agricidaniel/claude-ads

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.

All-time #4460 Trending #4424 First seen Apr 13, 2026
8-week activity · all time api

Installation

$ npx skills add agricidaniel/claude-ads --skill ads-test

Summary

  • 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.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 9.1K
License LICENSE
Default branch main
Open issues 19
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,245 B
  • docs SUMMARY.md 1,211 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2,915 installs

SKILL.md

Paid Media Experiment

  1. State the decision, causal hypothesis, treatment, control, randomization unit,

population, primary metric, guardrails, minimum effect, and stopping rule.

  1. Check platform constraints, overlapping experiments, conversion lag, seasonality,

interference, and measurement quality.

  1. Calculate sample and duration from declared assumptions; disclose approximations.
  2. Change one decision surface unless the design explicitly estimates interactions.
  3. Pre-register exclusions, quality checks, analysis, and decision thresholds.
  4. For readout, verify assignment integrity and data completeness before estimating

effect and uncertainty.

  1. 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.