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Experimentation Agent Skill

Designs, runs, and reads A/B tests and growth experiments — hypothesis, sample size, duration, and honest interpretation. Use this to plan a test, judge whether a result is real, build an experimentation program, decide what to test next, or diagnose why tests keep producing inconclusive or non-replicating results.

615 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
220
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/cbrock84/headcount --skill experimentation

The instruction itself

5 sections, as written by the author

Experimentation

Most A/B testing programs produce confident conclusions from insufficient data. The discipline is

almost entirely in what you do before launch.

Before running

  • Hypothesis with a mechanism. "Moving the pricing table above the fold will raise trial starts,

because visitors currently leave before seeing pricing." Not "let's try a green button."

  • One primary metric, chosen in advance. Secondary metrics are context, never the verdict.
  • Sample size calculated in advance, from your baseline rate and the smallest lift that would

change a decision. If the required sample is unreachable, do not run the test — decide by judgment

and say so.

  • Duration set in advance, covering at least one full weekly cycle, and two if the buying cycle

is long.

  • Guardrail metrics that would make you reject a win: refunds, support volume, downstream

retention.

While running

Do not look at results and act on them mid-flight. Peeking and stopping at significance is the

single most common way to generate false positives, and it is very effective at it.

Check only that the test is running correctly — even split, no broken variant, tracking firing.

Reading

  • At the pre-set duration, not before, and not extended because it is nearly significant.

Extending until significance manufactures it.

  • Significance is not size. A statistically significant 0.3% lift may not be worth shipping.
  • Inconclusive is a real result and the most common one. It means the change did not matter

enough to detect, which is useful.

  • Check the guardrails before declaring a win.
  • Segment afterward for hypotheses only, never for verdicts. Slice enough ways and something is

always significant.

Program level

Test where the traffic and the leverage are. Most sites can only run a handful of adequately powered

tests a year — spend them on structural questions, not button colors.

Keep a log of every test: hypothesis, result, decision. Without it, teams re-run the same tests every

eighteen months and re-learn the same things.

How to use it

Copy the folder

Take cbrock84/experimentation from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.