Individual skill
experiment-design
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.
dist/pi/.agents/skills/experiment-design/SKILL.md
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- Last checked
- Nov 4, 2026
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- 1,043
- Repository last updated
- Oct 27, 2026
- License
- MIT
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- claude Repository topic
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- testing Derived category
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SKILL.mdcore · 31,727 bytesSKILL.mdcore · 31,727 bytesreferences/common-failures.mddoc · 10,946 bytesreferences/hypothesis-templates.mddoc · 8,742 bytesreferences/platform-comparison.mddoc · 10,392 bytesreferences/post-experiment-decision-framework.mddoc · 6,436 bytesreferences/pre-experiment-readiness-checklist.mddoc · 5,457 bytesreferences/results-interpretation-checklist.mddoc · 7,660 bytesreferences/sample-size-tables.mddoc · 6,398 bytes
Read excerpt
# Experiment Design
A senior product manager's playbook for running experiments that produce trustworthy decisions.
The default state of experimentation in most companies is sloppy. PMs run tests against vague hypotheses, look at results too early, ignore guardrails, stratify into noise, and ship features whose lift is mostly measurement error. The cost is real: ship the wrong thing, kill the right thing, learn the wrong lesson, repeat.
This skill is the discipline that prevents most of those mistakes. It assumes you have a working experimentation platform (Statsig, PostHog, GrowthBook, Optimizely, Amplitude, Eppo, Kameleoon; the platform does not matter for the principles). It assumes you have product-design and engineering pipelines that can deliver real treatment changes. The hard part is the thinking, and that is what is here.
When to use this skill: any time you are about to design or interpret an experiment. Read the relevant section before you start, not after the test is running.
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## What this skill covers