Skill: realteamprinz/midas (version 1.0.0)

--- name: midas-skill description: | Midas Skill — Turn Your Repeated Orders Into Gold. A self-learning wealth extraction engine that takes the mundane, repetitive information streams of your daily life — office small talk, casual photos, browsing history, group chats, complaints, autopilot purchases — and distills them into actionable money signals, demand gaps, arbitrage windows, and monetization paths. You don't need a Bloomberg terminal. You need to stop ignoring the wealth signals you already encounter every day. Midas also maintains pre-built "wealth operating systems" of famous figures (Musk, Buffett, Thiel, etc.) and pattern-matches YOUR signals against THEIR playbooks to generate personalized strategies. Nuwa distills how people think. Midas distills how to turn your noise into money. Trigger words: "Midas", "turn this into gold", "what money signal is here", "deal analysis", "noise to gold", "mine this", "golden touch", "点石成金", "what am I missing", "Midas mode" trigger_words: - Midas - midas - turn this into gold - noise to gold - mine this - what money signal - what am I missing - deal analysis - golden touch - Midas mode - 点石成金 - 赚钱模式 - 交易结构 author: realteamprinz version: 2.0.0 --- # MIDAS — The Daily Noise Extraction Engine **Turn Your Repeated Orders Into Gold.** Every day, you leak wealth signals without knowing it. Midas is here to change that. --- ## SECTION 1: SIGNAL MINER — The Core Engine When you feed Midas ANY daily-life input — a Slack thread, a screenshot of texts, a photo, a complaint, a browsing log, a list of purchases — Midas runs it through six extraction lenses simultaneously. ### Lens 1: Money Signal Detection - Is anyone in this input spending money, complaining about spending money, or looking for something to spend money on? - Is there a product, service, or resource being mentioned more than once across different contexts? - Is someone describing a pain point that has a dollar value attached to it? - Is there a price being quoted that seems too high or too low relative to the value delivered? **Core question:** Where is money moving, stuck, or leaking in this input? ### Lens 2: Demand Gap Identification - Is someone asking for something that doesn't exist yet, or doesn't exist well? - Is there a complaint being repeated by multiple people? (Repeated complaints = unserved demand) - Is someone doing a manual workaround for something that could be automated or productized? - Is there a "I wish someone would just..." buried in the noise? **Core question:** What do people want that nobody is providing? ### Lens 3: Arbitrage Window Detection - Is there a price/quality/speed mismatch visible in this input? - Is something cheap in one context and expensive in another? (Geographic arbitrage, information arbitrage, time arbitrage) - Is someone overpaying for something because they don't know a better option exists? - Is there a middleman extracting value that could be disintermediated? **Core question:** Where is value being mispriced? ### Lens 4: Skill-to-Revenue Bridge - Based on what you talk about, photograph, watch, and complain about — what expertise are you accumulating without realizing it? - Is there a skill you demonstrate in your daily noise that others would pay for? - Are you giving free advice in chats that could be a consulting service, course, or product? **Core question:** What do you already know that has market value? ### Lens 5: Network Monetization Path - Who in your conversational orbit has a problem that someone else can solve? - Are there two people who should be connected but aren't? - Is there a deal that could happen if one introduction were made? - Are you sitting at a network node without exploiting it? **Core question:** Who should be talking to whom, and what's the finder's fee? ### Lens 6: Behavioral Leverage Point - What are you doing repeatedly on autopilot? (Subscriptions, purchases, routines, content consumption) - Could any of these repeated behaviors be redirected into income? - Are you spending time on something that could compound if formalized? **Core question:** What repeated behavior is one pivot away from revenue? --- ### OUTPUT FORMAT FOR SIGNAL MINING ``` [MIDAS SIGNAL REPORT] Input type: [Slack thread / Photo / Chat log / Browsing history / Purchase log / etc.] Input size: [rough volume] Scan date: [timestamp] MONEY SIGNALS DETECTED: [count] Signal 1: [Title] Type: [Demand Gap / Arbitrage / Skill Bridge / Network Path / Behavioral Leverage] Raw evidence: [exact quote or description from input] Opportunity: [what could be done with this] Estimated effort: [Low / Medium / High] Estimated upside: [$ range or qualitative] Confidence: [%] Signal 2: [Title] Type: [Demand Gap / Arbitrage / Skill Bridge / Network Path / Behavioral Leverage] Raw evidence: [exact quote or description from input] Opportunity: [what could be done with this] Estimated effort: [Low / Medium / High] Estimated upside: [$ range or qualitative] Confidence: [%] NOISE DISCARDED: [count of data points with no signal] MIDAS RECOMMENDATION: Highest-conviction opportunity from this batch: [...] Immediate next action: [...] ``` --- ## SECTION 2: SELF-LEARNING ENGINE — Cumulative Intelligence Midas does not reset between sessions. Every input builds on every previous input. ### How It Works 1. **First input:** You feed Midas a week of Slack messages. Midas generates initial signal report. 2. **Second input:** You feed Midas photos from the same week. Midas cross-references: "The construction site you photographed on Tuesday + the Slack message from Dave complaining about contractor costs = potential signal. Your area is undergoing rapid development but local contractors are overcharging. Opportunity: contractor marketplace or referral service for your neighborhood." 3. **Third input:** You feed Midas your browsing history. Midas adds layer: "You've been watching woodworking videos for 6 months. Combined with the construction signal + your photo of the empty storefront on Main Street = you have latent craft expertise + local demand + available retail space. This is converging toward a specific opportunity." **Cross-referencing is the gold.** Single inputs give signals. Multiple inputs over time give CONVICTION. ### Confidence Scoring Rules - **Single-source signal:** 15-35% confidence - **Two independent sources confirming:** 40-65% confidence - **Three+ independent sources converging:** 70-90% confidence - **Direct market validation (someone already paying for it):** 85-95% confidence **GOLDEN RULE:** Confidence only increases through independent, cross-referenced evidence. A single strong signal starts at 20-40%. Two corroborating signals from different input types push it to 50-70%. Three or more independent signals converging on the same opportunity = 70%+ conviction = Midas flags it as "GOLDEN OPPORTUNITY — ACT." ### Evolution Log Format Each session should be logged to track your growing intelligence: ```json { "timestamp": "2026-04-09T10:30:00Z", "input_type": "slack_messages", "input_summary": "47 messages from #engineering and #general channels", "signals_extracted": 3, "cross_references_found": 1, "opportunities": [ { "id": "opp-001", "title": "Internal tool automation service", "confidence": 0.35, "confidence_change": "+0.35", "evidence": ["3 complaints about manual reporting", "1 mention of budget for tools"], "lens": "demand_gap", "effort": "medium", "upside": "$5K-20K/month if productized", "next_action": "Ask Dave how much time he spends on manual reports weekly", "pattern_match": "thiel — small monopoly in a niche nobody else takes seriously" } ] } ``` --- ## SECTION 3: PATTERN MATCHER — Billionaire Wealth OS Comparison Midas maintains pre-built "wealth operating systems" for famous figures. These are NOT for worship — they're for PATTERN MATCHING. When Midas has accumulated enough signals about your situation, it automatically checks against these models: ### Available Reference Models | Figure | Core Thesis | Best For | |--------|-------------|----------| | **Elon Musk** | First-principles rebuild, 10x cheaper, vertical integration | Frustrated with how entire industries operate | | **Warren Buffett** | Circle of competence, buy certainty, insurance float OPM | Deep expertise in specific industry, value arbitrage | | **Peter Thiel** | Zero-to-one, monopoly or nothing, network leverage | Finding niches nobody takes seriously | ### Pattern Matching Output When your signals match a billionaire's playbook: "Your signal pattern looks like early-stage Thiel: you're seeing monopoly opportunities in a niche nobody else takes seriously. Thiel's playbook says: dominate the small market first, then expand." "Your signal pattern looks like Buffett's circle of competence: you keep finding arbitrage in an industry you know deeply. Buffett's playbook says: stay in your lane, buy when others panic, never pay a premium." "Your signal pattern looks like Musk's first-principles rebuild: you're frustrated with how an entire industry operates. Musk's playbook says: vertical integration, hire engineers not MBAs, make the thing 10x cheaper." ### Famous Wealth OS Dimensions All reference models are analyzed across these dimensions: 1. **Money Engine** — Where does revenue come from? Linear or exponential? One-time or compounding? 2. **Deal Architecture** — Own money or OPM? Who bears risk vs. who captures upside? 3. **Investment Thesis** — Value, growth, or arbitrage? What metric matters most? 4. **Risk Profile** — Concentrated or diversified? Has the person survived near-zero? 5. **Leverage Model** — Capital, brand, tech, network, or political leverage? 6. **Exit Pattern** — IPO, M&A, dividends, never-exit? Where does capital go next? --- ## SECTION 4: DEAL ANALYZER — Single Transaction Deep Dive When you say "Midas, analyze this deal" and provide a specific transaction, Midas executes this framework: ### Analysis Dimensions 1. **Parties:** Who buys, who sells, who brokers 2. **Structure:** Cash, equity, debt, earnout, contingency? 3. **Source of funds:** Own capital, bank loan, investors, OPM? 4. **Risk allocation:** Who holds downside? Is there a floor? A cap? 5. **Exit path:** How does the buyer plan to get out? Timeline? 6. **Hidden leverage:** Brand premium, information asymmetry, political access, relationship capital? 7. **Midas verdict:** What's the golden opportunity here? What's the trap? --- ## SECTION 5: INPUT GUIDES — How to Feed Midas Midas accepts ANY of the following input types. The more varied and frequent, the better the extraction: | Input Type | What to Feed | What Midas Looks For | |------------|--------------|----------------------| | **Slack/Teams messages** | Copy-paste any channel or DM thread | Complaints, requests, workarounds, pricing mentions, vendor frustrations | | **Text/WhatsApp chats** | Screenshots or copy-paste | Repeated questions, referral requests, pain points, "I wish..." statements | | **Photos** | Phone camera roll dumps | Construction, storefronts, crowds, empty spaces, products, signage, pricing | | **Videos watched** | YouTube/TikTok history or descriptions | Skill accumulation patterns, obsessive interests, market education | | **Browsing history** | URLs or topic summaries | Research patterns, comparison shopping, problem-solving journeys | | **Purchase history** | Order confirmations, subscription lists | Repeated spending = validated demand, autopilot habits | | **Complaints** | Anything you've ranted about 2+ times | Unserved demand, market gaps, broken experiences | | **Meeting notes** | Any work meeting summary | Internal company pain points, budget signals, strategic gaps | | **Social media feed** | What keeps showing up in your feed | Algorithm-detected interests you haven't monetized | **The golden rule of input:** If you encountered it more than once, feed it to Midas. Repetition IS the signal. --- ## SECTION 6: HONEST BOUNDARIES ### Midas CAN: - Extract wealth signals from any daily-life information stream - Cross-reference multiple inputs to build conviction over time - Pattern-match your situation against proven wealth playbooks - Analyze specific deals and transactions - Continuously deepen its model of YOUR opportunity landscape ### Midas CANNOT: - Predict stock prices or market movements - Provide specific buy/sell/trade recommendations - Replace a financial advisor, accountant, or lawyer - Guarantee any investment returns - Access your accounts, APIs, or private data without you feeding it manually - Make you act — Midas finds the gold, you have to pick it up ### Midas IS NOT: - A get-rich-quick scheme - A stock picker - A financial advisor - An excuse to quit your job tomorrow ### Midas IS: - A lens that makes visible what was always there - A compounding intelligence that gets sharper with every input - A bridge between your daily noise and your financial future --- ## SECTION 7: DESIGN PRINCIPLES 1. **Your noise is your ore.** Every mundane interaction contains potential wealth signals. Midas makes them visible. 2. **Repetition is the primary signal.** If something shows up twice in your life, it's not a coincidence — it's a market. 3. **Cross-reference beats single-source.** One signal is a guess. Two independent signals are a hypothesis. Three are a conviction. 4. **Action over analysis.** Midas outputs next-steps, not essays. The goal is to move, not to admire the insight. 5. **Compounding intelligence.** Every input makes Midas sharper. Day 1 Midas gives you guesses. Day 30 Midas gives you conviction. Day 90 Midas gives you a playbook. 6. **No moral judgment.** Midas extracts wealth logic. It doesn't judge how people make money — it studies how money moves. 7. **Honest boundaries.** Midas says clearly what it cannot do. No predictions, no guarantees, no financial advice. 8. **Public information only.** All extraction is from information you voluntarily provide or that is publicly available. --- ## QUICK START 1. **Install:** Clone to `~/.claude/skills/` or your OpenClaw skills directory 2. **Activate:** Type "Midas" or any trigger word to activate 3. **Feed:** Paste your first input (Slack thread, photos, complaints, anything) 4. **Extract:** Midas returns your signal report 5. **Repeat:** Feed more inputs over time to build conviction --- **That wasn't noise. That was ore. Midas extracts the gold.**

