---
name: lean-experiment-designer
description: "Converts an untested product or growth idea into a rigorous lean experiment design — riskiest-assumption identification, a falsifiable hypothesis, the minimum test to run, primary and guardrail metrics, sample size and duration estimates, decision thresholds, and a next-steps ladder. Use when a PM or growth manager describes an idea, feature, or optimization they haven't validated and needs a structured A/B test or lean experiment to test it before building."
argument-hint: "[idea] [context]"
allowed-tools: ["Read"]
---

# Lean Experiment Designer

## Purpose
Turn a vague product or growth idea into a decision-ready experiment: the specific assumption being tested, a falsifiable hypothesis, the smallest test that generates a valid signal, the metrics and thresholds that define a win, and what to do based on each outcome. Prevents the two most common failures — building on untested assumptions and running tests that can't actually produce a decision.

## When to Use
Invoke this skill when the user asks to:
- Design an A/B test for a feature, flow, or copy change
- Validate a new feature idea before committing engineering time
- Test a growth, onboarding, activation, or conversion hypothesis
- Figure out the cheapest way to de-risk an idea before building it
- Turn a "I think users want X" statement into a testable experiment
- Set up metrics, sample size, and success criteria for a planned test

## Inputs Required
Before starting, collect:
- **The idea**: What they want to build, change, or try — in their own words
- **The belief behind it**: Why they think it will work (the underlying assumption)
- **Current metric baseline**: The metric this is meant to move and its current value (e.g., activation rate 34%, trial-to-paid 18%)
- **Traffic/user volume**: How many users hit this surface per week or month
- **Constraints**: Engineering effort available, timeline, and any technical limits on what can be tested
- **Stage of confidence**: Is this a wild guess, an informed hunch, or something with supporting signal already?

If baseline metric or traffic volume is missing, estimate a realistic range from the context provided and flag the assumption — these directly affect the sample size and duration recommendation.

## Instructions

### Step 1: Identify the Riskiest Assumption
Extract the assumptions buried in the idea and rank them. The riskiest assumption is the one that, if false, kills the idea — and that you currently have the least evidence for.
- List 3–5 assumptions the idea depends on (demand, willingness to change behavior, technical feasibility, value perception)
- For each, mark **evidence level** (none / weak / moderate) and **impact if wrong** (low / medium / fatal)
- Select the single assumption with the highest impact and lowest evidence — this is what the experiment must test first
- State plainly: "The riskiest assumption is that [X]. If it's false, the idea doesn't work."

### Step 2: Write a Falsifiable Hypothesis
Reframe the idea as a hypothesis that can be proven wrong. Use the format:
> "We believe that [change] for [audience] will cause [measurable outcome], because [reasoning]. We'll know we're right if [specific metric] moves from [baseline] to [threshold] within [timeframe]."
- The outcome must be a number, not a feeling
- The hypothesis must be capable of failing — if any realistic result would still be interpreted as success, rewrite it

### Step 3: Choose the Minimum Viable Test
Match the test type to the riskiest assumption and the effort available — always favor the cheapest test that produces a valid signal.

- **Demand exists?** → Fake-door / painted-door test, landing page, or feature-interest CTA. No build required.
- **Behavior will change?** → A/B test on the live flow, or a concierge/manual version delivered to a small cohort.
- **Value is perceived?** → Wizard-of-Oz test (manual backend), or a prototype usability test with intent-to-pay questions.
- **Feasibility works?** → Technical spike or single-cell pilot with instrumentation.

State the chosen test type, why it fits this assumption, and the rough build effort (hours/days, not a full feature).

### Step 4: Define Metrics
- **Primary metric**: The single number the decision hangs on. One only.
- **Secondary metrics**: 1–2 metrics that add context or explain the "why"
- **Guardrail metrics**: 2–3 metrics that must NOT degrade (e.g., churn, support tickets, load time, downstream conversion). Define the tolerance for each.
- Confirm the primary metric is directly caused by the change and measurable within the test window.

### Step 5: Estimate Sample Size and Duration
- State the **minimum detectable effect (MDE)** worth acting on (e.g., a relative lift of 10%)
- Estimate required sample size per variant using the baseline conversion rate and MDE at 95% confidence and 80% power — provide a concrete number
- Divide by weekly traffic to get **estimated duration**
- Enforce a minimum runtime of one full business cycle (typically 1–2 weeks) to absorb weekday/weekend variance, even if sample size is reached sooner
- If traffic can't reach significance in a reasonable window, flag it and recommend a lower-effort qualitative test instead

### Step 6: Set Decision Thresholds and the Next-Steps Ladder
Define what happens at each outcome — no ambiguous results allowed:
- **Win** (primary metric hits threshold, guardrails hold): Ship, then specify the next experiment to compound the gain
- **Flat/inconclusive** (no significant movement): State whether to iterate on the variant, retest with a larger swing, or drop
- **Loss** (metric drops or guardrail breaks): State the learning and the pivot
- End with a one-line **kill criterion**: the result that means "stop pursuing this idea."

## Output Format
- **Structure**: Structured experiment brief with labeled sections in the order of the steps
- **Length**: 600–900 words
- **Format**: Structured document — short prose per section, hypothesis in blockquote, assumptions and thresholds as bullets/table
- **Sections**: Riskiest Assumption → Hypothesis → Minimum Viable Test → Metrics (primary/secondary/guardrail) → Sample Size & Duration → Decision Thresholds & Next Steps
- **Tone**: Product lead reviewing an experiment plan — pragmatic, decisive, allergic to tests that can't produce a decision

## Quality Checklist
Before delivering output, verify:
- [ ] Exactly one riskiest assumption is named with evidence level and impact rating
- [ ] The hypothesis includes a baseline number, a target threshold, and a timeframe — and can fail
- [ ] Exactly one primary metric is defined; guardrail metrics have explicit tolerances
- [ ] A concrete sample-size number and estimated duration are provided, tied to the stated baseline and MDE
- [ ] All three outcome branches (win / flat / loss) have specific actions, plus a kill criterion
- [ ] The chosen test is the cheapest option that still tests the riskiest assumption — not an over-built version

## Notes
- The most common mistake is testing the idea instead of testing its riskiest assumption — if the assumption is "people want this at all," a polished A/B test is wasteful; a fake-door test answers it in days.
- If traffic is too low for statistical significance, say so directly and shift to a qualitative or concierge test rather than running an underpowered A/B test that produces a false read.
- A hypothesis that can't produce a "loss" outcome is not a hypothesis — it's a launch plan in disguise. Push back and rewrite it.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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