experiment-designer
Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.
Profile →npx skills add alirezarezvani/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 Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions. ## When To Use Use this skill for: - A/B and multivariate experiment planning - Hypothesis writing and success criteria definition - Sample size and minimum detectable effect planning - Experiment prioritization with ICE scoring - Reading statistical output for product decisions ## Core Workflow 1. Write hypothesis in If/Then/Because format - If we change `[intervention]` - Then `[metric]` will change by `[expected direction/magnitude]` - Because `[behavioral mechanism]` 2. Define metrics before running test - Primary metric: single decision metric - Guardrail metrics: quality/risk protection - Secondary metrics: diagnostics only 3. Estimate sample size - Baseline conversion or baseline mean - Minimum detectable effect (MDE) - Significance level (alpha) and power Use: ```bash python3 scripts/sample_size_calculator.py --baseline-rate 0.12 --mde 0.02 --mde-type absolute ``` 4. Prioritize experiments with ICE - Impact: potential upside - Confidence: evidence quality - Ease: cost/speed/complexity ICE Score = (Impact * Confidence * Ease) / 10 5. Launch with stopping
- When To Use
- Core Workflow
- Hypothesis Quality Checklist
- Common Experiment Pitfalls
- Statistical Interpretation Guardrails
- Tooling
- scripts/samplesizecalculator.py
python3 scripts/sample_size_calculator.py --baseline-rate 0.12 --mde 0.02 --mde-type absolute python3 scripts/sample_size_calculator.py \
What does the experiment-designer skill do?
Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.
How do I install it?
Run `npx skills add alirezarezvani/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 alirezarezvani/claude-skills, a repository with 23,791 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.