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ads-test

Convert proposed ad changes into measurable A/B test hypotheses and execution plans.

1|Updated Apr 13, 2026
Search Tags:#experiment design#sample size#ab testing#statistical significance#paid ads#test duration#meta experiments
One-click install
npx skills add https://github.com/yahav123147/paid-ads-cro-skills --skill ads-test-yahav123147
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ads-test
Source: https://github.com/yahav123147/paid-ads-cro-skills/tree/main/skills/ads-test
Command: npx skills add https://github.com/yahav123147/paid-ads-cro-skills --skill ads-test-yahav123147

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents wasted ad spend by turning unclear A/B testing ideas into a measurable experiment plan with credible sample size, duration, and success criteria.

Core Features & Use Cases

  • Structured hypothesis planning: Builds a test-ready IF/THEN hypothesis that ties a specific change to a defined metric and rationale.
  • Statistical planning: Estimates required sample size using confidence and power assumptions, plus a practical minimum detectable effect (MDE) setup.
  • Duration forecasting: Computes an estimated test duration from expected traffic and provides guardrails to avoid premature conclusions.
  • Platform-specific setup guidance: Provides step-by-step recommendations for Meta Experiments, Google Experiments, LinkedIn A/B, and TikTok split testing.
  • Use cases: Ideal when users want to test creative, audience, landing page, bidding strategy, offer structure, or other funnel-impacting variables in paid campaigns.

Quick Start

Plan an A/B test for my Meta and Google ads by stating my hypothesis, primary KPI, baseline conversion rate, expected effect size (MDE), daily traffic, and then produce the sample size, recommended duration, and experiment setup steps.

Frequently Asked Questions about ads-test

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate sample size and test duration for paid ad A/B testing?▼

A/B testing sample size and test duration are calculated using your baseline conversion rate, minimum detectable effect, daily traffic, and statistical power assumptions. This planning prevents wasted ad spend by ensuring tests run long enough to reach statistical significance.

How do I structure a measurable hypothesis for Meta or Google ad experiments?▼

A structured A/B testing hypothesis uses an IF/THEN format that ties a specific creative or audience change to a defined primary KPI and rationale. This converts unclear testing ideas into a measurable experiment plan with explicit success criteria.

Does this A/B testing approach work for LinkedIn and TikTok split testing?▼

Yes, this A/B testing approach provides platform-specific setup guidance for Meta Experiments, Google Experiments, LinkedIn A/B, and TikTok split testing. It supports creative, audience, bidding, and landing page experiments across these ad platforms.

What is statistical significance and minimum detectable effect in ad testing?▼

Statistical significance in A/B testing confirms results are not due to random chance, while the minimum detectable effect (MDE) is the smallest improvement worth detecting. Both are required inputs for estimating sample size and test duration.

Why does my A/B test show inconsistent results across different ad platforms?▼

Inconsistent A/B testing results often occur when tests are concluded prematurely without reaching the required sample size or duration. Proper experiment design applies statistical guardrails and platform-specific setup rules to prevent unreliable conclusions.

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