experiment-design

Name: experiment-design
Availability: InStock
Author: clawpod-app

Design growth experiments with hypotheses, ICE scoring, and success criteria.

46|11|Updated Mar 29, 2026

Search Tags:#design #metrics #growth #a/b testing #experiments #hypotheses #ice scoring

Ready to run

npx skills add https://github.com/clawpod-app/awesome-openclaw-agent-packs --skill experiment-design-clawpod-app

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-design
Source: https://github.com/clawpod-app/awesome-openclaw-agent-packs/tree/main/packs/growth/skills/experiment-design
Command: npx skills add https://github.com/clawpod-app/awesome-openclaw-agent-packs --skill experiment-design-clawpod-app

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design teams need a repeatable framework to validate ideas, measure impact, and reduce guesswork when pursuing growth.

Core Features & Use Cases

Define growth levers (acquisition, activation, retention, revenue, referral) and the metric to improve.
Generate 3-5 experiment ideas and score them with ICE (Impact, Confidence, Ease).
Produce a complete experiment specification with hypotheses, target metrics, sample size, duration, and success criteria.
Use cases include channel tests, funnel optimizations, feature experiments, and onboarding improvements.

Quick Start

Use this skill to craft a structured plan by specifying the target metric and desired outcome, and ask for a top experiment with full details.

Frequently Asked Questions about experiment-design

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

FAQPage Schema

How do I design growth experiments with ICE scoring?▼

Growth experiments are structured using ICE scoring to evaluate 3-5 generated ideas, then producing a detailed experiment specification with hypotheses, target metrics, sample size, duration, and success criteria.

What is the best way to structure a hypothesis for a funnel optimization experiment?▼

A funnel optimization hypothesis defines the target metric, expected outcome, and success criteria. This framework validates if specific changes reliably boost the desired growth metric.

Can I use this framework for channel tests and feature experiments?▼

Yes, this experiment design framework applies to channel tests, feature experiments, funnel optimizations, and onboarding improvements across marketing, product, and growth initiatives.

How do I prioritize A/B testing ideas for acquisition and retention?▼

Prioritize A/B testing ideas by applying ICE scoring across impact, confidence, and ease to selected growth levers like acquisition, activation, retention, revenue, or referral.

What metrics do I need to define before running a growth test?▼

Before running a growth test, define the specific growth lever to improve, the target metric, the desired outcome, and the success criterion to ensure reliable measurement of the experiment's impact.