based on the multi-model analysis?
- What are the **key risks** identified by the models?
- What **mental model** should the decision-maker keep at the forefront?
- What would **Charlie Munger** probably say about this situation?

---

"To the man with only a hammer, every problem looks like a nail." The cure is a toolbox full of mental models from every discipline. Use them all, trust no single one, and pay attention when they disagree.


path: references/opportunity-cost.md
content: # Opportunity Cost Thinking

**Opportunity cost** is the most fundamental concept in economics and one of the most neglected in everyday decision-making. The true cost of any choice is not what you pay — it's **what you give up**. Every dollar, hour, and unit of attention spent on X is a dollar, hour, and unit of attention *not* spent on its best alternative. People instinctively focus on direct costs and benefits while ignoring the invisible cost of the road not taken. This blindness leads to systematically poor resource allocation.

---

Analyze the following decision using **opportunity cost thinking**. Make the invisible costs visible.

**Decision / Topic:**
$ARGUMENTS

---

## Step 1: Define the Decision and Resources at Stake

- What **decision** is being made?
- What **scarce resources** are being allocated? Be exhaustive:
  - 💰 **Money** — How much capital is being committed?
  - ⏰ **Time** — Whose time, how much, and for how long?
  - 🧠 **Attention** — What cognitive bandwidth and management focus does this consume?
  - 👥 **People** — What human talent is being dedicated?
  - 📊 **Data / Information** — What information-gathering capacity is being used?
  - 🔗 **Relationships** — What social/political capital is being spent?
  - 🚪 **Optionality** — What future options are being closed off?
  - 📈 **Reputation** — What reputational risk is being taken?

## Step 2: Identify the Alternatives

*The opportunity cost is defined by the **best** alternative use of the same resources.*

- List **all realistic alternatives** for these resources (including "do nothing / save / wait"):
  1. [Alternative A]: Description
  2. [Alternative B]: Description
  3. [Alternative C]: Description
  4. [Status quo / Do nothing]: Description
  5. [Wait and decide later]: Description
- Don't just list the obvious alternatives — think about **radically different** uses of the same resources.
- Which alternatives are **mutually exclusive** with the proposed decision?

## Step 3: Evaluate Each Alternative

For the proposed decision AND each major alternative, estimate:

| Option | Expected Return | Risk Level | Time to Payoff | Reversibility | Strategic Value |
|---|---|---|---|---|---|
| Proposed decision | | | | | |
| Alternative A | | | | | |
| Alternative B | | | | | |
| Do nothing | | | | | |

- Be honest: Is the proposed decision being compared against **strong alternatives**, or only against obviously bad ones?
- What is the **expected value** of each option? (Probability × outcome)
- What is the **risk-adjusted return**?

## Step 4: Calculate the Opportunity Cost

**Opportunity Cost = Value of the Best Forgone Alternative**

- What is the **single best alternative** to the proposed decision?
- What is its expected value?
- The **true cost** of the proposed decision = direct cost + opportunity cost.
- Does the proposed decision still look good when the opportunity cost is included?

### Concrete Examples:
- "Spending $100K on Project X means not spending $100K on Project Y, which has a projected ROI of 40%. So the true cost of Project X is $100K + $40K = $140K in value."
- "Spending 6 months building Feature A means not building Feature B, which would serve 3x more users."
- "Attending this 2-hour meeting means not spending 2 hours on deep work that could produce [specific deliverable]."

## Step 5: Hidden and Non-Obvious Opportunity Costs

Look for costs people typically miss:

### Time Opportunity Costs
- What could you **learn** in the time this takes?
- What **relationships** could you build?
- What **health or personal** investments are being sacrificed?
- Is this the **right time** for this — or would the same resources be more valuable later?

### Attention Opportunity Costs
- What are you **not thinking about** because this consumes your attention?
- Decision fatigue: does this decision crowd out other important decisions?
- Context-switching cost: does this fragment your focus?

### Optionality Costs
- What **future options** does this decision eliminate?
- Does this create **lock-in** (technical, contractual, strategic)?
- What is the **value of waiting** for more information before committing?

### Compounding Costs
- If the alternative would **compound** over time (investment, skill-building, relationship), the opportunity cost grows exponentially with the time horizon.
- A small difference in return, compounded over years, becomes enormous.

## Step 6: The "Compared to What?" Test

For every claimed benefit of the proposed decision, ask **"Compared to what?"**

- "This will generate $1M in revenue." → Compared to what? What would the same resources generate elsewhere?
- "This will save 20 hours per week." → Compared to what? What would those 20 hours produce in the best alternative use?
- "This will make users happier." → Compared to what? Would a different investment make them even happier?

Nothing has absolute value. Everything has only **relative** value compared to alternatives.

## Step 7: Sunk Cost Check

Before proceeding, verify this isn't a sunk cost trap:

- Are past investments (time, money, emotion) **influencing** this decision?
- **Sunk costs are irrelevant**. Only future costs and benefits matter.
- Reframe: "If I were starting fresh today, with no prior investment, would I make this choice?"
- Would you **restart** this project/investment from scratch if you lost all progress? If no, the sunk cost is influencing you.

