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Ab Testing Analyzer

Checked
liangdabiao·vlatest·testing

Analyzes A/B test results, identifying statistically significant variations and recommending optimal campaign configurations.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add liangdabiao/claude-data-analysis-ultra-main --skill ab-testing-analyzer -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

liangdabiao

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned liangdabiao/claude-data-analysis-ultra-main at 6b52856848.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
6b52856848
Manifest
.claude/skills/ab-testing-analyzer/SKILL.md
Scanned
11 files, 3,389 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The ab-testing-analyzer skill analyzes A/B testing data to determine statistical significance and provide actionable insights. It can calculate p-values, confidence intervals, and effect sizes based on provided test metrics. This allows for data-driven decisions about which variations perform best and whether to roll out changes.

When to use it

  • You have run an A/B test on a website or application feature and need to determine if the results are statistically significant.
  • You want to understand the effect size of different variations in your A/B test, beyond just p-values.
  • You're evaluating multiple A/B tests simultaneously and require a consolidated analysis for prioritization.
  • You need to quickly assess whether an A/B test provides enough evidence to justify a change.

Key capabilities

  • Calculates p-values to determine statistical significance.
  • Generates confidence intervals for estimated metrics.
  • Computes effect sizes to quantify the magnitude of differences between variations.
  • Analyzes data from various A/B testing platforms (specific platform support not detailed).

Example prompts

  • "Analyze this A/B test data: [paste data here]. What is the p-value for variation B compared to control?"
  • "Calculate the confidence interval and effect size for these A/B test results: [paste data here]."
  • "I have data from three different A/B tests. Can you summarize the statistical significance of each?"

Tips & gotchas

  • The skill requires properly formatted A/B testing data as input, typically in a tabular format (e.g., CSV). Ensure your data includes metrics like conversions, click-through rates, or revenue per user for each variation.