investing-from-darwin

Name: investing-from-darwin
Author: simbajigege

12installs21forks

How to install investing-from-darwin?

simbajigege/book2skills/investing-from-darwin

$npx -y skills add simbajigege/book2skills --skill investing-from-darwin

Installs into the current project.

Summary

Apply Pulak Prasad evolutionary investing rules for avoiding big losses,

Files · 1

View on GitHub

SKILL.md

1	# What I Learned About Investing from Darwin — Evolutionary Investing Skill
2
3	Knowledge source: What I Learned About Investing from Darwin by Pulak Prasad.
4
5	## Overview
6
7	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.
8
9	## When to Use This Skill
10
11	Use this skill when the user asks:
12	- "Is this business resilient enough to own?"
13	- "What big risks could permanently hurt this investment?"
14	- "Is this cheap stock a trap?"
15	- "Should I rely on this DCF?"
16	- "How patient should I be?"
17	- "How would Darwin-inspired investing judge this company?"
18
19	## Core Principle
20
21	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.
22
23	## Workflow Inventory
24
25	\| Workflow \| User question pattern \| Inputs \| Steps \| Output \| Independent trigger? \| Distinct references? \| Triage score \| Should be subskill? \| Reason \|
26	\|---\|---\|---\|---\|---\|---\|---:\|---:\|---\|---\|
27	\| 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. \|
28	\| 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. \|
29	\| 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. \|
30	\| 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. \|
31
32	## Architecture Justification
33
34	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.
35
36	## DIMENSION 1: Avoid Big Risks
37
38	The Rule: The first job is to avoid permanent capital loss.
39
40	### Key questions to ask:
41	- What could cause a large, unrecoverable loss?
42	- Is the business exposed to debt, disruption, fraud, regulation, customer concentration, or obsolescence?
43	- Would a 50% loss require unrealistic recovery?
44	- Is the investor underestimating extinction risk?
45
46	### Decision criteria / Checklist:
47	- Identify existential business risks.
48	- Test balance-sheet resilience.
49	- Avoid situations where one adverse event can permanently impair capital.
50	- Prefer adaptable businesses over fragile strength.
51
52	### Warning signals:
53	- Leverage plus uncertain cash flows.
54	- Cheap valuation masking structural decline.
55	- Single-product, single-customer, or single-regulation dependence.
56
57	### Agent instruction:
58	Before discussing upside, produce a big-risk screen and reject investments that fail survival tests.
59
60	## DIMENSION 2: Buy Quality at a Fair Price
61
62	The Rule: Resilient quality beats apparent cheapness.
63
64	### Key questions to ask:
65	- What durable advantage helps the company survive changing environments?
66	- Is quality natural and embedded, or dependent on constant restructuring?
67	- Are high returns caused by real advantages or merely correlated indicators?
68	- Is the price fair enough for quality without requiring heroic forecasts?
69
70	### Decision criteria / Checklist:
71	- Durable moat or adaptive advantage.
72	- Simple focused business.
73	- Robust economics across multiple scenarios.
74	- Fair price, not necessarily bargain-basement price.
75
76	### Warning signals:
77	- Turnaround stories requiring continuous consultant intervention.
78	- Confusing correlation with causation.
79	- Low multiple used as substitute for business quality.
80
81	### Agent instruction:
82	When evaluating cheapness, force the user to prove business resilience before calling the opportunity attractive.
83
84	## DIMENSION 3: Robustness Over Forecast Precision
85
86	The Rule: Long-term precision forecasts are fragile; prefer businesses that can survive many futures.
87
88	### Key questions to ask:
89	- Which DCF assumptions drive most of the valuation?
90	- Would the thesis survive if growth, margins, or terminal value were wrong?
91	- If history replayed differently, would the business still do well

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investing-from-darwin

12installs21forks

View on GitHub

How to install investing-from-darwin?

simbajigege/book2skills/investing-from-darwin

$npx -y skills add simbajigege/book2skills --skill investing-from-darwin

Installs into the current project.

Summary

Apply Pulak Prasad evolutionary investing rules for avoiding big losses,

Files · 1

View on GitHub

SKILL.md

1	# What I Learned About Investing from Darwin — Evolutionary Investing Skill
2
3	Knowledge source: What I Learned About Investing from Darwin by Pulak Prasad.
4
5	## Overview
6
7	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.
8
9	## When to Use This Skill
10
11	Use this skill when the user asks:
12	- "Is this business resilient enough to own?"
13	- "What big risks could permanently hurt this investment?"
14	- "Is this cheap stock a trap?"
15	- "Should I rely on this DCF?"
16	- "How patient should I be?"
17	- "How would Darwin-inspired investing judge this company?"
18
19	## Core Principle
20
21	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.
22
23	## Workflow Inventory
24
25	\| Workflow \| User question pattern \| Inputs \| Steps \| Output \| Independent trigger? \| Distinct references? \| Triage score \| Should be subskill? \| Reason \|
26	\|---\|---\|---\|---\|---\|---\|---:\|---:\|---\|---\|
27	\| 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. \|
28	\| 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. \|
29	\| 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. \|
30	\| 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. \|
31
32	## Architecture Justification
33
34	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.
35
36	## DIMENSION 1: Avoid Big Risks
37
38	The Rule: The first job is to avoid permanent capital loss.
39
40	### Key questions to ask:
41	- What could cause a large, unrecoverable loss?
42	- Is the business exposed to debt, disruption, fraud, regulation, customer concentration, or obsolescence?
43	- Would a 50% loss require unrealistic recovery?
44	- Is the investor underestimating extinction risk?
45
46	### Decision criteria / Checklist:
47	- Identify existential business risks.
48	- Test balance-sheet resilience.
49	- Avoid situations where one adverse event can permanently impair capital.
50	- Prefer adaptable businesses over fragile strength.
51
52	### Warning signals:
53	- Leverage plus uncertain cash flows.
54	- Cheap valuation masking structural decline.
55	- Single-product, single-customer, or single-regulation dependence.
56
57	### Agent instruction:
58	Before discussing upside, produce a big-risk screen and reject investments that fail survival tests.
59
60	## DIMENSION 2: Buy Quality at a Fair Price
61
62	The Rule: Resilient quality beats apparent cheapness.
63
64	### Key questions to ask:
65	- What durable advantage helps the company survive changing environments?
66	- Is quality natural and embedded, or dependent on constant restructuring?
67	- Are high returns caused by real advantages or merely correlated indicators?
68	- Is the price fair enough for quality without requiring heroic forecasts?
69
70	### Decision criteria / Checklist:
71	- Durable moat or adaptive advantage.
72	- Simple focused business.
73	- Robust economics across multiple scenarios.
74	- Fair price, not necessarily bargain-basement price.
75
76	### Warning signals:
77	- Turnaround stories requiring continuous consultant intervention.
78	- Confusing correlation with causation.
79	- Low multiple used as substitute for business quality.
80
81	### Agent instruction:
82	When evaluating cheapness, force the user to prove business resilience before calling the opportunity attractive.
83
84	## DIMENSION 3: Robustness Over Forecast Precision
85
86	The Rule: Long-term precision forecasts are fragile; prefer businesses that can survive many futures.
87
88	### Key questions to ask:
89	- Which DCF assumptions drive most of the valuation?
90	- Would the thesis survive if growth, margins, or terminal value were wrong?
91	- If history replayed differently, would the business still do well

People who liked this also viewed

6 picks

Type