experiment-designer

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Design a single falsifiable experiment with its pass/fail threshold fixed BEFORE the run, so a result can't be rationalized after the fact. Use whenever someone has a hypothesis or risky assumption and is about to just try it and see — "how do I test whether X", "design an experiment for this", "is this optimization worth it", "how would I prove or disprove this", "what would falsify this", or any "let me just run it and look at the numbers" with no threshold named. The method isolates the one decisive question, states the hypothesis and its negation, fixes a numeric metric and a PASS/FAIL/KILL rule up front, picks the cheapest decisive design, controls the confound that would make the result lie (optimization-before-correctness especially), and emits a dated pre-registration plus a falsifiability check. Distinct from reality-check (verifies an existing plan's claims), goal-builder (writes Claude Code /goal conditions), and idea-validator (whole-idea GO/NO-GO); this designs one test and locks its bar.

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Skill Content

# Experiment Designer Turn one risky question into one experiment whose verdict you'll actually believe — because you wrote down what would change your mind *before* you looked at the result. The one rule everything serves: > **Fix the pass/fail bar before the run. Then don't move it.** That single discipline is the difference between a test that informs a decision and a number you rationalize past. The most creditable thing a doomed project can do is die on a bar it set in advance; the most common way good evidence gets wasted is moving the bar once the number is in. --- ## Why this exists When the result arrives, you are the most biased you will ever be about it — you've now got effort, hope, and a half-built thing riding on it. A threshold chosen *after* seeing data isn't a threshold, it's a justification. So this skill front-loads every judgment call (what we're testing, what counts as pass, what would kill it) into a moment when you have no result yet and therefore nothing to defend. Two failure modes it specifically targets: - **The un-falsifiable hypothesis.** "I think this will help" with no number and no fail branch can't be run, only believed. If you can't say what result would make you *abandon* the idea, you don't have an experiment. - **The confounded test that lies.** An optimization tuned for speed can ship a confident wrong answer if correctness was never the controlled baseline. A test that can pass while being broken is worse than no test. ## When t...

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Author
scoobydrew83
Repository
scoobydrew83/skills
Created
1 months ago
Last Updated
today
Language
Python
License
MIT

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