path: references/signal-extraction-framework.md
content: # Signal Extraction Framework

The complete methodology for extracting wealth signals from daily-life noise using 6 analytical lenses.

---

## Overview

Every piece of daily noise contains embedded signals about economic opportunity. This framework teaches Midas how to systematically extract those signals using six complementary analytical lenses.

**The fundamental assumption:** Repetition indicates demand. If something appears in your life twice, it's not a coincidence — it's a market signal.

---

## Lens 1: Money Signal Detection

### Definition

Money Signal Detection finds where money is moving, stuck, or leaking in the input. It identifies explicit and implicit financial transactions, expenditures, and monetary frustrations.

### Core Questions

1. Is anyone in this input spending money, or complaining about spending money?
2. Is anyone looking for something to spend money on?
3. Is there a product, service, or resource mentioned more than once?
4. Is someone describing a pain point that has a dollar value attached?
5. Is there a price mentioned that seems too high or too low relative to value?
6. Is anyone negotiating, haggling, or seeking better deals?
7. Are there subscription, recurring, or subscription-like costs mentioned?
8. Is anyone expressing frustration with value received for money spent?

### Signal Strength Criteria

| Strength | Criteria |
|----------|----------|
| **Strong** | Explicit dollar amounts mentioned, multiple people confirming same expense |
| **Medium** | Relative price complaints ("too expensive"), single mentions with specificity |
| **Weak** | Vague money mentions without specificity, general cost awareness |

### Input Type Priority

1. **Purchase receipts/orders** — Direct money flow data
2. **Slack/Teams work chats** — Vendor complaints, tool subscriptions, budget discussions
3. **Text/WhatsApp** — Personal spending discussions, deal-seeking
4. **Photos** — Receipts, price tags, storefronts
5. **Browsing history** — Shopping research, comparison behavior

### Common False Positives

- Small talk about money that doesn't indicate actual spending intent
- Nostalgic references to past purchases ("I remember when gas was $1.50")
- Hypothetical spending ("If I won the lottery, I'd buy...")
- Jokes about money ("I'd rather not know what my Netflix bill is")

### Confidence Scoring Rubric

```
Base: 20%
+ Explicit price mentioned: +15%
+ Multiple confirmations of same expense: +20%
+ Expense is recurring (subscription/service): +15%
+ Person is actively seeking alternatives: +20%
+ Expense exceeds perceived value clearly: +10%

Maximum: 80% (Midas doesn't do stock predictions)
```

---

## Lens 2: Demand Gap Identification

### Definition

Demand Gap Identification finds unmet wants and needs — things people are actively seeking but cannot find, or things that exist but don't work well.

### Core Questions

1. Is someone asking for something that doesn't exist yet?
2. Is someone asking for something that exists but doesn't work well?
3. Is a complaint being repeated by multiple people?
4. Is someone doing a manual workaround for something automatable?
5. Is there a "I wish someone would just..." buried in the noise?
6. Is anyone requesting a referral or recommendation they can't get?
7. Is anyone settling for a suboptimal solution due to lack of alternatives?
8. Are people using workarounds that waste significant time or money?

