Plan paid advertising A/B tests with hypothesis frameworks and platform-specific setup.
npx skills add https://github.com/scanbott/claude-skills --skill ads-test
Please help me install this Agent Skill. Skill: ads-test Source: https://github.com/scanbott/claude-skills/tree/main/ads-test Command: npx skills add https://github.com/scanbott/claude-skills --skill ads-test
Paid advertising experiments can be inconsistent and slow; this Skill provides a structured framework to design, size, and compare ad variants across major platforms.
Provide a complete A/B test plan including hypothesis, design, sample size, and platform setup for an ad campaign.
High-intent search queries and answers about installing and using this skill.
Calculate paid ad A/B testing sample size and test duration by estimating the required audience reach and statistical confidence needed to compare ad variants reliably. This framework provides structured estimations to ensure your experiments reach significance.
Set up A/B tests on Meta, Google, and LinkedIn by following platform-specific deployment steps for creative, audience, bidding, and landing page variations. This provides guides for Meta Experiments, Google Experiments, and LinkedIn A/B testing.
A structured hypothesis framework for advertising A/B tests defines the specific creative, audience, bidding, or landing page variations being compared and sets clear success criteria. It ensures statistical rigor and consistent comparison across ad platforms.
Yes, you can test landing page variations across ad platforms. The framework addresses creative, audience, bidding, and landing page variations, providing platform-specific setup guidance to compare these elements across Meta, Google, and LinkedIn.
Reliable paid advertising experiments require sample size estimation, calculated test duration, and defined success criteria. This framework implements statistical rigor to ensure your A/B test comparisons yield confident and consistent results.
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