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.

ソースを見る

使い方

  1. インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
  2. 必要なツール、認証情報、対応環境を原文で確認します。
  3. 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/agricidaniel/claude-ads --skill ads-test

開始前の確認

  • 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
  • スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文

見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。

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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