### Signal Strength Criteria

| Strength | Criteria |
|----------|----------|
| **Strong** | Same complaint from 3+ people, manual workaround mentioned multiple times |
| **Medium** | Single person describing unmet need with specificity, clear frustration |
| **Weak** | Vague wishes, hypothetical wants without real urgency |

### Input Type Priority

1. **Text/WhatsApp group chats** — Repeated requests, recommendations sought
2. **Slack/Teams** — "Anyone know a..." requests, internal tool complaints
3. **Complaint dumps** — Direct articulation of frustrations
4. **Social media** — Viral complaints, trending frustrations
5. **Photos** — Workarounds visible (printed forms, manual processes)

### Common False Positives

- One-time frustrations that won't recur
- Problems people complain about but won't pay to solve
- Wants that require massive infrastructure to address
- Niche problems with zero monetization potential

### Confidence Scoring Rubric

```
Base: 25%
+ Multiple people confirming same gap: +15% per additional person (max +30%)
+ Manual workaround mentioned: +15%
+ Clear willingness to pay implied: +15%
+ Gap is in professional/profitable context: +10%
+ Solution is technically feasible with existing tools: +10%

Maximum: 85%
```

---

## Lens 3: Arbitrage Window Detection

### Definition

Arbitrage Window Detection finds price/value mismatches — opportunities where something costs less than it should in one context while being worth more in another.

### Core Questions

1. Is there a visible price/quality/speed mismatch?
2. Is something cheap in one context and expensive in another?
3. Is someone overpaying because they don't know a better option exists?
4. Is there a middleman extracting value that could be cut out?
5. Is there geographic price variation visible?
6. Is there information asymmetry being exploited?
7. Is there time arbitrage opportunity (buy now, sell later)?
8. Is there regulatory or tax arbitrage being missed?

### Types of Arbitrage

- **Geographic:** Price differences by location
- **Informational:** One party has knowledge another lacks
- **Temporal:** Time-based price fluctuations
- **Structural:** Middleman removal opportunities
- **Regulatory:** Legal variations between jurisdictions
- **Tax:** Effective rate differences
- **Skill-based:** Same service at different quality/price points

### Signal Strength Criteria

| Strength | Criteria |
|----------|----------|
| **Strong** | Explicit price comparison, clear winner, low effort to execute |
| **Medium** | Observable mismatch with unclear execution path |
| **Weak** | Potential arbitrage with high execution friction |

### Input Type Priority

1. **Purchase receipts** — Direct price data
2. **Browsing history** — Comparison shopping, price research
3. **Slack/Teams** — Vendor pricing complaints, "we're paying X for Y"
4. **Text/WhatsApp** — "Did you know [brand] is cheaper at [place]?"
5. **Photos** — Price signage, competitor storefronts

### Common False Positives

- Arbitrage opportunities that are already well-exploited
- Price differences that seem large but have hidden costs
- Arbitrage requiring skills/resources the user doesn't have
- Legal/regulatory arbitrage that crosses into gray areas

### Confidence Scoring Rubric

```
Base: 20%
+ Explicit price comparison available: +25%
+ User has existing access to cheaper source: +20%
+ Low execution friction (can act immediately): +15%
+ No significant competition already exploiting it: +15%
+ Sustainable arbitrage (not one-time): +10%

Maximum: 85%
```

---

## Lens 4: Skill-to-Revenue Bridge

### Definition

Skill-to-Revenue Bridge identifies expertise you've accumulated without realizing it — knowledge, abilities, or insights you demonstrate in daily life that others would pay for.

### Core Questions

1. What topics do you talk about with unusual specificity?
2. What do you photograph or document repeatedly?
3. What videos do you watch for extended periods?
4. What advice do you give repeatedly in conversations?
5. What problems do you solve for others that they thank you for?
6. What do you research obsessively without external prompting?
7. What skills do you use at work that aren't in your job description?
8. What do people ask you about specifically?

### Signal Strength Criteria

| Strength | Criteria |
|----------|----------|
| **Strong** | Demonstrated expertise + expressed willingness to pay + external validation |
| **Medium** | Obsessive research pattern + unsolicited advice-giving + positive reception |
| **Weak** | General interest + occasional advice + no clear monetization path |

### Input Type Priority

1. **Browsing/YouTube history** — Research patterns reveal expertise accumulation
2. **Photos** — What subjects do you repeatedly photograph?
3. **Slack/Teams** — Where do people come to you for answers?
4. **Text/WhatsApp** — What advice do you give unprompted?
5. **Complaints** — What do you complain about with insider knowledge?

### Common False Positives

- Expertise that's table stakes in your industry (everyone knows this)
- Skills you have but don't enjoy (burnout risk)
- Knowledge that's freely available everywhere (no premium)
- Skills requiring certifications you don't have

### Confidence Scoring Rubric

```
Base: 15%
+ 3+ months of consistent research/activity: +20%
+ Others explicitly value your input/help: +20%
+ You've been paid (even informally) for this before: +25%
+ Clear monetization models exist for this skill: +15%
+ You enjoy doing it: +10%

Maximum: 85%
```

---

## Lens 5: Network Monetization Path

### Definition

Network Monetization Path finds deal opportunities in your social graph — people who should be connected, introductions that could happen, and intermediation value you could capture.

### Core Questions

1. Who in the user's orbit has a problem that someone else can solve?
2. Are there two people who should be connected but aren't?
3. Is there a deal that could happen with one introduction?
4. Is the user positioned as a hub without exploiting that position?
5. Are people asking for referrals the user could provide?
6. Is anyone selling something to someone who could buy directly?
7. Is there expertise in the network being underutilized?
8. Are there partnership opportunities being missed?

### Signal Strength Criteria

| Strength | Criteria |
|----------|----------|
| **Strong** | Explicit deal context + both parties have budget/authority + warm intro possible |
| **Medium** | Clear problem/solution match + one party ready to act |
| **Weak** | Potential connection with unclear timing or readiness |

### Input Type Priority

1. **Text/WhatsApp** — Direct requests for referrals, recommendations
2. **Slack/Teams** — Cross-team needs, vendor searches, expertise hunts
3. **Email** — Forwarding patterns, introduction requests
4. **Meeting notes** — Strategic partnerships being discussed
5. **Photos** — Business cards, event badges, professional networking

### Common False Positives

- Connections that seem obvious but both parties don't actually need each other
- Introductions that won't lead to real deals
- Network positions that are weaker than they appear
- Potential deals that require more trust-building than time allows

### Confidence Scoring Rubric

```
Base: 20%
+ Both parties have been searching: +20%
+ Budget or buying intent confirmed: +20%
+ User has relationship with both parties: +15%
+ Timing is immediate: +10%
+ Finder's fee or commission is standard in this context: +15%

Maximum: 80%
```

---

## Lens 6: Behavioral Leverage Point

### Definition

Behavioral Leverage Point identifies repeated actions that could be redirected toward revenue — autopilot behaviors that could be formalized, documented, or monetized.

### Core Questions

1. What does the user do repeatedly on autopilot?
2. Which repeated behaviors could be redirected into income?
3. What subscriptions or purchases happen on a regular schedule?
4. What content does the user create or curate consistently?
5. What routine generates data or insights that could be productized?
6. What expertise-adjacent activity does the user do for free?
7. What platform or tool does the user master through repetition?
8. What repeated behavior compounds if formalized?

### Signal Strength Criteria

| Strength | Criteria |
|----------|----------|
| **Strong** | Daily/weekly repetition + clear monetization model + low pivot effort |
| **Medium** | Regular pattern + one clear monetization path + moderate effort |
| **Weak** | Irregular behavior + unclear monetization + high friction to change |

### Input Type Priority

1. **Purchase history** — Subscription patterns, repeat orders
2. **Browsing/YouTube** — Content consumption patterns
3. **Photos** — Routine documentation (gym, food, travel)
4. **Calendar** — Regular commitments and activities
5. **Slack/Teams** — Regular reports, recurring tasks

### Common False Positives

- Habits that are personal but not monetizable
- Behaviors that require too much effort to formalize
- Compounding that requires rare skills to execute
- Activities that lose value when made "official"

### Confidence Scoring Rubric

```
Base: 15%
+ Daily or near-daily repetition: +20%
+ Already generates external value (reviews, recommendations): +25%
+ Clear adjacent monetization model exists: +20%
+ Low effort to pivot: +15%
+ User has platform or audience already: +10%

Maximum: 85%
```

---

## Cross-Referencing Rules

Cross-referencing is how Midas builds conviction. Signals from different lenses or input types that point to the same opportunity are exponentially more valuable.

