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ads-test

Design and evaluate paid-ad experiments with hypotheses, randomization units, sample-size and duration assumptions, guardrails, platform experiment tools, analysis, and decision rules. Use for A/B test, split test, experiment design, hypothesis, statistical significance, sample size, test duration, or experiment readout.

Published by AgriciDaniel ↗AgriciDaniel/claude-ads

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ads-test

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Use ads-test to design a paid-ad experiment for [decision and causal hypothesis] in [target population], with [primary metric], treatment and control, randomization unit, sample-size and duration assumptions, guardrails, stopping rule, and a versioned JSON setup plus plain-language decision rule.

Required inputs

  • [decision and causal hypothesis]
  • [target population]
  • [primary metric]

User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗

SKILL.md

name: ads-testdescription: "Design and evaluate paid-ad experiments with hypotheses, randomization units, sample-size and duration assumptions, guardrails, platform experiment tools, analysis, and decision rules. Use for A/B test, split test, experiment design, hypothesis, statistical significance, sample size, test duration, or experiment readout."

Paid Media Experiment

  1. State the decision, causal hypothesis, treatment, control, randomization unit, population, primary metric, guardrails, minimum effect, and stopping rule.
  2. Check platform constraints, overlapping experiments, conversion lag, seasonality, interference, and measurement quality.
  3. Calculate sample and duration from declared assumptions; disclose approximations.
  4. Change one decision surface unless the design explicitly estimates interactions.
  5. Pre-register exclusions, quality checks, analysis, and decision thresholds.
  6. For readout, verify assignment integrity and data completeness before estimating effect and uncertainty.
  7. Return setup or readout in versioned JSON with a plain-language decision.

Do not repeatedly peek and stop on a favorable result, call underpowered noise a winner, or generalize beyond the tested population.