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m-binimranm-binimran/marketing-pack/skills/ab-test/SKILL.md

ab-test

Skill1 starsChanged 5 months agoMITView on GitHub

Design an A/B test - hypothesis, single variable, variants, sample size/duration, and the metric - and interpret results honestly. Use when optimizing a page, ad, email, or funnel step.

6 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Test the high-leverage thing, change one variable, and read the result honestly.

    line 8
  2. Very specific:

    Don't claim significance without the data (truth-telling).

    line 24

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

ab-test

Test the high-leverage thing, change one variable, and read the result honestly.

Process

  1. Hypothesis: "Because [insight], changing [element] will [effect] on [metric]." Test the high-leverage element first (headline, offer, CTA, hero) - not the button colour.
  2. One variable between A and B; everything else equal.
  3. Metric & guardrail: the primary conversion metric, plus a guardrail metric you don't want to harm.
  4. Sample size & duration: estimate the needed sample for a meaningful result; run full weeks; don't peek and stop early.
  5. Interpret honestly: report sample size and whether the difference is actually significant - small or short tests often aren't. No declaring a winner from noise.

Output

  • A test plan: hypothesis, variants, primary + guardrail metric, sample/duration, and a results-reading rule.

Guardrails

  • Don't claim significance without the data (truth-telling); report sample size + result honestly.
  • One variable; avoid stopping early on a lucky swing.

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