Ab Test Readout

CommunityPopular

mohitagw15856

ab-test-readout

Analyse a finished A/B test and write the readout — the result, whether it's statistically and practically significant, what it means, and the ship/no-ship call. Use when asked to analyse experiment results, write an A/B test readout, interpret test data, or decide whether to ship a variant. Produces a clear verdict with the lift and confidence, segment cuts, the risks (peeking, novelty, sample), and a recommendation. Distinct from planning a test — this reads results.

Overview

Publisher	mohitagw15856
Repository	`pm-claude-skills`
Skill name	`ab-test-readout`
Stars	1.4K
Forks	240
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by mohitagw15856 on GitHub. Read the source before you install it.

Installation

Install the Ab Test Readout 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.
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/mohitagw15856/pm-claude-skills/tree/main/exports/openclaw/ab-test-readout

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/mohitagw15856/pm-claude-skills.git /tmp/pm-claude-skills
mkdir -p .claude/skills
cp -r /tmp/pm-claude-skills/exports/openclaw/ab-test-readout .claude/skills/ab-test-readout

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

Use it in TypingMind

Enable Ab Test Readout 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 Readout 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 Readout 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 Readout Skill

The hard part of an experiment is the readout: not "B won" but "is this real, is it big enough to matter, and should we ship?" This skill turns results into an honest decision — and flags the ways A/B results lie.

Working from a brief

Given results (even partial), write the full readout anyway. If significance isn't provided, reason about it from the numbers and flag what's needed to confirm. Mark assumed figures. Never declare a winner without addressing significance and sample.

Required Inputs

Ask for (if not already provided):

The hypothesis and the primary metric
Results — control vs variant: conversions/rate, sample size per arm, duration
Guardrail metrics (revenue, retention, latency, complaints) that mustn't regress
Pre-registered decision rule (what would count as a win) if one exists

Output Format

1. Verdict (one line)

Ship / Don't ship / Inconclusive — keep running — with the headline number.

2. The result

Metric	Control	Variant	Relative lift	Significant?
Primary				p / CI
Guardrail(s)

State statistical significance (p-value / confidence interval) and practical significance (is the lift big enough to matter given the cost?).

3. Did it really win?

Address the ways A/B tests mislead:

Sample / power — was the test adequately powered, or under-sampled?
Peeking — was the call made early, inflating false positives?
Novelty / primacy — could the effect fade?
Segments — does the win hold across key segments, or is it driven by one?

4. Segment cuts

Where the effect is strong vs flat vs negative (new vs returning, platform, geography).

5. Recommendation & next step

Ship / iterate / re-run, plus what to monitor post-launch or what the follow-up test should isolate.

Quality Checks

Distinguishes statistical from practical significance
Checks guardrail metrics, not just the primary
Flags peeking, power, novelty, and segment-driven wins
Recommendation follows from the evidence, with a monitoring/next-test step
Doesn't declare a winner on an underpowered or peeked result

Anti-Patterns

"B won by 8%!" with no significance or sample size
Calling a result early (peeking) and shipping
Ignoring a guardrail regression because the primary went up
A statistically significant but practically meaningless lift treated as a win

Frequently asked questions

What does the Ab Test Readout AI skill do?

Why use Ab Test Readout on TypingMind?

Because you install it once and use it with any model. Ab Test Readout 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 Readout in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mohitagw15856/pm-claude-skills/tree/main/exports/openclaw/ab-test-readout. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ab Test Readout?

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 Readout?

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

Is the Ab Test Readout AI skill free?

Yes. It is published on GitHub by mohitagw15856 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 Readout Skill

Working from a brief

Required Inputs

Output Format

1. Verdict (one line)

2. The result

3. Did it really win?

4. Segment cuts

5. Recommendation & next step

Quality Checks

Anti-Patterns

Frequently asked questions

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