Individual skill

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.

skills/ads-test/SKILL.md

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Repository stars (not skill ratings): 10,848
Repository last updated: Nov 1, 2026
License: MIT

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claude-code Repository topic
claude-code-skill Repository topic
ai-marketing Repository topic
ai Repository topic
marketing-automation Repository topic
open-source Repository topic
agent-skills Repository topic
google-ads Repository topic
meta-ads Repository topic
paid-advertising Repository topic
design Derived category
testing Derived category

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# 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