## Step 8: Decision

- Given the full opportunity cost analysis, is the proposed decision still the best use of resources?
- If yes, what makes it clearly superior to the best alternative?
- If no, what alternative should be pursued instead?
- What **partial reallocation** might capture value from both the proposal and the best alternative?
- What information would **change** the opportunity cost calculation?

---

The most common decision-making error is not choosing badly among known options — it's **failing to consider the alternatives at all**. Opportunity cost thinking forces you to ask the question that matters most: "Is this the *best* use of these resources, or just *a* use?"


path: references/premortem.md
content: # Premortem

**Premortem** is a proactive risk analysis technique where you assume a project or decision has already failed catastrophically, then work backwards to identify what could have caused that failure. Unlike a postmortem (which analyzes failures after they happen), a premortem helps you prevent failures before they occur. Research shows this technique can reduce project failure rates by surfacing risks that teams are reluctant to voice during normal planning.

---

Conduct a **premortem** on the following plan or decision. Follow every step below — be brutally honest and imaginative.

**Plan / Decision:**
$ARGUMENTS

---

## Step 1: Assume Catastrophic Failure

- Imagine it is **6-12 months in the future**.
- The project has failed **spectacularly**. It's not just a minor setback — it's a complete disaster.
- Stakeholders are furious. Resources were wasted. Reputation is damaged.
- **Write the headline** of the failure: *"Project X Collapses: [Specific Catastrophic Outcome]"*

## Step 2: Generate Failure Scenarios

- Brainstorm **at least 10 specific reasons** why this failure occurred.
- For each reason, ask:
  - What specific event or condition caused this?
  - Was this a known risk that was ignored?
  - Was this a surprise (unknown unknown) or a blind spot (known but unacknowledged)?
- Think across categories:
  - **Technical** — Architecture flaws, integration failures, scalability issues
  - **Human** — Team dynamics, skill gaps, burnout, turnover
  - **Market** — Customer rejection, competitive response, timing issues
  - **Operational** — Process breakdowns, communication failures, resource constraints
  - **External** — Regulatory changes, economic shifts, supply chain disruptions

## Step 3: Identify the Most Likely Failure Modes

- Review all failure scenarios and rank them by:
  - **Likelihood** — How probable is this failure mode? (High/Medium/Low)
  - **Impact** — How severe would the consequences be? (Critical/High/Medium/Low)
  - **Detectability** — How early would we notice warning signs? (Early/Mid/Late/Too Late)
- Focus on **high-likelihood + high-impact** scenarios — these are your critical risks.

## Step 4: Surface the Uncomfortable Truths

- Which failure scenarios are people **reluctant to discuss**?
- What concerns have been **dismissed or minimized** during planning?
- Who on the team might have **private doubts** they haven't voiced?
- What assumptions are we making that, if wrong, would be catastrophic?

## Step 5: Design Preventive Measures

For each critical risk (high likelihood + high impact):
- **Prevention:** What can we do NOW to prevent this failure mode entirely?
- **Mitigation:** If prevention fails, how can we reduce the impact?
- **Early Warning:** What leading indicators would alert us that this risk is materializing?
- **Contingency:** What is our backup plan if this risk occurs despite our efforts?

## Step 6: Assign Ownership and Triggers

- For each preventive measure and contingency:
  - **Who owns** monitoring and execution?
  - **What trigger** activates the contingency plan?
  - **When** will we review and update these safeguards?

## Step 7: Decision Point

- Given this premortem analysis:
  - Should we **proceed** with the current plan (with safeguards in place)?
  - Should we **modify** the plan to address critical risks?
  - Should we **delay** until key uncertainties are resolved?
  - Should we **abandon** the plan entirely (failure risk too high)?

---

**Remember:** The goal of a premortem is not to kill good ideas through excessive caution. It's to make good ideas *actually succeed* by surfacing and addressing risks before they become failures. If the premortem reveals fatal flaws, that's a feature, not a bug — it saves you from wasting resources on a doomed effort.


path: references/probabilistic-thinking.md
content: # Probabilistic Thinking

**Probabilistic thinking** replaces the false certainty of "will this work?" with the honest question "what are the odds, and what's the expected value?" It embraces uncertainty as fundamental, not as a failure of analysis. Used by the best poker players, investors, military strategists, and forecasters (like Superforecasters in Philip Tetlock's research), it separates the quality of a decision from the quality of its outcome — because even good decisions can have bad outcomes, and vice versa.

---

Analyze the following problem using **probabilistic thinking**. Quantify uncertainty, compute expected values, and make decisions that are robust under uncertainty.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Define the Decision and Possible Outcomes

- What is the **decision** to be made?
- What are the **possible outcomes**? List them exhaustively:
  - Best case
  - Likely case(s)
  - Worst case
  - Black swan / tail risk scenarios
- Are there **binary outcomes** (yes/no) or a **distribution** of possibilities?
- What is the **time horizon** for these outcomes?

