AutoGineer
Generated banner for Ab Test Analysis
Generated logo for Ab Test Analysis

Ab Test Analysis

Pawel HurynCommunityCreated by Pawel Huryn

  • Skill
  • Research and analysis
  • Data and AI
  • MIT
  • No issues found
Download ZIP

From the author

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
— the description by Pawel Huryn

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured framework for evaluating A/B test performance. It guides users through validating experiment setups, calculating statistical significance, checking guardrail metrics, and interpreting results to make data-driven product decisions. The skill supports both manual data entry and automated analysis via Python scripts for raw data files.

What it does

  • Validates sample sizes and test duration to ensure statistical power.
  • Calculates conversion rates, relative lift, p-values, and confidence intervals.
  • Detects sample ratio mismatches and potential novelty effects.
  • Evaluates guardrail metrics to identify negative trade-offs.
  • Provides actionable recommendations based on statistical and practical significance.
  • Generates structured summary reports for experiment stakeholders.

When to use it

  • When you need to determine if an A/B test result is statistically significant.
  • When deciding whether to ship a new feature variant based on experiment data.
  • When investigating why an experiment failed to reach significance.
  • When you need to validate if your test setup is robust enough to draw conclusions.

Advantages

  • Ensures product decisions are backed by statistical evidence.
  • Standardizes the reporting format for experiment results.
  • Helps prevent premature shipping or stopping of tests.
  • Identifies hidden risks through guardrail metric monitoring.

Usage examples

“Analyze this CSV file of A/B test results for our checkout page.”
The agent reads the file, calculates the p-value and lift, checks for guardrail degradation, and provides a recommendation to ship or extend the test.
“Our test shows a 2% lift but the p-value is 0.08. Should we ship?”
The agent explains that the result is not statistically significant at the 0.05 level and recommends extending the test to gather more data.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/phuryn/ab-test-analysis/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip ab-test-analysis.zip -d .agents/skills

Documentation by Pawel Huryn

A/B Test Analysis

Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.

Context

You are analyzing A/B test results for $ARGUMENTS.

If the user provides data files (CSV, Excel, or analytics exports), read and analyze them directly. Generate Python scripts for statistical calculations when needed.

Instructions

  1. Understand the experiment:

    • What was the hypothesis?

    • What was changed (the variant)?

    • What is the primary metric? Any guardrail metrics?

    • How long did the test run?

    • What is the traffic split?

  2. Validate the test setup:

    • Sample size: Is the sample large enough for the expected effect size?

      • Use the formula: n = (Z²α/2 × 2 × p × (1-p)) / MDE²

      • Flag if the test is underpowered (<80% power)

    • Duration: Did the test run for at least 1-2 full business cycles?

    • Randomization: Any evidence of sample ratio mismatch (SRM)?

    • Novelty/primacy effects: Was there enough time to wash out initial behavior changes?

  3. Calculate statistical significance:

    • Conversion rate for control and variant

    • Relative lift: (variant - control) / control × 100

    • p-value: Using a two-tailed z-test or chi-squared test

    • Confidence interval: 95% CI for the difference

    • Statistical significance: Is p < 0.05?

    • Practical significance: Is the lift meaningful for the business?

    If the user provides raw data, generate and run a Python script to calculate these.

  4. Check guardrail metrics:

    • Did any guardrail metrics (revenue, engagement, page load time) degrade?

    • A winning primary metric with degraded guardrails may not be a true win

  5. Interpret results:

    OutcomeRecommendation
    Significant positive lift, no guardrail issuesShip it — roll out to 100%
    Significant positive lift, guardrail concernsInvestigate — understand trade-offs before shipping
    Not significant, positive trendExtend the test — need more data or larger effect
    Not significant, flatStop the test — no meaningful difference detected
    Significant negative liftDon't ship — revert to control, analyze why

  6. Provide the analysis summary:

    ## A/B Test Results: [Test Name]

**Hypothesis**: [What we expected]
**Duration**: [X days] | **Sample**: [N control / M variant]

| Metric | Control | Variant | Lift | p-value | Significant? |
|---|---|---|---|---|---|
| [Primary] | X% | Y% | +Z% | 0.0X | Yes/No |
| [Guardrail] | ... | ... | ... | ... | ... |

**Recommendation**: [Ship / Extend / Stop / Investigate]
**Reasoning**: [Why]
**Next steps**: [What to do]

Think step by step. Save as markdown. Generate Python scripts for calculations if raw data is provided.

Further Reading

Files

LICENSE

MIT License

Copyright (c) 2026 Pawel Huryn

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Pawel Huryn
Publisher
Pawel Huryn (Community)
Source
phuryn/pm-skills · pm-data-analytics/skills/ab-test-analysis · commit 2a12295
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Pawel Huryn.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download ab-test-analysis.zip