# product-experiments

Guides teams through hypothesis drafting, sample size calculation, and A/B test analysis using experimentation frameworks from Lenny's Podcast.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 330 installs · ★ 1434
- Author: RefoundAI
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/RefoundAI/lenny-skills/tree/HEAD/skills/product-experiments/SKILL.md
- Updated: 2026-07-16
- HTML: https://affpapa.org/en/nevibecode/skills/product-experiments

## What it does

This skill structures the full lifecycle of product experimentation using insights from nine guests and articles across Lenny's Podcast and Newsletter. It guides the definition of testable hypotheses, metric selection, and sample size calculations. To prevent short-term gains from hurting retention, it insists on defining guardrail metrics alongside primary revenue goals.

To detect real business impact, the workflow introduces long-term holdout groups running from three months to over a year. It includes checks for Sample Ratio Mismatch (SRM) and applies Twyman's Law to treat unexpected wins as potential tracking bugs. When changes involve low-risk industry best practices, the skill explicitly advises skipping the test to save engineering effort.

Failure is the baseline here. In optimized products like Bing or Airbnb, 80 percent to 92 percent of tests fail. Expecting these rates keeps teams from abandoning rigorous testing when initial variations do not move the needle.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill product-experiments -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/product-experiments/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/product-experiments/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill product-experiments -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/product-experiments/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill product-experiments -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/product-experiments/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install RefoundAI/lenny-skills product-experiments
```

2. **Manually: put the files in place**
- File: `.github/skills/product-experiments/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill product-experiments -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/RefoundAI/lenny-skills --path skills/product-experiments
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/product-experiments/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill product-experiments -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/RefoundAI/lenny-skills/tree/HEAD/skills/product-experiments/SKILL.md

## When to use it
- draft falsifiable hypotheses for product tests
- calculate required sample size for conversion funnels
- detect sample ratio mismatch across experiment variants
- set up multi-month holdout groups for incrementality
- evaluate whether to ship, iterate, or kill a feature

## Example prompts
- Help me calculate the sample size for a sign-up flow step converting at 10%.
- Check our experiment results for SRM and interpret the confidence intervals.
- What guardrail metrics should we track when changing the ad layout?

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE a0909d20ba208462
```text
---
name: product-experiments
description: Help users design, execute, and analyze product experiments to validate hypotheses and measure true incremental impact while avoiding common statistical pitfalls.
---

# Product Experimentation Excellence

Drive measurable growth and mitigate risk through rigorous A/B testing and data-driven learning.

Help the user with product experimentation excellence using insights from 9 guests and posts across Lenny's Podcast and Newsletter.

## How to Help

1. **Hypothesis Definition** - Guide the user in drafting clear, falsifiable hypotheses based on user behavior theories.
2. **Experimental Design** - Help determine the right metrics, sample sizes, and guardrail metrics for a clean test.
3. **Statistical Analysis** - Support the interpretation of p-values, confidence intervals, and potential sample ratio mismatches.
4. **Strategic Evaluation** - Assist in deciding whether to ship, iterate, or kill a feature based on experiment results and long-term business impact.

## Core Principles

### Use long-term holdouts for true incrementality
Archie Abrams: "So we constantly will relook at an experiment a year later, see that the way the GMV curve for the distribution was different than we might've originally thought. And that'll actually change what we do from that previous experiment. And so there's a lot of longterm monitoring of experiments over these very long time horizons to both inform what those input metrics are and more importantly hold ourselves accountable to, did we actually move what we cared about, which is that longterm GMV, in the right way?"

Implement holdout groups for one or more years to distinguish between immediate growth and short-term pull-forward effects. This ensures you are measuring the genuine downstream business impact of changes.

### Focus experiments on risk mitigation
Lauryn Isford: "So, with all that said, generally my advice is to experiment when you need to and to primarily see it as a risk mitigation tactic when you're making dramatic changes and to let the product development process do more work. So, spend more time with customers, be more rigorous in understanding precisely what problem you're solving, get mocks in front of people and see how they react, and hopefully have more conviction than you otherwise would when you ship something that it's okay if every customer sees it tomorrow and that the experiment doesn't actually matter as much."
```
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