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design-experiment

Generate experiment specifications with hypotheses, metrics, and power calculations.

21|11|Updated Mar 15, 2026
Search Tags:#design#statistics#experiment#sample size#power analysis#experimental design#a/b test
One-click install
npx skills add https://github.com/ai-analyst-lab/ai-analyst-plugin --skill design-experiment-ai-analyst-lab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: design-experiment
Source: https://github.com/ai-analyst-lab/ai-analyst-plugin/tree/main/skills/design-experiment
Command: npx skills add https://github.com/ai-analyst-lab/ai-analyst-plugin --skill design-experiment-ai-analyst-lab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design teams and product teams often struggle to validate ideas before building. This skill provides a structured approach to create rigorous experiments with clear hypotheses, success metrics, and power calculations, reducing risk of false positives and wasted effort.

Core Features & Use Cases

  • Structured Experiment Design: generates a complete plan including hypotheses, primary/secondary metrics, and power analysis for various experimental designs (A/B, multivariate, quasi-experiments).
  • End-to-End Specification: outputs a production-ready spec with design details, analysis plan, guardrails, and implementation checklist.
  • Use Case Coverage: applies to feature launches, pricing changes, messaging experiments, and UX variants across product lines.

Quick Start

Provide a brief feature description and request a complete experiment design.

Frequently Asked Questions about design-experiment

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

FAQPage Schema
How do I calculate sample size and power analysis for an A/B test?▼

Calculate sample size and power analysis by inputting baseline metrics, uplift targets, and constraints into an experiment design generator to produce a validated A/B test specification with risk guardrails.

What is the best way to structure a hypothesis and metrics for a multivariate test?▼

Structure multivariate test hypotheses and metrics by providing a brief feature description to generate an end-to-end experimental design specification, including primary and secondary metrics with an analysis plan.

Can I use experimental design for quasi-experiments and pricing changes?▼

Yes, experimental design supports quasi-experiments, pricing changes, messaging experiments, and UX variants by generating a production-ready spec tailored to the specific brief and timeline constraints.

How do I create an A/B test implementation checklist and analysis plan?▼

Create an A/B test implementation checklist and analysis plan by submitting your baseline metrics and uplift targets to output a complete experiment specification with design details and guardrails.

When do I need a structured experiment specification before launching a feature?▼

You need a structured experiment specification before launching a feature to validate ideas, establish clear success metrics, and perform power calculations, reducing the risk of false positives and wasted effort.

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