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.

View source

How to use

  1. Copy the installation command and review the source package before installing it in your own agent environment.
  2. Check the source document for required tools, credentials and supported hosts.
  3. Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/agricidaniel/claude-ads --skill ads-test

Before you start

  • Installation does not include a model subscription, API allowance or permission to access your files.
  • Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document

Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.

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.

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