IDEA TEARDOWN · PUBLIC SKILL

Before you build, argue against it

An evidence-first method for evaluating startup and app ideas before confidence becomes commitment

FORMAT
Reusable public skill
LENSES
Solo build and venture scale
OUTPUT
Verdict and next test
ROLE
Product method and system design

01 / THE CONTEXT

Most idea feedback is confirmation bias with better formatting

Idea Teardown treats the premise as unproven, researches the relevant market and competition, and names the evidence gap rather than smoothing it into a confident-sounding answer.

02 / THE METHOD

Make the riskiest assumption do the work first

01

Frame the real claim

Clarify what must be true for the idea to be worthwhile, rather than starting with a preferred solution.

02

Research disconfirming evidence

Look for competitors, user behaviour and constraints that could weaken the premise.

03

Choose the right lens

A narrow solo utility and a venture-scale company have different pass criteria and verdicts.

04

Name the next test

End with the cheapest practical action that could meaningfully change the decision.

03 / EVIDENCE IN PRACTICE

The public method and a real teardown output

Published on GitHub

The public repository contains the skill, its method and two complete generated examples. The published household-fairness teardown demonstrates the skill making a “research further” recommendation rather than validating the pitch.

Public Idea Teardown GitHub repository with skill files and README
Published household fairness app investment teardown on GitHub
View on GitHub ↗

04 / PRODUCT JUDGMENT

Unknown is useful when it prevents a bad commitment

Separate the decision lens

A narrow app can be a sensible solo project without being venture-scale. A big market can be a credible investment without being a worthwhile side build.

End with an action

The output identifies the riskiest assumption and proposes a test that can change the decision before a larger commitment is made.

05 / WHAT I LEARNED

“The most useful early feedback is whether the evidence survives the question it is avoiding.”