Ab Test Designer
Design robust A/B test experiments. Use when testing a new feature, validating a hypothesis, or optimizing conversion rates.
- Skill ID
- pmprompt/claude-plugin-product-management/ab-test-designer
- Publisher
- pmprompt
- Repository
- claude-plugin-product-management
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
pmprompt/claude-plugin-product-management/ab-test-designerInstalls these files- SKILL.md
What this skill tells the agent
Domain Context
This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.
Input Requirements
- Context about your product, feature, or problem
- Relevant data, research, or constraints (recommended but optional)
- Clear articulation of what you're trying to achieve
A/B Test Designer
When to Use
- Testing a new feature or design variation
- Validating a hypothesis before full rollout
- Optimizing conversion rates or key metrics
- Choosing between multiple design approaches
- Need to make a data-driven decision on a change
What This Skill Does
Helps you design rigorous A/B tests with clear hypotheses, success metrics, sample size calculations, and analysis plans.
Instructions
Help me design an A/B test for [feature/change]. Include:
- Hypothesis
- Current situation and metrics
- Proposed change
- Expected impact and why
- Test Design
- Primary success metric
- Secondary metrics
- Sample size needed
- Test duration
- User segments to include/exclude
- Variants
- Control (A): current experience
- Variant (B): new experience
- Any additional variants (C, D, etc.)
- Risks and Controls
- Potential negative impacts
- Guardrail metrics
- When to stop the test early
- Analysis Plan
- Statistical significance threshold
- How to handle edge cases
- Decision criteria
Feature context: [Add context about the change you want to test]
Best Practices
- Start with a clear, falsifiable hypothesis
- Choose one primary metric to avoid multiple comparison issues
- Calculate sample size upfront based on expected effect size
- Run tests for full weekly cycles to account for day-of-week effects
- Set a minimum test duration (usually 1-2 weeks)
- Define success criteria before running the test
- Monitor guardrail metrics (revenue, errors, performance)
Example
Input: Testing new onboarding flow vs current 3-step process Output: Hypothesis (new 1-step flow will increase co...
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