Google ads ab testing

Skill kastriasani/google-ads-skills/skills/google-ads-ab-testing

13 Claude Code Agent Skills for Google Ads (research, planning, build, optimization, reporting) with a premium visual dashboard and a live example per skill.

Install

npx -y skills add kastriasani/google-ads-skills --skill google-ads-ab-testing

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Design valid Google Ads experiments (RSA ad copy, landing page, bid strategy) and read them for statistical significance. Use this whenever someone wants to set up or plan a Google Ads experiment or A/B test, run a campaign experiment or draft, test an RSA or ad variation, test a landing page, compare two bid strategies or tROAS/tCPA targets, work out the sample size or how long a test must run, check whether a result is statistically significant, compute observed lift and a p-value, or decide whether a test can be called or was stopped too early. Triggers include A/B test, experiment, split test, campaign experiment, draft and experiment, ad variation test, RSA test, landing page test, bid strategy test, sample size, test duration, statistical significance, p-value, confidence, MDE, minimum detectable effect, and lift. Runs standalone on pasted numbers; optional Google Ads API and GA4 sharpen it.

SKILL.md

5.3 KB, as published. Nobody here has run it

Google Ads A/B Testing

Design experiments that can actually answer a question, then read them honestly. The two ways tests go wrong are bad design (changing more than one thing, unfair split) and bad statistics (too small, called early). This skill closes both: it sizes the experiment before launch, gives the setup checklist, and computes significance from the results.

When to use

Reach for this skill when the user wants to:

Plan or set up a Google Ads experiment - RSA/ad-variation, landing page, or bid strategy - using drafts and experiments.
Work out the required sample size and how long the test must run.
Check whether a finished test is statistically significant and what the lift is.
Decide whether a test can be called or was stopped too early (peeking).

If the request is to choose a bid strategy in the first place, use google-ads-bidding. If it is to forecast results, use google-ads-forecast.

Workflow

Frame the test. Pin down the single variable being changed (one only), the primary metric (usually conversion rate), the guardrails that must not get worse (CPA, conversion value), and the baseline conversion rate of the control. See references/experiment-design.md for what is worth testing and in what order.
Size it before launch. Run scripts/experiment_planner.py --baseline 0.05 --mde 0.15 --daily-clicks 600 (or --demo). It returns clicks needed per arm, the estimated duration, and a drafts-and-experiments setup checklist (50/50 split, hold constant, one-variable rule, minimum one week, pre-committed stop rule). Use scripts/sample_size_calculator.py for a quick sample/duration with a design-quality score across multiple scenarios.
Sanity-check feasibility. If the required sample needs more than ~8 weeks at the campaign's click volume, raise the MDE, reduce variants, or test higher in the funnel. A test that cannot reach significance should not be launched - say so. references/significance.md has the sample-by-baseline table and the alternatives.
Set it up correctly. Use Google Ads campaign experiments (from a draft), not two live campaigns. Change one variable, split 50/50, hold budget/targeting/ schedule constant, and exclude the Smart Bidding learning window for bid-strategy tests.
Read the result once, at the planned sample. Run scripts/experiment_planner.py --read --control-clicks 12000 --control-conv 600 --variant-clicks 12000 --variant-conv 690 --needed-per-arm 14190. It reports observed CVRs, absolute and relative lift, the z-test p-value, whether it is significant, a WINNER/LOSER/INCONCLUSIVE verdict, and a warning if the sample is below what the test needed (called early).
Decide and document. Apply the verdict, check practical significance and the guardrails, and record the learning. Do not peek-and-stop; honour the committed sample and minimum run length.

Inputs and data sources

Runs standalone: paste the baseline rate and the arm-level clicks/conversions, or hand the scripts the numbers directly. Each script has a --demo mode and a --json flag for piping into a report or sheet.

Optional sources that improve accuracy:

Google Ads API / UI - baseline conversion rate, daily clicks, and the experiment arms' clicks and conversions; experiment status and learning state.
GA4 - downstream conversion and value validation, segment-level conversion rates for segment-powered tests.
Google Sheets / BigQuery - log the hypothesis bank and results, and pipe --json output into a test repository.

Outputs

Clicks needed per arm and estimated test duration.
A drafts-and-experiments setup checklist (split, hold-constant, one-variable rule, stop rule).
A design-quality score flagging under-powered or impractically long tests.
Observed absolute and relative lift, p-value, and a significance verdict.
An early-call warning when the observed sample is below the planned sample.

References

references/experiment-design.md - Google Ads experiments setup, what to test, the one-variable rule, hypothesis format, split and duration.
references/significance.md - sample size, duration, p-value and confidence, and the common pitfalls (peeking, under-powering, wrong baseline, learning period).