## Step 2: Estimate Probabilities

For each outcome, estimate the probability:

- Use **reference classes**: How often does this type of thing happen historically?
- Use **base rates**: What is the general frequency before considering specifics?
- Use **decomposition**: Break the event into sub-events and estimate each.
  - P(A and B) = P(A) × P(B|A) — for dependent events
  - P(A or B) = P(A) + P(B) - P(A and B) — for overlapping events
- **Calibrate** your estimates:
  - If you say 90% confident, are you right 9 out of 10 times?
  - Most people are overconfident. Widen your ranges.
  - Use the "surprise index": if the opposite happened, how surprised would you be?
- Present probabilities explicitly:
  - Outcome A: __% probability
  - Outcome B: __% probability
  - Outcome C: __% probability
  - (Must sum to ~100%)

## Step 3: Assess Payoffs and Costs

For each outcome:

| Outcome | Probability | Payoff (if good) | Cost (if bad) | Notes |
|---|---|---|---|---|
| A | | | | |
| B | | | | |
| C | | | | |

- Quantify payoffs and costs in concrete terms (dollars, time, reputation, opportunity).
- Consider **asymmetry**: Is the upside/downside symmetric?
  - **Positive asymmetry** (limited downside, unlimited upside) → favor action
  - **Negative asymmetry** (limited upside, unlimited downside) → favor caution
- Are there any outcomes that are **existential/irreversible** (ruin risk)?

## Step 4: Compute Expected Value

**EV = Σ (Probability × Value) for all outcomes**

- Calculate the expected value of each option.
- Compare against the **expected value of inaction** (the default path also has an EV).
- Consider **risk-adjusted expected value**:
  - For decisions where you only get one shot (no retry), variance matters as much as EV.
  - A high-EV gamble with high variance may be worse than a lower-EV option with lower variance.
  - Kelly Criterion: never bet more than the edge/odds ratio.

## Step 5: Consider the Distribution, Not Just the Average

- What does the **full distribution** of outcomes look like?
  - Thin-tailed (normal distribution) → Average is informative
  - Fat-tailed (power law, Pareto) → Extremes dominate, average is misleading
- What is the **median** outcome? (Often more informative than the mean)
- What are the **tail risks** — low-probability, high-impact scenarios?
- Apply the **barbell strategy** where appropriate:
  - Protect against catastrophic downside (insurance, hedging)
  - Expose to asymmetric upside (optionality, experiments)

## Step 6: Decision-Making Under Uncertainty

Apply the appropriate framework:

- **Maximize expected value** — When you have many tries and outcomes are not existential.
- **Minimax regret** — When you want to minimize the worst-case regret ("Which decision would I regret least if it went wrong?")
- **Maximin** — When protecting against the worst case is paramount (survival situations).
- **Value of information** — Would gathering more data significantly change the decision? Is the cost of gathering info less than the expected improvement in decision quality?
- **Reversibility test** — Is this decision easily reversible? If yes, lower the bar for action. If no, require higher confidence.
- **Optionality** — Does this decision preserve or eliminate future options? Prefer actions that keep options open.

## Step 7: Pre-Register Your Predictions

Before the outcome is known:

- State your **probability estimate** clearly.
- Define what **evidence would update** your estimate (and in which direction).
- Set **tripwires**: specific future observations that would trigger a change in strategy.
- Distinguish between **being wrong** (probability was poorly calibrated) and **bad luck** (probability was right, just got an unlikely outcome).

## Step 8: Recommendation

- What is the **probabilistically optimal decision**?
- What is the **confidence interval** around your recommendation?
- What are the **key uncertainties** that most affect the outcome?
- What **cheap experiments** could reduce uncertainty before committing?
- State explicitly: "I recommend X with approximately Y% confidence, primarily because Z. The biggest risk is W."

---

The core discipline: separate the **quality of the decision** from the **quality of the outcome**. A good decision can have a bad outcome (bad luck). A bad decision can have a good outcome (good luck). Over time, good decision-making dominates. Think in probabilities, decide with expected values, and review with calibration.


path: references/red-team.md
content: # Red Team Thinking

**Red teaming** is the practice of deliberately attacking your own plans, ideas, products, or arguments to find vulnerabilities before reality does. Originating in military war games (where a "red team" plays the enemy), it's now used in cybersecurity, business strategy, policy analysis, and any domain where failure is costly. The red team's job is to be adversarial — not to be destructive, but to make the blue team's plan stronger by exposing weaknesses that insiders can't see due to blind spots, groupthink, and optimism bias.

---

You are now the **Red Team**. Your mission: relentlessly attack the following plan, idea, argument, or system. Find every crack, exploit every weakness, expose every blind spot.

