Stress Test
Use before betting on a plan whose core assumptions are unvalidated (a hockey-stick...
Install
npx skills add alirezarezvani/claude-skills --skill stress-test -g -a claude-code -y- Repository
- alirezarezvani/claude-skills29.1k
- Category
- Engineering
- Updated
- 4d ago
- License
- MIT
- Context cost
- Mediumabout 1,897 tokens
- Runs scripts
- No, instructions only
What it does
Use before betting on a plan whose core assumptions are unvalidated (a hockey-stick model, a long enterprise pilot).
/executive-mentor:stress-test, Business Assumption Stress Testing
Command: /executive-mentor:stress-test <assumption>
Take any business assumption and break it before the market does. Revenue projections. Market size. Competitive moat. Hiring velocity. Customer retention.
Why Most Assumptions Are Wrong
Founders are optimists by nature. That's a feature, you need optimism to start something from nothing. But it becomes a liability when assumptions in business models get inflated by the same optimism that got you started.
The most dangerous assumptions are the ones everyone agrees on.
When the whole team believes the $50M market is real, when every investor call goes well so you assume the round will close, when your model shows $2M ARR by December and nobody questions it, that's when you're most exposed.
Stress testing isn't pessimism. It's calibration.
The Stress-Test Methodology
Step 1: Isolate the Assumption
State it explicitly. Not "our market is large" but "the total addressable market for B2B spend management software in German SMEs is €2.3B."
The more specific the assumption, the more testable it is. Vague assumptions are unfalsifiable, and therefore useless.
Common assumption types:
- Market size, TAM, SAM, SOM; growth rate; customer segments
- Customer behavior, willingness to pay, churn, expansion, referrals
- Revenue model, conversion rates, deal size, sales cycle, CAC
- Competitive position, moat durability, competitor response speed, switching cost
- Execution, team velocity, hire timeline, product timeline, operational scaling
- Macro, regulatory environment, economic conditions, technology availability
Step 2: Find the Counter-Evidence
For every assumption, actively search for evidence that it's wrong.