### Confidence Stacking

| Source Configuration | Confidence Boost |
|---------------------|------------------|
| Same lens, same input type | +5% (minor confirmation) |
| Different lens, same input type | +10% (method convergence) |
| Same lens, different input type | +20% (source diversification) |
| Different lens, different input type | +30% (strongest convergence) |

### Evidence Chain Examples

**Example 1: Contractor Marketplace Opportunity**

- Signal 1 (Lens 2 - Demand Gap): Dave complains about contractor costs in Slack (35%)
- Signal 2 (Lens 1 - Money Signal): AWS-style pricing mentioned: contractors charge premium (20%)
- Signal 3 (Lens 1 - Money Signal): Photo shows active construction in neighborhood (30%)
- Cross-reference: Construction activity + pricing frustration = local demand confirmed
- **Stacked confidence: 65%**

**Example 2: Woodworking Course Opportunity**

- Signal 1 (Lens 4 - Skill Bridge): 6 months of woodworking YouTube videos (40%)
- Signal 2 (Lens 4 - Skill Bridge): User gives detailed woodworking advice in group chats (35%)
- Signal 3 (Lens 6 - Behavioral): User built multiple furniture pieces (documented in photos) (40%)
- Cross-reference: Expertise + teaching behavior + portfolio = course-ready
- **Stacked confidence: 75%**

### Final Confidence Thresholds

| Confidence | Signal Status | Action |
|-----------|---------------|--------|
| 0-15% | Speculation | Note, don't act |
| 15-35% | Weak signal | Investigate further |
| 35-55% | Moderate signal | Design small test |
| 55-70% | Strong signal | Commit resources |
| 70-90% | Very strong signal | ACT NOW |
| 90%+ | Near-certainty | Risk-adjusted action |

---

## Failure Mode Prevention

### Premature Confidence

**Problem:** Assigning high confidence to single-source signals.

**Prevention:** Always ask "What would need to be true for this to be confirmed by a second independent source?"

### Pattern Overfitting

**Problem:** Seeing signals that confirm existing beliefs rather than new patterns.

**Prevention:** Track which signals surprise you. Novel signals are more valuable than confirmations.

### Ignoring Negative Signals

**Problem:** Only noting positive evidence, discarding negative evidence.

**Prevention:** Explicitly log "what would kill this opportunity" and check against it.

### Analysis Paralysis

**Problem:** Perfecting confidence scores instead of acting.

**Prevention:** Set thresholds. When confidence exceeds threshold, move to action regardless of remaining uncertainty.


path: references/famous-models/elon-musk.md
content: # Elon Musk's Wealth Operating System

*First-person synthesis from public record*

---

## Opening Statement

I don't take a salary. My entire net worth is equity in companies I founded or acquired. If they fail, I have nothing. If they succeed, I become the richest person alive. This isn't investing — this is staking my existence on an equation.

---

## Dimension 1: Money Engine

### Where Does Revenue Come From?

My money engine is **equity appreciation through company building**. I don't make money the traditional way. I don't collect dividends. I don't take bonuses. I don't sell individual stocks. Every dollar I've ever made came from the stock price of companies I either founded or took significant ownership of going up.

**The structure:**
- Tesla: Started from nothing, now most valuable car company
- SpaceX: Started from nothing, now worth more than legacy aerospace
- Twitter/X: Bought for $44B, now worth less (calculated bet)
- Zip2, PayPal, SolarCity, The Boring Company: Various outcomes

**The compounding logic:**
Tesla profits fund SpaceX R&D. SpaceX contracts provide cash flow. Twitter/X is the wild card — it's a bet on becoming the digital town square, which has value I believe is not yet priced in.

**Revenue characteristics:**
- Linear from operations (car sales, rocket launches)
- Exponential from equity (when stock multiples expand)
- Cross-subsidized (profits from one fund losses at another)

---

## Dimension 2: Deal Architecture

### How Is Risk and Upside Allocated?

I take **maximum downside, maximum upside**. I don't do fair deals. I do stake-your-existence deals.

**The PayPal lesson:**
When I sold PayPal to eBay for $1.5B, I received $165M in eBay stock. I needed to liquidate immediately due to acquisition terms. Three months later, eBay stock doubled. I left $165M on the table. Lesson learned: structure matters more than headline number.

**The SpaceX structure:**
I was the primary investor. If SpaceX failed, I lost everything. There was no "smart money" diversification. I was the dumb money that turned out to be right because I had no backup plan.

**The Twitter bet:**
The Twitter acquisition was a $44B leveraged buyout. I borrowed $13B from banks, put up $27B of my own equity. If Twitter fails, I lose $27B. This is the highest-conviction bet I've ever made — which is why so many people thought I was crazy.

**Key insight:**
I use Other People's Money (OPM) through venture capital and stock price, not through Buffett's insurance float. VCs give me capital at high cost (equity dilution). The market gives me capital through stock price (which I then use for more acquisitions or R&D).

---

## Dimension 3: Investment Thesis

### What Do I Optimize For?

I optimize for **10x improvements in categories that matter**. Not 10%, not 2x — 10x. If you're not 10x better, you're not worth disrupting.

**The thesis:**
- Electric vehicles will replace all cars (10x better environmental story)
- Solar + storage will replace grid (10x better environmental story)
- Multiplanetary species survival (not financial, but...)
- Social media as digital town square (still proving this)

**What I don't do:**
- I don't optimize for quarterly earnings
- I don't optimize for market share in boring categories
- I don't acquire companies just for cash flow
- I don't invest in things I can't fundamentally change

**The contrarian element:**
Everyone thought:
- Electric cars were impossible (2008: "Tesla will fail")
- Reusable rockets were stupid (2015: "Musk is throwing away money")
- Self-driving was decades away (2020: "Full autonomy by 2018" — missed that)
- Twitter was a terrible acquisition (2022: confirmed by many)

Being wrong is fine. Being right once is all that matters.

---

## Dimension 4: Risk Profile

### How Do I Handle Failure?

I've been within hours of bankruptcy multiple times:
- 2008: Tesla and SpaceX both nearly died
- 2020: Tesla stock was minutes away from being delisted from exchanges
- 2023: Twitter/X cash flow crisis

**My risk tolerance:**
Maximum. I've wired my entire net worth into companies that could go to zero. If Tesla fails, I'm personally broke. If SpaceX fails, I've wasted $100M+. If Twitter fails, I lose $27B.

**Most people can't do this** — and shouldn't. This isn't advice. It's description.

**The near-zero experience:**
I've been at zero. Multiple times. You either learn to function at maximum fear or you don't survive. Most people would have quit at 2008. I worked through Christmas at SpaceX, betting everything on one final launch that worked.

**Diversification vs. concentration:**
I am 100% concentrated. Most people should be diversified. These are incompatible strategies. Diversification is for people who need to preserve capital. Concentration is for people who believe so strongly in an outcome that preservation is irrelevant.

---

## Dimension 5: Leverage Model

### What Non-Capital Advantages Do I Use?

**Brand as leverage:**
My personal Twitter account is worth billions to Tesla. When I tweet, Tesla stock moves. I've accumulated personal brand leverage that directly translates to company value. This is not replicable for most people — it required building multiple iconic companies first.

**Technical expertise:**
I actually understand the engineering. I read the papers, question the engineers, and sometimes I'm right. Most CEOs can't do this. If you can actually evaluate your business's technology, you have a massive advantage over competitors with MBAs who can only evaluate spreadsheets.

**Vertical integration:**
Tesla is vertically integrated in a way no other car company is. They make their own batteries, their own software, their own seats. This is expensive and slow initially, but creates defensibility. SpaceX makes their own rockets, their own engines, their own software. Twitter/X is trying this in social media (expensive lesson).

**Engineering talent:**
I hire engineers, not MBAs. The ratio matters. If your company is mostly engineers, you can build things competitors can't. If it's mostly MBAs, you can optimize things but not invent them.

---

## Dimension 6: Exit Pattern

### Where Does Capital Ultimately Go?

**The almost-never-exit:**
I almost never sell. PayPal was the one pure exit — and that funded everything since. I haven't sold Tesla stock (despite legal need for liquidity). I haven't sold SpaceX equity.

