What problem does it solve?
This Skill helps you stop guessing and instead systematically turn uncertain problems into clear outcomes by defining success, testing multiple hypotheses, measuring results, and iterating based on evidence.
Core Features & Use Cases
- Goal-first success criteria: define measurable success so you can judge experiments objectively.
- Hypothesis plurality: generate and prioritize multiple competing hypotheses (minimum 3) to avoid confirmation bias.
- Minimum viable experiments: design the smallest test that meaningfully learns whether a hypothesis is right or wrong.
- Honest measurement + analysis: collect goal-relevant data, compare against pre-committed criteria, and label hypotheses as confirmed/refuted/inconclusive.
- Iteration loop: decide whether to ship, implement, run the next test, or reframe the approach.
Use case: Debug a failing login flow by defining what “fixed” means, proposing competing causes (DB outage, auth regression, rate limiting, etc.), testing the fastest falsifications, and iterating until the success criteria are met.
Quick Start
Use the science Skill to solve this problem by stating the GOAL, generating at least three falsifiable hypotheses, choosing the quickest experiment per hypothesis, then measuring and iterating until the success criteria are satisfied.
