Ab Test Analysis

Community

nimrodfisher

ab-test-analysis

Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.

Overview

Publisher	nimrodfisher
Repository	`data-analytics-skills`
Skill name	`ab-test-analysis`
Stars	493
Forks	87
Bundled files	3
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by nimrodfisher on GitHub. Read the source before you install it.

Installation

Install the Ab Test Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/ab-test-analysis
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/ab-test-analysis

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git /tmp/data-analytics-skills
mkdir -p .claude/skills
cp -r /tmp/data-analytics-skills/03-data-analysis-investigation/ab-test-analysis .claude/skills/ab-test-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ab Test Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ab Test Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ab Test Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

A/B Test Analysis

When to use

An experiment has finished and the team needs a ship / no-ship recommendation
Results look directionally positive but the team is unsure if they're statistically significant
A test has been running for weeks without a clear winner and someone needs to decide whether to continue
A new experiment needs sample-size planning before launch
Results are disputed and need a rigorous, documented analysis

Process

Confirm test design — verify the hypothesis, the control and treatment definitions, the randomisation unit (user/session/device), the primary metric, any guardrail metrics, and the target split ratio.
Check for sample ratio mismatch (SRM) — run a chi-square test on the actual vs. expected split. If SRM is detected, stop and investigate the randomisation pipeline before interpreting results. Use scripts/ab_test_analyzer.py --check-srm.
Calculate per-variant metrics — compute the rate (or mean) and 95% confidence interval for the primary metric in each variant. Document absolute and relative difference.
Run the significance test — execute a two-proportion z-test (for rates) or Welch's t-test (for means). Record z-score, p-value, and 95% CI for the effect. Use references/statistical_tests_reference.md if unsure which test applies.
Check guardrail metrics — run the same significance test for each guardrail metric. A significant degradation on any guardrail is a blocker regardless of primary metric results.
Produce the recommendation — synthesise SRM result, power, significance, and guardrail checks into a clear ship / no-ship / extend decision. Quantify the expected business impact if shipped. Record in assets/ab_test_report_template.md.

Inputs the skill needs

Test plan or hypothesis document (variant definitions, randomisation unit, primary metric)
Data with at minimum: user_id, variant assignment, primary metric outcome
Optional: guardrail metric values per user, daily aggregate data for temporal validity checks
Target split ratio (e.g., 50/50)
Minimum detectable effect or business threshold for "worth shipping"

Output

scripts/ab_test_analyzer.py — runs SRM check, significance test, power analysis, and guardrail checks from a CSV or summary stats input
references/statistical_tests_reference.md — which test to use and when
references/ab_test_design_guide.md — SRM causes, power planning, peeking and multiple testing
assets/ab_test_report_template.md — structured report: design, results, checks, recommendation, expected impact

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ab Test Analysis AI skill do?

Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.

Why use Ab Test Analysis on TypingMind?

Because you install it once and use it with any model. Ab Test Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ab Test Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/ab-test-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ab Test Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ab Test Analysis?

As many as you like. As long as a model supports skills, you can use Ab Test Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ab Test Analysis AI skill free?

Yes. It is published on GitHub by nimrodfisher under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

A/B Test Analysis

When to use

Process

Inputs the skill needs

Output

Bundled files

Frequently asked questions

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