**Target to Attack:**
$ARGUMENTS

---

## Phase 1: Reconnaissance — Understand the Target

Before attacking, fully understand what you're attacking:

- What is the **core claim, plan, or system** being proposed?
- What are its **stated goals** and success criteria?
- What **assumptions** does it rely on (explicitly and implicitly)?
- Who are the **stakeholders** and what are their roles?
- What is the **threat model** — what risks has the team already considered?
- Where has the team expressed the **most confidence**? (That's often where blind spots hide.)

## Phase 2: Attack Surface Analysis

Identify all possible vectors of failure:

### Strategic Attacks
- Is the **fundamental strategy** sound, or is it based on a flawed premise?
- What if the **market, environment, or context** changes in ways not anticipated?
- What **competitor response** would neutralize this plan?
- Is there a **faster, cheaper, or simpler** alternative that makes this plan obsolete?
- What **black swan events** could destroy this plan overnight?

### Execution Attacks
- Where are the **single points of failure** — components where one failure cascades?
- What are the **dependencies**? Which are fragile?
- Where is the plan **most complex**? (Complexity is the enemy of reliability.)
- What **resources** (people, money, time, attention) are underestimated?
- What happens when the **timeline slips** by 50%? By 100%?

### Human Attacks
- Where might **key people fail** — through incompetence, burnout, turnover, or misaligned incentives?
- How might **politics, ego, or groupthink** derail execution?
- What would a **disgruntled insider** do to sabotage this?
- Where might **communication failures** between teams/departments cause problems?

### Technical Attacks
- What **technical assumptions** could be wrong?
- What **edge cases** haven't been considered?
- How does the system behave under **extreme load, adversarial input, or unexpected conditions**?
- What **security vulnerabilities** exist?

### Market/User Attacks
- What if **users don't behave as expected**?
- What if the **value proposition** is weaker than believed?
- How would a user **misuse, abuse, or game** the system?
- What would make users **leave** or choose an alternative?

## Phase 3: Exploit Development

For the **top 5 most critical vulnerabilities** found above:

For each vulnerability:
1. **Description**: What exactly is the weakness?
2. **Attack scenario**: Describe specifically how it could be exploited or how failure would occur.
3. **Probability**: How likely is this to happen? (High/Medium/Low)
4. **Impact**: If it happens, how bad is it? (Critical/Severe/Moderate/Minor)
5. **Detectability**: Would the blue team notice this going wrong in time to react?
6. **Current mitigation**: Is there any existing protection? Is it adequate?

## Phase 4: Kill Chain Analysis

Construct the **most devastating realistic attack scenario**:

- Step 1: [Initial failure point]
- Step 2: [How it cascades]
- Step 3: [How it compounds]
- Step 4: [How it reaches critical failure]
- End state: [Worst realistic outcome]

What is the **fastest path to total failure**? What is the **most insidious** path (slow, hidden, irreversible)?

## Phase 5: Cognitive Bias Audit

Check for these biases in the blue team's thinking:

- **Optimism bias** — Are projections systematically too optimistic?
- **Planning fallacy** — Are timelines and budgets realistic based on reference class?
- **Groupthink** — Has the team converged on a plan without genuine dissent?
- **Sunk cost fallacy** — Is momentum driving decisions rather than current merit?
- **Survivorship bias** — Is the team studying only successes and ignoring failures?
- **IKEA effect** — Is the team overvaluing this plan because they built it?
- **Anchoring** — Is a specific number or narrative unduly influencing the analysis?
- **Confirmation bias** — Is the team seeking evidence that confirms the plan while ignoring contradictions?

## Phase 6: Red Team Report

### Critical Findings (must address immediately)
1. [Finding + recommendation]
2. [Finding + recommendation]
3. [Finding + recommendation]

### Important Findings (should address before launch)
1. [Finding + recommendation]
2. [Finding + recommendation]

### Minor Findings (address when possible)
1. [Finding + recommendation]

### Overall Assessment
- **Confidence level** in the plan's success given current design: Low / Medium / High
- **Top 3 changes** that would most improve the plan's robustness
- **Go / No-Go / Conditional Go** recommendation with conditions

---

The red team's job is to be the loyal opposition — tough, honest, and constructive. Breaking the plan in a war game is infinitely cheaper than reality breaking it in production. A plan that survives a competent red team is worth trusting.


path: references/second-order-thinking.md
content: # Second-Order Thinking

**Second-order thinking** asks "And then what?" It pushes beyond the immediate, obvious consequences of an action to consider the cascading effects — the consequences of consequences. Popularized by Howard Marks (Oaktree Capital), it's the difference between a novice chess player (who sees one move ahead) and a grandmaster (who sees five). Most bad decisions come from optimizing for first-order effects while ignoring the second- and third-order effects that ultimately dominate.

---

Analyze the following problem using **second-order thinking**. Trace every chain reaction to its logical conclusion.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Identify the Action or Decision

- What is the **proposed action, decision, or change** being considered?
- What is the **intended first-order effect** (the obvious, immediate result)?
- Who is the decision-maker, and what is their time horizon?

