A/B Test Analyzer for Openclaw

An automated tool for analyzing experiment data, calculating statistical significance, and providing actionable growth recommendations.

yang1002378395-cmyk
v1.0.52
Mar 16, 2026
0
1.4k
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install ab-test-analyzer

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install ab-test-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is A/B Test Analyzer?

The A/B Test Analyzer is a specialized tool within the Openclaw Skills ecosystem designed to bridge the gap between raw data and actionable product insights. It empowers growth hackers, product managers, and digital marketers to conduct rigorous statistical analysis without needing a deep background in data science. By automating the calculation of confidence levels and conversion lifts, this skill ensures that your product decisions are backed by mathematical certainty rather than intuition.

Built for high-velocity experimentation, this skill simplifies the process of comparing different versions of landing pages, marketing copy, or UI elements. It helps teams avoid common pitfalls like premature stopping or misinterpreting random noise as significant results, making it a vital asset for anyone using Openclaw Skills to drive measurable growth.

A/B Test Analyzer Use Cases

  • Optimizing landing page conversion rates by identifying the highest-performing design variations.
  • Comparing email subject lines to determine which version triggers higher open and click-through rates.
  • Testing call-to-action (CTA) button copy and colors to maximize user engagement.
  • Validating product feature rollouts by analyzing user behavior metrics across different cohorts.

How A/B Test Analyzer Works

  1. Import your experiment data from a CSV file or direct input into the skill environment.
  2. Specify the core metric you wish to analyze, such as conversion rate or click-through rate.
  3. The skill performs statistical significance tests to determine if the variation results are mathematically valid.
  4. Review the generated comparison table which highlights visitors, conversions, and lift percentages.
  5. Export a detailed Markdown report containing the winning variation and suggested next steps for your optimization strategy.

A/B Test Analyzer Setup

To start using this analysis tool within your Openclaw Skills workflow, follow these steps:

# Import your test data for analysis
openclaw run ab-test-analyzer import test_data.csv

# Run the analysis on a specific metric
openclaw run ab-test-analyzer analyze --metric conversion

# Generate a summary report to a file
openclaw run ab-test-analyzer report --output report.md

A/B Test Analyzer Data Schema & Taxonomy

The skill processes and generates data according to the following schema:

Attribute Description
Version The identifier for the test group (e.g., Control vs. Variant).
Visitors The total number of users or sessions in the sample group.
Conversions The count of successful actions taken by users.
Conversion Rate The percentage of visitors who converted.
Lift The relative improvement of the variant over the control.
Confidence The statistical probability (p-value) that the results are significant.

A/B Test Analyzer Advanced Features

  • Multivariate testing support for analyzing more than two variations simultaneously.
  • Automated optimization suggestions including projected revenue impact and scaling advice.
  • Configurable confidence thresholds to align with specific business risk tolerances.
  • Integrated report generation in Markdown for easy sharing in documentation and project management tools.

SKILL.md


Loading

Related Openclaw Skills

METADATA

Github Stars: 0
forks: 0

Featured*