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Ads test

Skill AgriciDaniel/claude-ads/skills/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.From its SKILL.md

Install
npx -y skills add AgriciDaniel/claude-ads --skill ads-test

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

SKILL.md

1.2 KB, 167 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most test skills give in 167 tokens

Counted across 1,201 of the 2,096 authors here whose files we hold, read 2026-09-06

  • Write a failing test before writing codein 43 of 1201, across 36 files
  • Run the full test suitein 36 of 1201, across 35 files
  • Test only one variable per experimentin 34 of 1201, across 17 files
  • Read product marketing context before asking questionsin 34 of 1201, across 14 files
  • Mock external dependenciesin 34 of 1201, across 30 files
  • Define primary, secondary, and guardrail metricsin 33 of 1201, across 16 files
  • Pre-determine sample size before startingin 31 of 1201, across 14 files
  • Test behavior rather than implementationin 31 of 1201, across 29 files
  • Formulate a hypothesis before designing a testin 30 of 1201, across 13 files
  • Document every test hypothesis, variant, and resultin 29 of 1201, across 11 files
  • Use descriptive test function namesin 25 of 1201, across 21 files
  • Commit to the methodology without stopping earlyin 24 of 1201, across 8 files

Said here and by no other author read

  • State hypothesis and experiment parameters
  • Check platform constraints and measurement quality
  • Change only one decision surface
  • Pre-register analysis and decision thresholds
  • Verify assignment integrity before readout
  • Return results in versioned JSON

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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