## Step 2: First-Order Effects (Immediate)

*What happens right away?*

- List all **direct, immediate consequences** of this action.
- For each: Who is affected? How? Positively or negatively?
- These are what most people consider. They are usually obvious and often positive (that's why the action is being considered).
- Time horizon: days to weeks.

## Step 3: Second-Order Effects (Then What?)

*What do the first-order effects cause?*

For each first-order effect, ask **"And then what happens?"**

- How will **other actors respond** to the first-order effects?
  - Competitors, customers, regulators, markets, employees, the public
  - People are not passive — they adapt, game the system, and find workarounds
- What **behavioral changes** will the first-order effects incentivize?
  - Follow the incentives: if you change the reward structure, behavior will shift
- What **equilibrium shifts** occur?
  - Markets, supply chains, social norms, power dynamics
- Time horizon: weeks to months.

## Step 4: Third-Order Effects (And Then?)

*What do the second-order effects cause?*

Push one more level deeper:

- What **systemic changes** emerge from the accumulated second-order effects?
- Are there any **feedback loops** that amplify or dampen the effects?
- Do any **tipping points** get crossed — where gradual change becomes sudden transformation?
- What **new problems** are created by solving the original problem?
- What **norms, cultures, or institutions** shift over the long term?
- Time horizon: months to years.

## Step 5: Map the Chains

Organize the cascade visually:

```
Action → 1st Order Effect A → 2nd Order Effect A1 → 3rd Order Effect A1a
                              → 2nd Order Effect A2 → 3rd Order Effect A2a
       → 1st Order Effect B → 2nd Order Effect B1 → ...
```

- Identify which chains are **most consequential** (highest impact × highest probability).
- Identify which chains involve **irreversible** changes (these deserve extra attention).

## Step 6: Historical Parallels

- Has a similar action been taken before? What happened?
- Examples of famous second-order failures:
  - Cobra bounty in colonial India (paying for dead cobras → cobra farming)
  - Rent control → housing shortage → higher prices for non-controlled units
  - Antibiotics overuse → antibiotic resistance
  - Social media engagement optimization → polarization → erosion of trust
- What **analogous situation** provides a warning or a model?

## Step 7: The "Who Adapts?" Test

For every proposed action, identify:
- Who will **benefit** from the first-order effect and **lobby to preserve** it?
- Who will be **harmed** and seek **workarounds or countermeasures**?
- What **perverse incentives** does this action create?
- How will **smart, self-interested actors** game this system?

## Step 8: Net Assessment

- **First-order**: Does the action achieve its intended goal? (Usually yes — that's why it's proposed.)
- **Second-order**: Do the downstream effects support or undermine the goal?
- **Third-order**: Is the world better or worse off after all cascading effects?
- **Time-weighted**: When do positive effects peak? When do negative effects peak? Is there a temporal mismatch (short-term gain, long-term pain)?

## Step 9: Recommendations

- Should the action be taken given the full cascade of effects?
- If yes, what **mitigations** can address the negative second/third-order effects?
- If no, what **alternative action** has better second-order properties?
- What **early warning signals** should be monitored to detect whether negative cascades are materializing?

---

First-order thinkers look for simple, easy answers. Second-order thinkers ask "And then what?" The best decisions often look *wrong* in the short term because they sacrifice first-order benefits to avoid second-order disasters.


path: references/six-thinking-hats.md
content: # Six Thinking Hats

**Six Thinking Hats** is Edward de Bono's parallel thinking framework that separates thinking into six distinct modes, each represented by a colored hat. Instead of everyone arguing from entrenched positions, the group (or individual) systematically wears each hat in turn, ensuring all dimensions of a problem are explored without the confusion of mixing them. It prevents the loudest voice or dominant emotion from hijacking the analysis.

---

Analyze the following problem by wearing each of the **Six Thinking Hats** in sequence. Give each hat full, genuine effort.

**Problem / Topic:**
$ARGUMENTS

---

## 🔵 Blue Hat — Process & Meta-Thinking (Facilitator)

*The Blue Hat manages the thinking process itself.*

- What exactly is the **subject** of our thinking? Restate it clearly.
- What is the **goal** of this analysis? What do we need to decide or understand?
- What **sequence** of hats will be most productive? (Default: Blue → White → Red → Yellow → Black → Green → Blue)
- What are the **constraints** (time, resources, context)?

---

## ⚪ White Hat — Facts & Information (Data Analyst)

*The White Hat deals ONLY with objective data. No opinions, no interpretations.*

- What **facts and data** do we currently have about this topic?
- What are the **key numbers, statistics, and measurements**?
- What information is **missing** that we need?
- What information is **disputed or uncertain**? (State both sides without judging.)
- What is the **source and reliability** of our data?
- Distinguish clearly between: **verified facts** / **believed but unverified** / **gaps in knowledge**

---

## 🔴 Red Hat — Feelings & Intuition (Gut Check)

*The Red Hat captures emotions, hunches, and intuitions — WITHOUT justification. No need to explain why you feel this way.*

- What is the **gut reaction** to this problem/proposal?
- What **emotions** does it evoke? (Excitement? Anxiety? Skepticism? Enthusiasm? Dread?)
- What does **intuition** suggest — even if it can't be logically justified?
- How might **other stakeholders** feel about this? (Users, customers, team members, competitors)
- Are there any **aesthetic or visceral** responses? ("This feels elegant" / "This feels clunky")
- What are the **unspoken fears** or **hopes** surrounding this topic?

