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growth-experimentation — Claude Code skill: a growth experiment engine

Walks you through building a steady stream of growth experiments: prioritisation with ICE and RICE, cheap tests first, learnings shared across the team.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add RefoundAI/lenny-skills --skill growth-experimentation -a claude-code
Open on GitHub

What growth-experimentation does

The skill moves through four stages. First you map the current funnel and pick a single North Star metric. Then ideas get ranked by impact and engineering cost. Scrappy tests go out early to find signs of life before anything becomes a real feature. Finally, wins and failures get socialised so every insight counts more than once.

It ships with templates: an Airtable setup called EVELYN for RICE/DRICE prioritisation, a Google Doc experiment design template, a weekly review meeting structure, and a decision tree for when to A/B test versus when to just ship. There is also a list of common mistakes it will flag — waiting for one big feature, running strict tests on every minor change without the data volume, or burying launches under multi-level approvals.

Self-check questions come with it too: how many experiments run per week, what share of users actually sees the flow you want to optimise. The material draws on 10 guests from Lenny's Podcast and Newsletter, with sources listed in references/guest-insights.md. Install runs through npx skills add, or gh skill install for GitHub Copilot.

What it helps with

  • pick one North Star metric instead of tracking a dozen at once
  • rank growth ideas by impact against engineering cost
  • test a new channel cheaply before committing budget
  • set up a routine for sharing experiment results across teams
  • decide when a change needs an A/B test and when it can just ship

Example prompts for the agent

  • help me build a growth experimentation engine: map the funnel and suggest a North Star metric
  • run my growth ideas through RICE and show the top 3 by impact and cost
  • draft an experiment design template for the new onboarding flow

How to install growth-experimentation

1Skills CLI (Vercel)

npx skills add RefoundAI/lenny-skills --skill growth-experimentation -a claude-code

2Manually: put the files in place

~/.claude/skills/growth-experimentation/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/growth-experimentation/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about growth-experimentation

How do I install growth-experimentation in Claude?

Run npx skills add RefoundAI/lenny-skills --skill growth-experimentation -a claude-code. The skill lands at ~/.claude/skills/growth-experimentation/SKILL.md.

What does growth-experimentation do?

It guides you through four stages: baseline funnel and North Star, experiment prioritisation, fast tests, then spreading the learnings. It also flags common mistakes like chasing a single big feature.

What is growth-experimentation built on?

Insights from 10 guests across Lenny's Podcast and Newsletter. The full source list sits in references/guest-insights.md, templates and frameworks in references/artifacts.md.

Source text

SKILL.md · MIT
---
name: growth-experimentation
description: Help users build and scale a high-velocity growth experimentation engine that prioritizes impact and fosters a culture of rapid learning.
---

# Growth Experimentation Velocity

Build a high-output engine to compound small wins into massive growth.

Help the user with growth experimentation velocity using insights from 10 guests and posts across Lenny's Podcast and Newsletter.

## How to Help

1. **Establish the Baseline** - Analyze current conversion funnels and identify the single North Star metric to focus on.
2. **Prioritize and Plan** - Use frameworks like ICE or RICE to rank experiments by impact and engineering cost.
3. **Execute and Iterate** - Launch scrappy tests quickly to find signals of life before scaling into robust features.
4. **Scale and Socialize** - Systematize the sharing of wins and failures across the organization to multiply the impact of every insight.

## Core Principles

### Search for signs of life
Timothy Davis: "You can always do a very, very small test. You can just put a little money into a platform, see if there's a sign of life. If there is, then you can pull back and say, 'Okay, we have signs of life. Now let's build a campaign around that.'"

Validate new channels or ideas using low-budget tests and narrow match thresholds before committing significant resources.

### Embrace the counterfactual
From "How today’s top consumer brands measure marketing’s impact": "Testing/conversion lift studies (CLS): regularly run by marketers to validate what performance would look like if you switched a channel off, or scaled spend up or down."