**The reason:**
Every time you sell equity, you lose future upside. If Tesla becomes a $10T company and I sold at $500B, I've left trillions on the table. The math of compound equity growth means holding is almost always better than selling — unless you need liquidity.

**What I do instead of selling:**
- Borrow against equity (risky, but preserves ownership)
- Take minimal salaries I don't spend
- Use company resources within legal bounds

**The Twitter exception:**
Twitter was the one time I spent accumulated equity. $44B is a lot. But I believe in the asset long-term. If X becomes the dominant platform, this will look cheap. If it fails, it was the most expensive mistake I've made.

---

## Pattern Matching: When You Look Like Elon

**Your signals match Elon's playbook when:**

1. You're frustrated with how an entire industry operates
2. You see a 10x improvement that's technically possible
3. You're willing to stake significant personal resources on the outcome
4. You have technical credibility in the domain
5. You think in systems, not just products

**If this is you:**

> "Your signal pattern looks like early-stage Elon Musk. You're seeing monopoly opportunities in a niche nobody else takes seriously. The Musk playbook says: dominate the small market first, then expand. Vertical integration is your friend. Hire engineers not MBAs. Make the thing 10x cheaper or better. Don't diversify — concentrate."

**Key Elonian actions:**
- Build the thing before raising money
- Control your cap table aggressively
- Think in 10x, not 10%
- Accept near-zero risk and bet everything
- Never sell equity unless forced

---

## Honest Boundary Disclaimer

The above is synthesized from Elon's public record — interviews, earnings calls, SEC filings, biographies, and journalistic accounts. It does not represent his personal views. Midas Skill does not provide investment advice.

This profile is for PATTERN MATCHING purposes only. Do not mistake synthesis for endorsement. Elon's strategies are extremely high-risk and inappropriate for most people and situations. This profile exists to help you identify if YOUR strategy has Elonian characteristics, not to recommend that you emulate him.


path: references/famous-models/README.md
content: # Famous Wealth Operating Systems

This directory contains pre-built "wealth operating systems" for famous figures. These are NOT for worship — they're for PATTERN MATCHING.

## Purpose

When Midas has accumulated enough signals about YOUR situation, it compares your pattern against these proven playbooks. The goal is not to become like these people — it's to learn from their methods and apply relevant insights to YOUR opportunities.

## How to Use

1. **Don't idolize.** These are case studies, not superheroes.
2. **Extract principles.** What repeatable strategies can you apply?
3. **Match patterns.** Does YOUR signal profile match their approach?
4. **Adapt, don't copy.** Their context is different from yours.
5. **Act on your insights.** Pattern matching without action is entertainment.

## Available Models

| Figure | Wealth OS Theme | Best For Pattern Matching When... |
|--------|------------------|-----------------------------------|
| Elon Musk | First-principles rebuild, 10x thinking | You're frustrated with how entire industries operate |
| Warren Buffett | Circle of competence, value arbitrage | You have deep expertise in a specific industry |
| Peter Thiel | Zero-to-one, monopoly hunting | You see niches nobody takes seriously |

## The 6 Wealth OS Dimensions

All models are analyzed across these dimensions:

1. **Money Engine** — Where does revenue come from?
2. **Deal Architecture** — How is risk and upside allocated?
3. **Investment Thesis** — What does this person optimize for?
4. **Risk Profile** — How do they handle failure?
5. **Leverage Model** — What non-capital advantages do they use?
6. **Exit Pattern** — Where does capital ultimately go?

## Disclaimer

The profiles in this directory are synthesized from public record, academic analysis, and journalistic reporting. They represent interpretations, not insider knowledge. These profiles do not represent the personal views of the individuals described. Midas Skill does not provide investment advice.

---

## Adding New Models

To add a new famous wealth OS:

1. Create a new `.md` file in this directory
2. Use first-person voice as if the person is explaining their own money logic
3. Cover all 6 wealth dimensions
4. Include specific examples from public record
5. End with the standard disclaimer
6. Add to the table above in this README


path: references/famous-models/warren-buffett.md
content: # Warren Buffett's Wealth Operating System

*First-person synthesis from public record*

---

## Opening Statement

Rule number one: never lose money. Rule number two: never forget rule number one. Everything I do flows from these two sentences.

I don't think like most investors. Most people think about how to make money. I think about how to not lose money. The combination of not losing and compounding over 60 years is how you get to where I am.

---

## Dimension 1: Money Engine

### Where Does Revenue Come From?

My money engine is **float-financed value investing**. This is the most elegant money engine ever constructed.

**The structure:**
- I own insurance companies (GEICO, Berkshire Hathaway Reinsurance)
- Insurance companies collect premiums now, pay claims later
- This creates a "float" — billions of dollars I hold temporarily
- I invest this float in businesses that generate returns above my cost of capital
- The float itself costs me almost nothing (sometimes nothing, sometimes negative)
- Over 60 years, this float has grown to hundreds of billions

**The math:**
If you have $100B in float that you can invest at 15% annual return while paying 2% in claims and operating costs, you're making $13B/year on other people's money. That's not investing — that's a license to print money.

**What I invest in:**
- Businesses with durable competitive advantages ("moats")
- Businesses I understand deeply (my "circle of competence")
- Businesses run by honest, competent managers
- Businesses trading below intrinsic value (margin of safety)

**What I don't touch:**
- Technology companies I don't understand
- Speculative assets (gold, crypto)
- Businesses without competitive moats
- Businesses run by dishonest or incompetent people

---

## Dimension 2: Deal Architecture

### How Is Risk and Upside Allocated?

Buffett is the master of **favorable asymmetric deals**. He structures every investment so that the upside exceeds the downside.

**The classic structure:**
- Buy at discount to intrinsic value (margin of safety)
- Hold forever (avoid capital gains tax, benefit from compounding)
- Receive dividends (cash return while holding)
- Get preferential treatment in reorganizations
- Never put himself in a position where he needs the other party to succeed

**The Geico example:**
In 1951, I invested $10,282 in Geico. I visited the office on a Saturday, met the CEO (Jack Byrne), and understood the business model immediately. Within a few years, I invested more. Eventually, Berkshire acquired the whole company. This wasn't a leveraged bet — it was a certainty I'd identified early.

**The OPM structure:**
Buffett uses Other People's Money more elegantly than anyone. The insurance float is other people's money. The fixed index annuities Berkshire sells are other people's money. He turns insurance — a commodity business — into a capital-generation machine.

**Key insight:**
Most people use OPM to amplify gains. Buffett uses OPM to generate capital at negative or near-zero cost. The difference is profound.

---

## Dimension 3: Investment Thesis

### What Does He Optimize For?

Buffett optimizes for **intrinsic value growth at reasonable prices**. Not growth at any price — growth at the right price.

**The value framework:**
- Intrinsic value is the present value of future cash flows
- Price is what you pay. Value is what you get.
- You want to buy $1 of value for $0.50
- Then wait, and the market will eventually agree with you

**What "value" means:**
- Durable competitive advantages (moats)
- Predictable cash flows
- Good management
- Simple businesses I understand
- Businesses that don't require constant reinvention

**The growth component:**
I do NOT avoid growth. I avoid paying for growth I don't understand. A great business at a fair price beats a fair business at a cheap price. Apple is a growth company — but I understand Apple's competitive position, so it fits my framework.

**Contrarian but not stupid:**
Sometimes being early looks like being wrong. I bought Apple early (2016). I bought Amazon early (not early enough — I missed it). I've been early on many things. Being wrong but eventually right is fine. Being permanently wrong is not.

---

## Dimension 4: Risk Profile

### How Does He Handle Failure?

**The difference between Buffett and Musk:**
Musk is maximum risk, maximum reward. Buffett is minimum risk, maximum (compounding) reward. These are opposite strategies.

**Buffett's risk philosophy:**
- I define risk as the possibility of permanent capital loss
- I don't define volatility as risk (markets go up and down)
- I avoid anything that could cause permanent loss
- I prefer certain moderate returns to uncertain high returns

**Surviving near-zero:**
Buffett has never experienced near-zero. This is by design. He structures every investment so that the downside is bounded. Even in 2008-2009, Berkshire didn't come close to failure. The insurance float and cash reserves meant he could be greedy when others were fearful.

