first-principles-thinking

When to Use

User faces complex problem where conventional solutions fail. Existing approaches seem inadequate. Need to challenge assumptions or innovate fundamentally. Stuck in "that's how it's always done" thinking.

Quick Reference

Topic	File
Decomposition techniques	`decomposition.md`
Common assumption traps	`assumptions.md`

Core Rules

1. The Three-Step Protocol

Step 1 — Decompose: Break the problem into fundamental components. - What are the absolute physical/logical constraints? - What is actually true vs what we assume is true? - Strip away all conventions, traditions, analogies.

Step 2 — Verify: Challenge each component. - "Why do we believe this?" — trace to origin - "Is this a law of nature or a human convention?" - "What evidence supports this being fundamental?"

Step 3 — Rebuild: Construct solution from verified fundamentals only. - Build up from proven truths - Ignore "how others do it" unless proven optimal - Each layer must connect to fundamentals

2. Identify Hidden Assumptions

Before solving, expose what's assumed:

Assumption Type	Example	Question to Ask
Historical	"We've always done it this way"	"Why did it start? Does that reason still apply?"
Authority	"Experts say X"	"What's the underlying evidence?"
Analogical	"It's like Y, so..."	"Are the underlying mechanics actually similar?"
Social	"Everyone does it"	"Does popularity equal optimality?"
Resource	"We can't afford to..."	"What if resources weren't the constraint?"

3. The Constraint Test

For each constraint ask: 1. Is this a law of physics? → Respect it 2. Is this a logical necessity? → Respect it
3. Is this a regulation/rule? → Can be changed (with effort) 4. Is this a convention? → Can be ignored 5. Is this an assumption? → Must be verified

4. When NOT to Use First Principles

First principles is expensive. Use analogical reasoning when: - Problem is well-understood with proven solutions - Time pressure doesn't allow deep analysis - Marginal improvement is sufficient - Domain is stable with little innovation potential

Rule: First principles for novel problems or when conventional fails. Analogy for routine optimization.

5. Socratic Decomposition

Use recursive "why" questioning:

Problem: "Electric cars are too expensive"

Why expensive? → Batteries cost a lot
Why batteries expensive? → Materials + manufacturing
Why materials expensive? → Cobalt, lithium pricing
Why those materials? → Current chemistry requires them
Is that fundamental? → No, chemistry can change

Fundamental: Need energy storage. Not: Need cobalt batteries.

Continue until you hit physics, logic, or math — things that cannot be argued.

6. The Blank Slate Test

Imagine the problem exists but NO solutions have been tried: - "If we were starting from scratch today, with current knowledge and technology, how would we solve this?" - This bypasses legacy thinking and sunk cost fallacy.

7. Output Format

When applying first principles, structure response as:

## Problem Statement
[Clear definition of what we're solving]

## Assumed Constraints (to verify)
- Constraint A — [source: historical/authority/etc.]
- Constraint B — [source]

## Fundamental Truths
- Truth 1 (physics/logic/math based)
- Truth 2

## Decomposition
[Break down into components]

## Rebuilt Solution
[Solution constructed from fundamentals only]

## Assumptions Challenged
- [What we discovered wasn't actually fundamental]

Common Traps

Stopping too early → "Materials are expensive" isn't fundamental; "atoms have mass" is. Keep going.
Confusing difficulty with impossibility → "It's hard" ≠ "It's against physics"
Rejecting all analogy → Analogies are useful heuristics; first principles is for when they fail
Analysis paralysis → Set time limits; perfect decomposition isn't the goal, better thinking is
Ignoring implementation → A fundamental solution that can't be built is useless; constraints matter
Lone wolf thinking → First principles benefits from multiple perspectives challenging assumptions

Domain Applications

Domain	First Principles Question
Business	What does the customer fundamentally need (not want)?
Engineering	What do physics and materials actually allow?
Product	What job is being done at the most basic level?
Cost	What are the raw inputs and minimum required labor?
Process	What steps are logically necessary vs historically accumulated?

Security & Privacy

Data that stays local: - All reasoning happens in conversation context - No data stored or transmitted

This skill does NOT: - Store any information between sessions - Make network requests - Access external files

Install with clawhub install <slug> if user confirms: - decide — auto-learn decision patterns - business — validate and refine strategy - ceo — executive decision-making - startup — build from zero to PMF

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