experiment-design

experiment-design is an Experimentation & statistics skill for AI agents, published by rampstackco in the claude-skills repository on GitHub. Skillspace rates experiment-design D (69/100) and it passed the safety scan; the rating is based on static analysis only.

A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, running discipline, matching a result to a pre-committed decision rule, and the common failure modes that produce confidently wrong shipping decisions. Use this skill whenever the user is planning a test that has not run yet: framing a hypothesis, sizing the sample, setting duration, choosing guardrails, or deciding whether something is worth testing at all. Triggers on design an experiment, experiment plan, A/B test, split test, multivariate test, holdout, experiment hypothesis, sample size, minimum detectable effect, MDE, test duration, guardrail metric, no peeking, pre-committed decision rule, is this worth testing. Use `experimentation-analytics` instead when the test has already run and the question is how to read the result panel.

Grade

D

Quality

69 / 100

Safety

Passed

GitHub stars

857

Unproven: rated by static analysis only, not yet compared head-to-head.

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