An automated tool for analyzing experiment data, calculating statistical significance, and providing actionable growth recommendations.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ab-test-analyzer
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
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).
Get the raw skill files in a ZIP archive.
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.
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
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. |
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