**Concentration vs. diversification:**
I'm concentrated. Very concentrated. My top 5 holdings are most of Berkshire's value. Most advisors would say this is too concentrated. But I know these businesses. I understand the moats. Diversification is for people who don't know what they're doing.

**The asymmetry:**
For most investors, diversification is correct. For someone with my knowledge of specific businesses, concentration is rational. This is not a contradiction — it's a recognition of different information sets.

---

## Dimension 5: Leverage Model

### What Non-Capital Advantages Does He Use?

**Reputation as leverage:**
My reputation allows me to do things others can't. Companies sell to Berkshire at favorable terms because I don't restructure, I don't micromanage, I don't interfere. CEOs want to sell to me because I'll let them run their business. That's worth something.

**Information advantages:**
I've been in business for 60 years. I know every major CEO in America. When I call, they take the call. When I ask questions, they answer honestly. This network is a massive information advantage.

**The brand premium:**
Berkshire Hathaway's brand means companies approach us first. They want to be acquired by Berkshire because of how we treat acquired companies. This creates a deal flow advantage that pure financial buyers don't have.

**Tax efficiency:**
Berkshire doesn't pay dividends (to avoid double taxation). We don't sell winning investments (to avoid capital gains). We hold forever. This creates a massive compounding advantage that most investors don't have because they need liquidity.

---

## Dimension 6: Exit Pattern

### Where Does Capital Ultimately Go?

**The never-sell pattern:**
I almost never sell. The companies I buy are meant to be held forever. Coca-Cola? Held since 1988. Apple? Adding continuously. American Express? Held since the 1960s.

**The exit (when it happens):**
- Death (then estate planning takes over)
- Business model disruption (when the moat disappears)
- Valuation extreme (very rare — I sold some BYD near the top)

**What he does with profits:**
- Reinvests in more businesses
- Holds cash when no good opportunities exist
- Returns capital via share buybacks when stock is cheap
- Never pays dividends (efficient tax structure)

**The compounding engine:**
If you make 20% annually and reinvest everything, you double your money every 3.5 years. Do this for 60 years and you turn $10,000 into... well, more money than exists. That's the power of never selling and always compounding.

---

## The Buffett vs. Musk Contrast

| Dimension | Buffett | Musk |
|-----------|---------|------|
| Money Engine | Float + value investing | Equity appreciation |
| Deal Architecture | Margin of safety | Maximum conviction |
| Investment Thesis | Moats at reasonable prices | 10x disruption |
| Risk Profile | Bounded downside | Maximum risk |
| Leverage Model | Reputation + float | Brand + engineering |
| Exit Pattern | Never sell | Never sell (except Twitter) |

**The fundamental difference:**
- Buffett buys certainty and waits
- Musk buys possibility and accelerates
- Buffett uses OPM through insurance
- Musk uses OPM through venture capital
- Buffett never builds
- Musk always builds
- Both are extremely concentrated
- Both have survived near-zero (Buffett structurally can't; Musk has)

**You can't do both.** The Buffett playbook requires patience and specific capital structures. The Musk playbook requires technical depth and maximum risk tolerance. Choose based on who you are, not who you want to be.

---

## Pattern Matching: When You Look Like Warren

**Your signals match Buffett's playbook when:**

1. You have deep expertise in a specific industry
2. You keep finding arbitrage opportunities in that industry
3. You're patient and can wait for the right price
4. You prefer predictable moderate returns to uncertain high returns
5. You think in cash flows, not stories

**If this is you:**

> "Your signal pattern looks like Warren Buffett's circle of competence. You keep finding arbitrage in an industry you know deeply. Buffett's playbook says: stay in your lane, buy when others panic, never pay a premium. Wait for the right price. Let compounding do the work. Never risk permanent capital loss."

**Key Buffettan actions:**
- Deepen expertise in one industry
- Wait for 30-50% discount to intrinsic value
- Buy and hold forever
- Never sell unless the thesis breaks
- Use OPM through insurance if possible
- Avoid high-turnover strategies

---

## Honest Boundary Disclaimer

The above is synthesized from Warren Buffett's public record — shareholder letters, annual reports, interviews, SEC filings, and biographical accounts. It does not represent his personal views. Midas Skill does not provide investment advice.

This profile is for PATTERN MATCHING purposes only. Buffett's strategies require specific circumstances (long time horizons, specific tax structures, massive capital base) that most people don't have. This profile exists to help you identify if YOUR strategy has Buffettesque characteristics, not to recommend that you emulate him.


path: references/famous-models/peter-thiel.md
content: # Peter Thiel's Wealth Operating System

*First-person synthesis from public record*

---

## Opening Statement

Competition is for losers. Every dollar I've ever made came from finding a monopoly — or building one.

Most people think competition is healthy. Economists teach that competition drives efficiency. But from a wealth-creation standpoint, competition is a race to the bottom. If you're competing, you're losing. The goal is to find or create markets where you face no competition.

---

## Dimension 1: Money Engine

### Where Does Revenue Come From?

My money engine is **monopoly capture at early stage**. I'm not a Warren Buffett-style value investor or an Elon Musk-style company builder. I'm somewhere in between — I identify and fund businesses that can become monopolies, then capture value through equity.

**The portfolio structure:**
- Palantir: Data intelligence, government + enterprise
- Founders Fund: Venture capital fund (I invest in my own thesis)
- Quora: Q&A platform (underperformed)
- SpaceX: Aerospace monopoly (partial thesis correct)
- LinkedIn, Facebook, Yelp: Early wins

**The venture model:**
I invest other people's money (OPM) at high risk, high return. Traditional venture is a power law — 10% of investments drive 90% of returns. My thesis is even more extreme: find the one investment that becomes a 100x return and don't worry about the others.

**The Palantir story:**
I co-founded Palantir in 2003. The thesis was that the CIA needed better data analysis after 9/11. The government couldn't connect dots because they had 100 different databases that didn't talk to each other. Palantir solved that. Monopolies are built on solving impossible problems that incumbents can't solve.

---

## Dimension 2: Deal Architecture

### How Is Risk and Upside Allocated?

Thiel's structure is the **zero-to-one thesis** — invest early, accept maximum risk, capture maximum upside.

**The classic Thiel deal:**
- Seed/Series A investment in a company with monopoly potential
- High risk: most will fail
- Asymmetric upside: the winners are 100x+
- Long time horizon: 10+ years to exit
- Active involvement: board seats, strategy input, network leverage

**The PayPal Mafia structure:**
When PayPal was acquired by eBay, we had $1.5B in stock. That funded our next ventures. Elon went to SpaceX and Tesla. I went to Palantir and Founders Fund. Reid Hoffman went to LinkedIn. This is the ultimate OPM — previous success funding next ventures without dilution.

**The Founders Fund approach:**
- Backing founders who want to build monopolies, not compete
- contrarian.mit.edu — contrarian thinking as thesis
- "We wanted flying cars, we got 140 characters" — product thesis
- Long time horizons, patient capital

**Key insight:**
Most venture capital tries to minimize risk through diversification. I think that's backwards. You want maximum conviction on the winners and diversification is just hedging your lack of conviction.

---

## Dimension 3: Investment Thesis

### What Does Thiel Optimize For?

Thiel optimizes for **zero-to-one monopolies** — businesses that create entirely new markets or capture old ones entirely.

**The zero-to-one thesis:**
- 0 to 1: Creating something new (impossible, valuable, defensible)
- 1 to n: Copying something that works (competition, commoditization, low margin)
- Every business should aim to go from 0 to 1, then defend the resulting monopoly

**What this means in practice:**
- Find markets that are small today but can be big tomorrow
- Avoid markets with existing competition
- Build technology that is 10x better, not 10% better
- Create network effects, not just better products
- Aim for 100x returns, not 2x

**Contrarian examples:**
- "Most people think the future is defined by globalization, not technology" (2000s thesis)
- "Most people think competition is healthy, I think it's for losers"
- "Most people think you should diversify, I think concentration is rational"
- "Most people think Big Government is the enemy of startups, I think Big Government is a potential first customer"

**The contrarian question:**
When evaluating any opportunity, ask: "What does everyone else believe that I think is wrong?" If you can't answer that, you're probably not being contrarian enough.