Use randomized testing and lift studies as the gold standard to observe what would happen without your intervention.

### Leverage compounding effects
From "The secret to Duolingo’s exponential growth": "To get the best long-term gains, you should always have a sense of urgency. The quicker you launch winning experiments, the quicker those changes impact your growth. Not only that, but these improvements compound!"

Focus on high experiment velocity because early small wins multiply over time into significant competitive advantages.

### Optimize psychological commitment
Jackson Shuttleworth: "We've actually set up really good infrastructure for copy testing. We used to say continue, our standard CTA is continue, and we changed that to commit to my goal, and it was a massive win."

Shift from generic microcopy to intentional language that reinforces the user's specific goals and psychological state.

### Lower friction with scrappy tools
From "Fostering a culture of experimentation": "When systems are still in flux, you don't want to overinvest in tooling that will become outdated immediately when your data schema gets updated or some other piece of infrastructure changes. However, it is essential to have a way to rapidly iterate, and that means quick access to experiment results data. So if you need to in the early days, build something simple and scrappy at first, and over time evolve it to support the team's needs."

Prioritize rapid iteration over perfect infrastructure by starting with simple internal tools to prove the value of testing.

## Templates & Frameworks

- **EVELYN (Experiment Velocity Engine Lifting Your Numbers) - Airtable Template** (Introducing DRICE: a modern prioritization framework) - A batteries-included Airtable template for managing growth experiment prioritization using RICE/DRICE
- **Noom's Experimentation Velocity Principles** (How to win in consumer subscription) - A set of operating principles for running a high-velocity experimentation program in growth
- **4-Step Conversion Optimization Process** (Prioritizing conversion opportunities) - A structured end-to-end process for identifying, prioritizing, executing, and learning from conversion optimization work
- **Experiment Design Template** (Breaking into growth) - A Google Doc template for designing and running growth experiments
- **Impact and Learnings Review Meeting** (Ben Williams) - A weekly document and meeting structure used by growth teams to discuss and socialize experiment learnings.
- **6 Guidelines for Experiment Urgency** (The secret to Duolingo’s exponential growth) - Tactical guidelines for moving quickly on experiments to maximize compound growth, used at Duolingo
- **Growth Ideas Brainstorming Framework ('How Might We…?')** (Growth ideas) - A facilitation approach for running team brainstorming sessions where you go through a categorized list of growth ideas and apply 'How might we…?' framing to ge
- **Conversion Optimization Decision Tree: Experiment vs. Ship** (Strategy and tactics for increasing conversion) - Guidance on when to A/B test conversion changes vs. when to just ship them

See `references/artifacts.md` for the full list with details.

## Questions to Help Users

- "What is the single North Star metric you are currently trying to move?"
- "How many experiments are you currently running per week?"
- "What is the estimated engineering cost versus the predicted impact for your top three ideas?"
- "Do you have a standardized process for sharing experiment learnings across the whole team?"
- "Is your team autonomous enough to launch experiments without multi-level approvals?"
- "What percentage of your user base actually encounters the flow you are planning to optimize?"

## Common Mistakes to Flag

- **Waiting for silver bullets** - Teams often stall growth by looking for one massive feature instead of accumulating many small optimizations.
- **Paralysis by testing** - Applying rigorous A/B testing to every minor change can slow down execution if there is not enough data volume.
- **Ignoring the addressable pie** - Failing to factor in how many users actually see a change leads to overestimating the real-world impact.
- **High-friction approvals** - Requiring multiple levels of sign-off for experiments kills the momentum needed for a high-velocity culture.

## Deep Dive

For all 16 sourced insights from 10 guests, see `references/guest-insights.md`

## Related Skills

- Growth Model
- Acquisition Channels
- User Onboarding Activation
- Retention Engagement

© RefoundAI · MIT · RefoundAI/lenny-skills

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