---

## 🟡 Yellow Hat — Benefits & Optimism (Advocate)

*The Yellow Hat seeks value and benefits. Be genuinely constructive — this is NOT about being naively positive, but about finding real value.*

- What are the **best-case outcomes** if this works?
- What **specific benefits** does this approach offer? (List concretely, not vaguely)
- What **opportunities** does this create that don't currently exist?
- Who benefits most, and how?
- What is the **logical basis for optimism**? (Not just "it could be great" — why specifically?)
- What **existing strengths** can we leverage?
- Even if the overall idea has problems, what **elements** are genuinely valuable and worth preserving?

---

## ⚫ Black Hat — Risks & Caution (Devil's Advocate)

*The Black Hat is the critical judge — finding risks, dangers, and weaknesses. This is the most natural hat for many people, but it must be used fairly, not as a weapon.*

- What could go **wrong**? List specific failure modes.
- What are the **risks** (probability × impact)?
- What **weaknesses** exist in this plan/idea?
- Does this contradict any **known facts** (from White Hat)?
- What **past precedents** suggest this could fail?
- What are the **worst-case scenarios**?
- What **legal, ethical, or regulatory** concerns exist?
- Is the timing right, or are there **environmental factors** working against this?
- Where are the **hidden costs** (financial, social, opportunity)?

---

## 🟢 Green Hat — Creativity & Alternatives (Innovator)

*The Green Hat generates new ideas, alternatives, and possibilities. Judgment is suspended — all ideas are welcome.*

- What are **alternative approaches** we haven't considered?
- Can we **modify, combine, or adapt** existing ideas?
- What **provocative ideas** challenge our assumptions? (Even if impractical)
- How could we overcome the risks identified by the Black Hat?
- What would a **completely different type of organization/person** do?
- What if we **changed one fundamental constraint**?
- Generate at least **5 fresh ideas or modifications**, ranging from conservative to radical.
- What **experiments** could we run to test uncertain areas?

---

## 🔵 Blue Hat — Synthesis & Next Steps (Wrap-Up)

*Return to the Blue Hat to synthesize findings.*

- **Summary**: What did each hat reveal?
  - Facts (White): _
  - Feelings (Red): _
  - Benefits (Yellow): _
  - Risks (Black): _
  - Alternatives (Green): _
- **Key tensions**: Where do the hats conflict? (e.g., Yellow sees opportunity but Black sees risk)
- **Decision or recommendation**: Based on the complete analysis, what should we do?
- **Next steps**: What are the concrete, immediate actions?
- **What needs more exploration?** Which hat's analysis needs deeper work?

---

The power of Six Hats is in the **separation** — by wearing one hat at a time, we avoid the muddle of trying to be creative, critical, and factual simultaneously. Each mode gets its full due.


path: references/socratic-method.md
content: # Socratic Method

**The Socratic Method** is the art of systematic questioning to probe the depth and validity of beliefs, expose contradictions, and guide thinking toward clearer understanding. Named after Socrates, who claimed to know nothing and instead asked questions that revealed others didn't know as much as they thought. It doesn't lecture — it illuminates through inquiry.

---

Analyze the following problem using the **Socratic Method**. Pursue each line of questioning relentlessly but fairly. The goal is deeper understanding, not gotcha moments.

**Problem / Topic:**
$ARGUMENTS

---

## Round 1: Clarifying Questions — What Do We Actually Mean?

Start by questioning the core concepts and terms.

- What exactly do we mean by [key term]? Define it precisely.
- Can you give a concrete example? And a counter-example?
- Is this concept clear enough that two people would apply it the same way?
- Are there ambiguities or multiple valid interpretations?
- How is this different from [similar concept]?
- What would someone who disagrees say this means?

## Round 2: Probing Assumptions — What Are We Taking for Granted?

Dig into the foundations of the claim or position.

- What are we **assuming** to be true here?
- Why do we believe that assumption? What's the basis?
- What if that assumption were **false** — what changes?
- Is this assumption **universal** or only true in certain contexts?
- Are we confusing **correlation with causation** anywhere?
- Is there a **circular argument** hidden here (assuming what we're trying to prove)?

## Round 3: Probing Evidence and Reasons — How Do We Know?

Challenge the evidence and reasoning.

- What **evidence** supports this position?
- How was that evidence obtained? Is the method reliable?
- Is this evidence **sufficient** to support the conclusion, or merely consistent with it?
- Could the same evidence support a **different conclusion**?
- What evidence would **disprove** this position? Does such evidence exist?
- Are we relying on **anecdote** where we need **data**, or vice versa?
- Is the reasoning **deductive** (guaranteed if premises are true) or **inductive** (probable but not certain)?