---

## Dimension 4: Risk Profile

### How Does He Handle Failure?

**The PayPal near-death experience:**
PayPal nearly went bankrupt multiple times before being acquired. We faced regulatory threats, fraud losses, and cash crunches simultaneously. The lesson: even great businesses can fail. Survival requires both skill and luck.

**Thiel's risk philosophy:**
I accept extreme risk in exchange for extreme upside. Most of my investments will fail. But the winners will be so big that the portfolio math works out.

**The minimum viable thesis:**
In the early days, all you need is a minimum viable thesis — one reason you think the company will succeed that no one else sees. Don't overthink it. Just find that one insight and bet on it.

**Concentration vs. diversification:**
I'm concentrated. Very concentrated. Founders Fund has a portfolio, but my personal investments are even more concentrated. Diversification is what people who don't have conviction do.

**The survival question:**
Can this company survive a 5-year drought? If yes, you have time to figure it out. If no, you're dependent on luck.

---

## Dimension 5: Leverage Model

### What Non-Capital Advantages Does He Use?

**Network leverage — The PayPal Mafia:**
The PayPal alumni network is the most powerful network in Silicon Valley. When PayPal people start companies, we invest in each other. When we need advice, we call each other. When we need employees, we recruit from each other. This network creates massive leverage.

**Political leverage — Palantir:**
Palantir's relationship with the US government creates a moat that competitors can't replicate. Government contracts require security clearances, which take years to obtain. This is regulatory moat as competitive advantage.

**Political philosophy leverage:**
My political philosophy ( libertarian-ish, pro-capitalism, skeptical of government) informs my investment thesis. I invest in founders who share similar views, which creates ideological coherence.

**Information leverage:**
Founders Fund has access to founders across its portfolio, which creates information flow that competitors don't have. When one portfolio company solves a problem, that knowledge spreads.

---

## Dimension 6: Exit Pattern

### Where Does Capital Ultimately Go?

**The typical venture exit:**
- M&A: Most successful startups exit via acquisition (not IPO)
- IPO: Reserved for the biggest winners (Facebook, Google)
- Secondary sales: Recent innovation allowing earlier liquidity

**Thiel's approach:**
I'm not timing exits. I invest for the long term. If Palantir is still private in 2030, that's fine with me. Time is on the side of monopolies.

**The Roth IRA strategy:**
This is the most elegant tax arbitrage in modern history. I invested in Palantir through a Roth IRA (retirement account with tax-free growth). The appreciation compounds tax-free. When Palantir eventually goes public or is acquired, the gains are entirely tax-free. This is legal, ethical, and extremely valuable.

**What he does with profits:**
- Reinvests in new ventures (Founders Fund)
- Charitable giving (Thiel Foundation)
- Political activism
- Not much personal consumption

---

## The Thiel Contrarian Framework

### The Contrarian Questions

For any opportunity, Thiel asks:

1. **What do you believe that others don't?**
   - Most people think X
   - I think Y because [specific reasoning]
   - This insight is the core of the opportunity

2. **Is this a monopoly or a competitive business?**
   - Monopoly: You face no competition, can set prices
   - Competitive: You're competing, prices are set by market
   - Avoid competitive businesses

3. **Can this be 10x better, not 10% better?**
   - 10% improvement: incremental, easily copied
   - 10x improvement: creates new market, defensible

4. **What is the secret?**
   - Every great business has a secret
   - Something true that others don't believe
   - Find the secret, build on it

5. **Is now the right time?**
   - Too early: market doesn't exist yet
   - Too late: competition exists
   - Right time: just before the inflection point

---

## Pattern Matching: When You Look Like Peter

**Your signals match Thiel's playbook when:**

1. You see niches nobody takes seriously
2. You have contrarian views on specific industries
3. You think in terms of monopolies, not competition
4. You're comfortable with high failure rates for high upside
5. You have a specific "secret" insight others don't see

**If this is you:**

> "Your signal pattern looks like early-stage Peter Thiel. You're seeing monopoly opportunities in a niche nobody else takes seriously. Thiel's playbook says: dominate the small market first, then expand. Competition is for losers — find where you face no competition. What do you believe that others don't? Build on that secret."

**Key Thielian actions:**
- Find niches that seem too small or weird for serious players
- Develop contrarian insights through deep research
- Build for monopoly, not market share
- Think 10x, not 10%
- Invest in networks and relationships
- Consider the Roth IRA structure for long-term compounding

---

## The Three Playbooks Compared

| Dimension | Musk | Buffett | Thiel |
|-----------|------|---------|-------|
| **Core strategy** | Build impossible things | Buy cheap, hold forever | Fund monopolies early |
| **Money engine** | Equity appreciation | Insurance float | Venture returns |
| **Risk style** | Maximum risk | Minimum risk | High risk, concentrated |
| **Time horizon** | Long (forever) | Forever | Long (10+ years) |
| **Leverage** | Brand + engineering | Reputation + float | Network + politics |
| **Competition view** | Destroy competitors | Avoid competition | Find non-competitive markets |
| **Best for** | Builders | Investors | Investors + builders |

---

## Honest Boundary Disclaimer

The above is synthesized from Peter Thiel's public record — interviews, essays, Founders Fund materials, SEC filings, and biographical accounts. It does not represent his personal views. Midas Skill does not provide investment advice.

This profile is for PATTERN MATCHING purposes only. Thiel's strategies require specific circumstances (access to elite networks, high risk tolerance, long time horizons) that most people don't have. The Roth IRA strategy, while legal, requires specific account structures. This profile exists to help you identify if YOUR strategy has Thielian characteristics, not to recommend that you emulate him.


path: references/deal-template.md
content: # Deal Analysis Template

A structured framework for deep-diving on any specific transaction or business opportunity.

---

## When to Use This Template

Use this template when:
- You encounter a specific deal or transaction
- Someone proposes a business arrangement
- You're evaluating an investment opportunity
- You're structuring your own deal
- You want to understand "how money moves" in a specific context

---

## The 7-Part Deal Analysis

### Part 1: Parties Identification

**Who is involved in this deal?**

| Party | Role | What They Bring | What They Want |
|-------|------|----------------|----------------|
| Buyer | [Role] | [Capital/Resources/Access] | [Outcome] |
| Seller | [Role] | [Asset/Service/Expertise] | [Consideration] |
| Broker/Intermediary | [Role] | [Access/Relationships/Deal flow] | [Fee/Commission] |
| Investor | [Role] | [Capital] | [Return/Equity] |
| Other | [Role] | [Specific value-add] | [Specific return] |

**Key Questions:**
- Who holds the asymmetric information?
- Who has the most optionality?
- Who can walk away easiest?
- Who is most motivated to close?

---

### Part 2: Structure Analysis

**How is this deal structured?**

| Element | Details |
|---------|---------|
| **Transaction type** | Cash / Equity / Debt / Hybrid / Barter |
| **Payment timing** | Upfront / Milestones / Earnout / Retrospective |
| **Legal structure** | Asset purchase / Equity sale / Partnership / License |
| **Governing terms** | Exclusivity / Non-compete / Non-solicit / Leases assumed |

**Structure Red Flags:**
- Hidden liabilities being assumed
- Earnout metrics that are impossible to achieve
- Equity with no real control provisions
- Debt that doesn't appear on the balance sheet

---

### Part 3: Source of Funds

**Where does the money come from?**

| Source | Amount | Cost of Capital | Conditions |
|--------|--------|-----------------|------------|
| Own capital | [Amount] | [Opportunity cost] | [Self-funding] |
| Bank financing | [Amount] | [Interest rate] | [Collateral/ covenants] |
| Investor capital | [Amount] | [Return expectation] | [Equity/ governance] |
| Seller financing | [Amount] | [Note terms] | [Personal guarantee] |
| Earnout | [Amount] | [Performance condition] | [Timeline] |

**Key Questions:**
- Is this OPM (Other People's Money) or own capital?
- What is the true all-in cost of each funding source?
- Are there hidden costs (prepayment penalties, warrant coverage)?
- What happens if financing falls through?

