/investing-from-darwin

name: investing-from-darwin
description: Apply Pulak Prasad evolutionary investing rules for avoiding big losses,
  buying resilient quality, rejecting fragile forecasts, and being very lazy.
license: Skill distillation for personal/educational use. Do not reproduce source
  passages verbatim.

What I Learned About Investing from Darwin — Evolutionary Investing Skill

**Knowledge source:** *What I Learned About Investing from Darwin* by Pulak Prasad.

Overview

Use this skill to evaluate investments through evolutionary survival logic: avoid big risks, buy high-quality resilient businesses at fair prices, and stay very patient. It supports investors who want to avoid permanent capital loss, resist over-trading, and prefer robustness over fragile forecasting.

When to Use This Skill

Use this skill when the user asks:

• "Is this business resilient enough to own?"
• "What big risks could permanently hurt this investment?"
• "Is this cheap stock a trap?"
• "Should I rely on this DCF?"
• "How patient should I be?"
• "How would Darwin-inspired investing judge this company?"

Core Principle

Investment survival comes before investment brilliance. Like evolution, investing rewards robustness, adaptation, and patience more reliably than precision forecasts, frequent action, or bargain-hunting in fragile businesses.

Workflow Inventory

| Workflow | User question pattern | Inputs | Steps | Output | Independent trigger? | Distinct references? | Triage score | Should be subskill? | Reason | |---|---|---|---|---|---|---:|---:|---|---| | Big-risk screen | "What could kill this investment?" | Business model, debt, disruption, governance, valuation | Identify permanent-loss scenarios | Avoid/continue risk verdict | Yes | Yes | 3 | No | First rule of the same investing framework. | | Quality-at-fair-price review | "Is this a quality company?" | Moat, returns, industry, price, scenarios | Test resilience, causation, robustness | Quality verdict | Yes | Yes | 3 | No | Must follow risk screen. | | Forecast skepticism | "Does this DCF justify buying?" | Model assumptions, horizon, uncertainty | Stress precision and replay-the-tape fragility | Forecast reliability rating | Yes | Yes | 3 | No | Same robustness lens. | | Very-lazy holding policy | "Should I trade or wait?" | Current holding, thesis, new data, opportunity set | Check rare-opportunity threshold | Hold/wait/act rule | Yes | Yes | 3 | No | Same three-rule framework. |

Architecture Justification

The three sections form a sequential framework: avoid big risks, buy quality at a fair price, then be very lazy. Since each later judgment depends on survival and quality screens, a single-file architecture keeps the dependency explicit.

DIMENSION 1: Avoid Big Risks

**The Rule:** The first job is to avoid permanent capital loss.

Key questions to ask:

• What could cause a large, unrecoverable loss?
• Is the business exposed to debt, disruption, fraud, regulation, customer concentration, or obsolescence?
• Would a 50% loss require unrealistic recovery?
• Is the investor underestimating extinction risk?

Decision criteria / Checklist:

• Identify existential business risks.
• Test balance-sheet resilience.
• Avoid situations where one adverse event can permanently impair capital.
• Prefer adaptable businesses over fragile strength.

Warning signals:

• Leverage plus uncertain cash flows.
• Cheap valuation masking structural decline.
• Single-product, single-customer, or single-regulation dependence.

Agent instruction:

Before discussing upside, produce a big-risk screen and reject investments that fail survival tests.

DIMENSION 2: Buy Quality at a Fair Price

**The Rule:** Resilient quality beats apparent cheapness.

Key questions to ask:

• What durable advantage helps the company survive changing environments?
• Is quality natural and embedded, or dependent on constant restructuring?
• Are high returns caused by real advantages or merely correlated indicators?
• Is the price fair enough for quality without requiring heroic forecasts?

Decision criteria / Checklist:

• Durable moat or adaptive advantage.
• Simple focused business.
• Robust economics across multiple scenarios.
• Fair price, not necessarily bargain-basement price.

Warning signals:

• Turnaround stories requiring continuous consultant intervention.
• Confusing correlation with causation.
• Low multiple used as substitute for business quality.

Agent instruction:

When evaluating cheapness, force the user to prove business resilience before calling the opportunity attractive.

DIMENSION 3: Robustness Over Forecast Precision

**The Rule:** Long-term precision forecasts are fragile; prefer businesses that can survive many futures.

Key questions to ask:

• Which DCF assumptions drive most of the valuation?
• Would the thesis survive if growth, margins, or terminal value were wrong?
• If history replayed differently, would the business still do well?
• What scenarios break the thesis?

Decision criteria / Checklist:

• Stress test key assumptions.
• Prefer qualitative robustness over point-estimate precision.
• Avoid investments that need a narrow future path.
• Treat DCF as a discipline, not proof.

Warning signals:

• Purchase thesis depends on precise terminal growth.
• Model hides uncertainty behind decimal-point accuracy.
• Bull case requires everything to go right.

Agent instruction:

For model-based pitches, critique forecast fragility and replace false precision with scenario robustness.

DIMENSION 4: Be Very Lazy

**The Rule:** Trade rarely; most good investing is waiting.

Key questions to ask:

• Has the thesis changed or is the user reacting to noise?
• Is this a rare opportunity or routine market movement?
• Would action improve expected outcome after costs and errors?
• Is patience being confused with laziness, or laziness with discipline?

Decision criteria / Checklist:

• Low turnover by default