## Round 4: Considering Alternative Viewpoints — What Would Others Say?

- What would a **thoughtful critic** say in response?
- What is the **strongest version** of the opposing argument?
- How would someone from a **different background, culture, or discipline** see this?
- If we accepted the opposing view, what would follow?
- Is there a **third position** that transcends both sides?

## Round 5: Probing Implications and Consequences — What Follows?

Trace the claim to its logical conclusions.

- If this is true, what **else** must also be true?
- What are the **practical consequences** of accepting this position?
- Does this lead to any **contradictions** or **absurd conclusions** (reductio ad absurdum)?
- How does this apply to **edge cases** or **extreme situations**?
- If we apply this principle **consistently**, are we comfortable with all the results?
- What **precedent** does this set?

## Round 6: Meta-Questions — Questioning the Question

- Is this even the **right question** to ask?
- **Why** is this question important? What depends on the answer?
- What makes this question **hard**? Where does the difficulty actually lie?
- Are we capable of answering this with current knowledge, or is it fundamentally **uncertain**?
- What **better question** should we be asking instead?

## Synthesis: What Have We Learned?

After the Socratic examination:

- What beliefs **survived** the questioning and are stronger for it?
- What beliefs were **weakened** or **revealed as unjustified**?
- What **new questions** emerged that we hadn't considered before?
- What do we now know that we **don't know** (known unknowns)?
- What is the most **intellectually honest** conclusion we can draw?
- What would Socrates say: did we find **wisdom**, or did we discover **the limits of our knowledge**?

---

The Socratic method is not about winning arguments — it's about discovering truth through disciplined inquiry. The most valuable outcome is often not a better answer, but a better question.


path: references/steelman.md
content: # Steelman Argument

**Steelmanning** is the opposite of strawmanning. Instead of weakening an opponent's argument to easily defeat it, you **strengthen it to its most powerful form** — then see if it still falls to your critique. Coined in contrast to "straw man," the steelman forces intellectual honesty: if you can defeat the best version of an argument, your critique is genuinely strong. If you can't, maybe the argument has more merit than you assumed. It's the ultimate test of intellectual rigor.

---

Apply **steelman analysis** to the following. First build the argument up to its strongest form, then evaluate it honestly.

**Argument / Position / Topic:**
$ARGUMENTS

---

## Step 1: State the Original Argument

- What is the argument or position as **originally presented**?
- Who holds this position, and in what context?
- What is the **initial reaction** many people have to this argument? (Often dismissal — note that.)

## Step 2: Understand the Argument's Foundations

Before strengthening, understand deeply:

- What **values** does this position prioritize? (Fairness? Efficiency? Freedom? Safety? Tradition? Progress?)
- What **experiences or evidence** led someone to hold this view?
- What **legitimate concerns** motivate this position?
- What **worldview or framework** makes this position internally coherent?
- What would the **most thoughtful, informed, good-faith** holder of this position say?
- What do proponents of this view see that critics might be **missing**?

## Step 3: Build the Steelman

Now construct the **strongest possible version** of this argument:

### Strengthen the Evidence
- What is the **best available evidence** supporting this position?
- Are there **studies, data, historical examples, or expert opinions** that bolster it?
- What evidence do critics typically **ignore or dismiss** that actually has merit?

### Strengthen the Logic
- What is the **most logically rigorous** way to construct this argument?
- Remove any weak premises and replace with stronger ones.
- Fix any logical fallacies while preserving the core insight.
- Make the causal chain as tight as possible.

### Strengthen the Framing
- What is the **most compelling way** to frame this argument?
- What **analogies or examples** make it most persuasive?
- How would you present this to a skeptical but fair-minded audience?

### Address Known Objections
- What are the **common objections** to this position?
- How would the **steelmanned version** address each one?
- Where does the steelmanned argument **concede ground** gracefully and where does it **hold firm**?

### The Steelmanned Argument (Full Version)
> Present the steelmanned argument as a coherent, flowing case — as if you were the most articulate, well-informed, good-faith advocate for this position. Write it as they would write it at their best.

## Step 4: Evaluate the Steelman

Now, honestly evaluate the **strongest version**:

### Where It's Strong
- What parts of the steelmanned argument are **genuinely compelling**?
- What **inconvenient truths** does it raise that critics should acknowledge?
- Where might it actually be **correct**?

### Where It's Weak
- Even in its strongest form, what are the **remaining vulnerabilities**?
- What **evidence or logic** still cuts against it?
- What does it **fail to account for**?
- Are there **internal contradictions** that can't be resolved even in the best version?

### The Strongest Counterargument
- Given the steelmanned version, what is the **strongest possible response**?
- This counter-critique is only valid if it addresses the **steelmanned** version, not the weak original.
- Where does the counterargument **concede** what's true in the steelman?