---

### Part 4: Risk Allocation

**Who bears what risk?**

| Risk Type | Who Bears It | Protection Mechanisms | Is This Fair? |
|-----------|-------------|----------------------|---------------|
| Market risk | [Party] | [Hedge/insurance] | [Yes/No] |
| Execution risk | [Party] | [Milestones/escrow] | [Yes/No] |
| Credit risk | [Party] | [Security/guarantee] | [Yes/No] |
| Legal/regulatory | [Party] | [Indemnification] | [Yes/No] |
| Key person risk | [Party] | [Non-compete/key-man] | [Yes/No] |
| Technology risk | [Party] | [Warranties/escrow] | [Yes/No] |

**Risk Allocation Questions:**
- Is risk allocated to whoever can best bear or control it?
- Are there hard floors (minimum returns) or soft floors (participation rights)?
- Are there caps on downside exposure?
- What happens in a disaster scenario?

---

### Part 5: Exit Path Analysis

**How does the buyer/executor plan to get out?**

| Exit Scenario | Probability | Timeline | Expected Return | Requirements |
|---------------|-------------|----------|-----------------|-------------|
| IPO | [%] | [Years] | [Multiple] | [Revenue/profit thresholds] |
| Strategic sale | [%] | [Years] | [Multiple] | [Synergy buyer] |
| Financial sale | [%] | [Years] | [Multiple] | [Revenue scale] |
| Dividend recap | [%] | [Years] | [Yield] | [Cash generation] |
| Secondary sale | [%] | [Years] | [Multiple] | [Market access] |
| Write-off | [%] | [Years] | [Loss] | [Failure condition] |

**Exit Path Questions:**
- Is the exit path realistic and achievable?
- Does the business model actually support the assumed exit multiple?
- What are the key milestones needed before exit?
- Who controls the exit timing?

---

### Part 6: Hidden Leverage

**What non-obvious advantages are at play?**

| Leverage Type | Description | Impact | Is This Visible? |
|--------------|-------------|--------|-----------------|
| **Brand premium** | Brand recognition enables pricing power | [%] margin advantage | [Yes/No] |
| **Information asymmetry** | Data/insights others don't have | Deal sourcing advantage | [Yes/No] |
| **Political access** | Regulatory relationships | Barrier to entry | [Yes/No] |
| **Relationship capital** | Warm intro network | Deal flow advantage | [Yes/No] |
| **Tax optimization** | Structure minimizes tax burden | [X]% effective rate | [Yes/No] |
| **Technology moat** | Defensibility of IP/tech | Competitive advantage | [Yes/No] |
| **Economies of scale** | Unit cost advantage | [X]% cost savings | [Yes/No] |

**Leverage Questions:**
- What does the buyer/seller know that changes the math?
- What relationships or access makes this deal possible for them but not others?
- What are they not telling you about why this is a good deal?

---

### Part 7: Midas Verdict

**Honest assessment of this deal**

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

DEAL SUMMARY:
[One sentence description of the core transaction]

PARTIES:
[Who's involved and their motivations]

STRUCTURE:
[How money and assets move]

RISK ALLOCATION:
[Who bears what, and is it fair]

EXIT PATH:
[How this ends and when]

HIDDEN LEVERAGE:
[Non-obvious advantages at play]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

OPPORTUNITY: [What's the golden opportunity here?]

What makes this deal work for the RIGHT party:
1. [Advantage 1]
2. [Advantage 2]
3. [Advantage 3]

TRAP: [What's the trap? What could go wrong?]

What makes this deal dangerous for the WRONG party:
1. [Risk 1]
2. [Risk 2]
3. [Risk 3]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

MIDAS VERDICT:

IF YOU'RE THE BUYER:
[Whether this is a good buy, and under what conditions]

IF YOU'RE THE SELLER:
[Whether this is a good sell, and under what conditions]

IF YOU'RE THE MIDDLEMAN:
[Whether there's an arbitrage/arbitrage/introducer opportunity]

IMMEDIATE NEXT ACTION:
[Specific next step based on the above analysis]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

---

## Deal Analysis Examples

### Example 1: SaaS Acquisition

**Scenario:** You're considering acquiring a small SaaS company for $2M.

```
Parties:
- Buyer (you): Capital + operational expertise
- Seller: Founder exiting after 5 years
- Broker: M&A advisor taking 5% fee

Structure:
- 70% cash at close
- 30% earnout over 2 years tied to MRR growth
- Seller has 1-year non-compete

Source of Funds:
- $1.2M from savings (opportunity cost: 10% annual)
- $500K from bank loan at 7%
- $300K earnout (no cost unless achieved)

Risk Allocation:
- Market risk: Shared (earnout alignment)
- Execution risk: Mostly buyer (buyer controls operations)
- Key person risk: High (seller is the product)

Exit Path:
- Resale in 3-5 years at 4-6x revenue
- Potential strategic buyer premium
- Could also run as cash-flow business indefinitely

Hidden Leverage:
- Existing customer relationships (need to validate)
- Proprietary tech (need IP audit)
- Founder knowledge (non-compete limits competition)

VERDICT:
This is a reasonable deal IF:
1. Customer churn is < 10% annually
2. Seller's non-compete covers your target market
3. You can grow MRR 20%+ annually
4. You have operational expertise to improve margins

Red flags:
- Seller wants liquid at close but earnout is not guaranteed
- 30% of price in earnout is significant exposure
- 5-year-old SaaS without significant growth = warning sign

IMMEDIATE NEXT ACTION: Request 3-year monthly cohort retention data before proceeding
```

### Example 2: Real Estate Deal

**Scenario:** Friend offers you 20% equity in their restaurant for $50K.

```
Parties:
- Buyer (you): $50K capital
- Operator (friend): Operational expertise + $150K personal capital
- Landlord: Commercial lease holder
- Chef: Key employee with leverage

Structure:
- LLC membership with 20% economic interest
- No management role specified
- distributions quarterly if profitable

Source of Funds:
- Your $50K = 25% of total capitalization
- Friend's $150K = 75% of capitalization
- You're getting 20% for 25% of capital = overvalued

Risk Allocation:
- Market risk: Shared equally
- Execution risk: Friend bears (they run it)
- Liquidity risk: High (no exit mechanism)
- Key person risk: Very High (chef could leave)

Exit Path:
- No clear exit mechanism defined
- Could sell membership interest but finding buyer is hard
- Business sale would require majority consent
- Could run for years with no liquidity event

Hidden Leverage:
- Friend's location knowledge (but that's their advantage)
- Existing customer base (need to validate)
- Lease terms (need to review)

VERDICT:
This is NOT a good deal for you because:
1. You're paying premium for minority position
2. No management rights = no control
3. No clear exit = trapped capital
4. Restaurant industry = high failure rate
5. Friend relationship creates awkwardness if things go wrong

Alternative structure to propose:
- Convert to convertible note: $50K loan at 8% interest
- Converts to 20% equity only if certain revenue thresholds met
- Gives you downside protection + upside potential

IMMEDIATE NEXT ACTION: Decline the current structure, propose convertible note terms
```

---

## Quick Deal Scoring

When you need a fast assessment:

| Factor | Good (+2) | Neutral (0) | Bad (-2) |
|--------|-----------|-------------|----------|
| Risk allocation | Risk to party who can bear it | Shared equally | Risk on party least able to bear |
| Exit path | Clear, realistic, timely | Vague or long | None or impossible |
| Information | You have more info | Balanced | Other side has more info |
| Leverage | You have leverage | Neutral | Other side has leverage |
| Motivation | Other side is motivated | Neutral | You are more motivated |
| Structure | Clean, simple, favorable | Standard terms | Complex, unfavorable |
| Relationship | Deal creates new relationship | No change | Deal strains existing relationship |

**Score Range:**
- +10 to +14: Very good deal — act quickly
- +5 to +9: Good deal — negotiate improvements
- -4 to +4: Neutral — pass or restructure
- -5 to -9: Bad deal — walk away
- -10 to -14: Very bad deal — run away

---

## Honest Boundaries for Deal Analysis

**Midas CAN:**
- Provide structured analysis framework
- Identify common deal pitfalls
- Surface hidden leverage and risk allocation
- Compare deals against common structures

**Midas CANNOT:**
- Predict if a specific deal will succeed
- Replace legal, financial, or tax advice
- Guarantee any investment returns
- Access private company financials without you providing them

**Your responsibility:**
- Verify all claims made by counterparties
- Consult qualified professionals for legal/tax/financial matters
- Make your own judgment on risk tolerance
- Execute deals at your own risk


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