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analyze-experiments

Analyze Amplitude MCP experiments and return structured reports with lift, p-values, and confidence intervals.

30|8|Updated Jan 28, 2026
Search Tags:#A/B testing#data-driven#analyze#statistical analysis#experiments#Amplitude MCP
One-click install
npx skills add https://github.com/amplitude/mcp-marketplace --skill analyze-experiments
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyze-experiments
Source: https://github.com/amplitude/mcp-marketplace/tree/main/plugins/amplitude-analysis/skills/analyze-experiment
Command: npx skills add https://github.com/amplitude/mcp-marketplace --skill analyze-experiments

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables data teams and product leaders to perform thorough analysis of experiments, turning results into actionable decisions and clear next steps.

Core Features & Use Cases

  • Comprehensive experiment retrieval: gather setup, variants, metrics, and status across Amplitude MCP.
  • Statistical evaluation: compute primary metric lift, p-values, confidence intervals, power analysis, with clear business interpretation.
  • Segment and guardrail analysis: dissect results by platform, geography, and user segments; evaluate guardrails for unintended consequences.

Quick Start

To begin, provide an experiment URL or ID to analyze, or start with a search term to locate relevant experiments.

Frequently Asked Questions about analyze-experiments

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze A/B testing results from Amplitude experiments?▼

To analyze A/B testing results, provide an experiment URL or ID to retrieve setup, variants, and metrics, then compute lift, p-values, and confidence intervals for a structured decision-ready report.

Can I break down experiment metrics by user segments and guardrails?▼

Yes, experiment analysis can dissect results by platform, geography, and user segments, while evaluating guardrail metrics to identify any unintended consequences across your key business metrics.

Does statistical analysis for experiments include power analysis and data quality flags?▼

Yes, statistical evaluation includes power analysis and data quality flags alongside p-values and confidence intervals, ensuring your experiment results meet rigorous data-driven decision standards.

What is the best way to evaluate primary and secondary metrics for running experiments?▼

The best way to evaluate running experiments is applying comprehensive statistical analysis to primary, secondary, and guardrail metrics, generating actionable recommendations based on computed lift and significance.

How do I get actionable recommendations from completed Amplitude MCP experiments?▼

Completed Amplitude MCP experiments are transformed into actionable recommendations by analyzing lift, confidence intervals, and segment breakdowns, returning a structured report detailing clear next steps for product leaders.

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