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Skills from A Smart Bear

Hypotheses: Your best guesses, written down to facilitate learning

Convert your untested beliefs about customers, pain, and price into numbered, falsifiable hypotheses — the raw material your customer interviews are designed to test. Adapted from Chapter 10 of Hidden Multipliers: A multiplier of one's own.

Input
Your GOALS.md goal questions, plus what you currently believe about customers, pain, price, and competition.
Output
A HYPOTHESES.md file: numbered, falsifiable hypotheses (H1, H2, …), each mapped to the goal questions it addresses.

What this is about

It feels wrong to write down the answers before interviewing anyone — the whole point is to discover the truth, not presume you have it. But recorded predictions are what make interviews teach you anything: an unwritten belief cannot lose an argument with reality, and confirmation bias will quietly discard every answer that contradicts what you already think. Writing your best guesses down first — even the ones that feel too obvious to bother with — is the step that defeats it. It's also where great interview questions come from: each question is a miniature experiment designed to test one hypothesis.

This is the second step of the interview workshop that begins with goal questions. This skill takes your GOALS.md as input, then walks your goals one at a time, drawing out what you actually believe about each — and pressing your beliefs into specific, falsifiable form, because "customers care about speed" is a mood while "customers over X page-views lose revenue when the site is slow, and have paid to fix it" is a claim that can lose. It will offer candidate hypotheses when you're stuck, but only as templates for you to correct: the list must be your bets, because half the value of the exercise is the wrestling, and a plausible list you never owned teaches you nothing when reality contradicts it. When the list is sharp, it's preserved in HYPOTHESES.md — numbered H1, H2, …, each mapped to the goals it addresses — ready for the next step of the method.

Expect half of them to be wrong. That's not a flaw in your list; that's why you're interviewing.

Example invocation

You can invoke the skill like this:

/asb-interview-hypotheses Here's my GOALS.md from the goal-questions
exercise. Let's write the hypotheses — I have a lot of opinions about our
property-manager customers but I've never written any of them down.

The session walks your goals one at a time, drawing out what you believe and pressing vague hunches into specific, falsifiable claims. You walk away with HYPOTHESES.md: your unvalidated beliefs in prose, then H1, H2, … with goal mappings.

One practical note: say where you're working. Name a directory (or point at your existing files) when you invoke, and the skill keeps the method's files together there — if you don't, it asks before creating anything.

From the source

Two sources form the foundation of this skill:

  • As in Chapter 10 of Hidden Multipliers: A multiplier of one's own (section "Hypotheses: Your Current Best Guesses") — hiddenmultipliers.com — the hypotheses step itself: the prediction-science case for writing guesses down, the eighteen-hypothesis WP Engine example with its goal mappings, and the famous wrong ones (security, free trials) that interviews overturned.
  • The Iterative-Hypothesis customer development method — the published article version of the method, including the warning that AI-generated hypotheses are only templates to correct to what you actually believe, because half the value is thinking it through yourself.

Supporting articles each supply a piece of the mechanism: