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.

查看源码

使用方法

  1. 复制安装命令,在自己的智能体环境安装前先检查源码包。
  2. 从原始文档核对所需工具、凭据和支持的宿主。
  3. 说明输入材料与期望交付物,并对照来源检查输出。
npx skills add https://github.com/agricidaniel/claude-ads --skill ads-test

开始前需了解

  • 安装不包含模型订阅、API 额度或访问文件的权限。
  • 仓库星数和安装量反映活跃情况,不等于独立质量或安全测试。
仓库原始内容

按源仓库内容呈现,保留标题、案例、代码、表格、链接以及原文引用的演示图片。

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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