## Step 5: Synthesis — What Truth Remains?

- After steelmanning and then critiquing, what **kernel of truth** survives?
- What should critics **update their views on** based on the steelman exercise?
- What should proponents **update their views on** based on the counter-critique?
- Is there a **higher-level synthesis** that incorporates the valid elements from both sides?
- What **common ground** exists that both sides might agree on?

## Step 6: Meta-Reflection

- Did the steelmann exercise change your assessment of the original argument? How?
- What **blind spots** did you have before engaging with the strongest version?
- What does this tell you about the **quality of public discourse** on this topic? (Often, people argue against straw versions and talk past each other.)

---

Steelmanning is not about being "fair and balanced" for its own sake. It's a **self-improvement tool**: by engaging with the strongest opposition, you either sharpen your own position or discover you need to change it. Both outcomes make you smarter. As John Stuart Mill wrote: "He who knows only his own side of the case knows little of that."


path: references/systems-thinking.md
content: # Systems Thinking

**Systems thinking** is the discipline of seeing wholes rather than parts, understanding how components interact to produce emergent behavior, and recognizing that most real-world problems are embedded in complex webs of feedback loops, delays, and nonlinear dynamics. Rooted in the work of Donella Meadows, Jay Forrester, and Peter Senge, it prevents the trap of solving one problem while creating three others.

---

Analyze the following problem using **systems thinking**. Map the system, find the leverage points, and avoid unintended consequences.

**Problem / Topic:**
$ARGUMENTS

---

## Step 1: Define the System Boundary

- What is the **system** we are examining? Draw the boundary.
- What is **inside** the system (components we can influence)?
- What is **outside** the system (environment, exogenous factors)?
- Who are the key **actors/stakeholders** within the system?
- What is the system's **purpose** or function (intended and actual — they often differ)?

## Step 2: Map the Key Components and Relationships

- Identify the **5-10 most important variables** in this system.
- For each pair of related variables, describe the relationship:
  - **Direction**: Does A increase B (+) or decrease B (-)?
  - **Strength**: Strong, moderate, or weak influence?
  - **Delay**: Is the effect immediate or delayed? How long?
- Identify **stocks** (things that accumulate: money, trust, inventory, knowledge) and **flows** (rates of change: spending, learning, production).

## Step 3: Identify Feedback Loops

- **Reinforcing loops (R)**: Positive feedback — growth spirals or death spirals
  - Example: More users → more content → more users (network effect)
  - These create exponential growth or collapse
- **Balancing loops (B)**: Negative feedback — stabilizing forces
  - Example: More users → more load → slower performance → fewer users
  - These create equilibrium or oscillation
- Map at least 2-3 key feedback loops. Which are currently **dominant**?

## Step 4: Find Delays and Nonlinearities

- Where are there significant **time delays** between cause and effect?
  - Delays cause oscillation, overshoot, and policy resistance
- Where are there **thresholds or tipping points** — small changes that trigger large shifts?
- Where are there **diminishing returns** or **increasing returns**?

## Step 5: Identify Emergent Behavior and Archetypes

- What **system archetypes** are present?
  - **Fixes that fail** — short-term solution worsens long-term problem
  - **Shifting the burden** — treating symptoms instead of root cause
  - **Tragedy of the commons** — individual rationality leads to collective ruin
  - **Success to the successful** — winners accumulate advantages
  - **Limits to growth** — reinforcing loop hits a balancing constraint
  - **Escalation** — competing parties drive each other to extremes
  - **Growth and underinvestment** — growth stalls because capacity isn't built ahead of demand
- What **emergent properties** does the system exhibit that no single component produces alone?

## Step 6: Find Leverage Points

Using Donella Meadows' hierarchy of leverage points (from least to most effective):
1. Constants, parameters, numbers (subsidies, taxes, standards)
2. Buffer sizes (inventories, reserves, slack)
3. Structure of material stocks and flows
4. Delays relative to system change rates
5. Strength of negative feedback loops
6. Gain of positive feedback loops
7. Structure of information flows (who has access to what)
8. Rules of the system (incentives, constraints, punishments)
9. Power to change system structure (self-organization)
10. Goals of the system
11. Mindset or paradigm from which the system arises
12. Power to transcend paradigms

- Where are the **high-leverage intervention points** in this specific system?
- Which interventions might cause **policy resistance** (the system pushing back)?

## Step 7: Unintended Consequences

- For any proposed intervention, trace the effects through the feedback loops:
  - **First-order effects** (immediate, intended)
  - **Second-order effects** (downstream, often unintended)
  - **Third-order effects** (systemic shifts, emergent)
- What could go **wrong** if we intervene at the proposed point?

## Step 8: Recommendations

- Propose interventions that work **with** the system's dynamics, not against them.
- Identify what to **monitor** to detect whether the intervention is working or backfiring.
- Suggest **adaptive strategies** that can adjust as the system responds.

---

The goal is not to predict the future — complex systems are inherently unpredictable in detail. The goal is to understand the *structure* that produces behavior, and to intervene wisely.


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