experiment-designer
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
npx skills add mohitagw15856/pm-claude-skills --skill experiment-designer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Experiment Designer Skill Produce rigorous experiment designs from product hypotheses, and interpret results with statistical and practical significance — so you can defend every decision to a sceptical engineering lead or data scientist. ## Required Inputs Ask the user for these if not provided: **For experiment design:** - Hypothesis (what change, what metric, what expected movement) - Current baseline metric value - Minimum detectable effect (MDE) — the smallest lift worth caring about - Available daily sample size **For results interpretation:** - Control and variant results (raw numbers or percentages) - P-value or confidence interval - Run duration (days) - Any anomalies observed during the test ## Two-Phase Process ### Phase 1: Experiment Design 1. Restate hypothesis as: "If we [change], we expect [metric] to [move by X%] because [reason]" 2. Define control and variant clearly 3. Select primary metric (one only) and secondary guardrail metrics (2-3 max) 4. Calculate required sample size from MDE and baseline 5. Estimate run time in days 6. Set pre-defined success criteria before the test runs — no moving goalposts 7. Flag design risks: novelty effects, seasonal confounds,
- Required Inputs
- Two-Phase Process
- Phase 1: Experiment Design
- Phase 2: Results Interpretation
- Output Structure
- Quality Checks
- Anti-Patterns
What does the experiment-designer skill do?
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill experiment-designer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.