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Agent skill

Growth Experimentation

by RefoundAI in RefoundAI/lenny-skills

Help users build and scale a high-velocity growth experimentation engine that prioritizes impact and fosters a culture of rapid learning.

MITAuto-check passedMarketing & SEO

Install Growth Experimentation

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

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install RefoundAI/lenny-skills growth-experimentation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/growth-experimentation .claude/skills/growth-experimentation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
growth-experimentation
GitHub stars
1.4k
Token cost
~1.5k tokens
SKILL.md length
858 words
Files
3 (incl. references)
Skills in repo
76
Repo updated
First seen
Licence
MIT

At a glance

Help users build and scale a high-velocity growth experimentation engine that prioritizes impact and fosters a culture of rapid learning.

  • Works in 4 steps: Establish the Baseline - Analyze current… → Prioritize and Plan - Use frameworks… → Execute and Iterate - Launch scrappy… → …
  • Tasks that involve A/B testing
  • SKILL.md covers How to Help, Core Principles, Templates & Frameworks and Questions to Help Users, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Growth Experimentation is an agent skill from RefoundAI/lenny-skills. Help users build and scale a high-velocity growth experimentation engine that prioritizes impact and fosters a culture of rapid learning.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/artifacts.md` and `references/guest-insights.md`).

It sits in Marketing & SEO, covering A/B testing. The repository describes itself as: 86 product management skills from Lenny's Podcast for Claude Code and AI agents. Hiring, user research, strategy, shipping, and more. The licence is MIT.

When your agent uses it

  • Tasks that involve A/B testing

Example prompts

  • “/growth-experimentation”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit 13598cc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Growth Experimentation loads about 1.5k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 40 tokens; SKILL.md has 858 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from RefoundAI/lenny-skills at commit 13598cc, republished under its MIT licence (© RefoundAI). 858 words, ~1,547 tokens.

Download SKILL.mdSave it as .claude/skills/growth-experimentation/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
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.

Show full SKILL.md (394 more words)Show less

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

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

© RefoundAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/growth-experimentation of RefoundAI/lenny-skills.

  • SKILL.md
  • references/artifacts.md
  • references/guest-insights.md

Open the folder on GitHubat commit 13598cc

Compare with similar skills

Growth Experimentation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Growth Experimentation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Growth Experimentation this skillRefoundAI/lenny-skills1.4k—~1.5kAutomated safety check: PassMIT
Ab Testingcoreyhaines31/marketingskills54k3 repos~3.1kAutomated safety check: PassMIT
AnalyticsNexus-JPF/note-companion8707 repos~2.2kAutomated safety check: PassMIT
Ad Test Designeraaron-he-zhu/aaron-marketing-skills2.9k2 repos~2.8kAutomated safety check: PassApache-2.0
Ab Test Analyzeririnabuht12-oss/marketing-skills4.1k—~1.4kAutomated safety check: PassNone
Ab Test Store Listingappeeky/aso-skills2.2k—~1.8kAutomated safety check: PassMIT

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Categories

Questions about Growth Experimentation

What does Growth Experimentation do?

Help users build and scale a high-velocity growth experimentation engine that prioritizes impact and fosters a culture of rapid learning. Growth Experimentation is an agent skill from RefoundAI/lenny-skills. Help users build and scale a high-velocity growth experimentation engine that prioritizes impact and fosters a culture of rapid learning.

When should I use Growth Experimentation?

Growth Experimentation fits situations like: tasks that involve A/B testing.

How do I install Growth Experimentation in Claude Code?

Run `npx skills add RefoundAI/lenny-skills --skill growth-experimentation -a claude-code`. Or copy the skill folder (skills/growth-experimentation in RefoundAI/lenny-skills) into .claude/skills/growth-experimentation in your project. Claude Code loads it when a task matches its description.

How do I install Growth Experimentation in Codex?

Run `npx skills add RefoundAI/lenny-skills --skill growth-experimentation -a codex`. Or copy the skill folder (skills/growth-experimentation in RefoundAI/lenny-skills) into .agents/skills/growth-experimentation in your project. Codex loads it when a task matches its description.

Can I use Growth Experimentation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add RefoundAI/lenny-skills --skill growth-experimentation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/growth-experimentation, .gemini/skills/growth-experimentation, .github/skills/growth-experimentation and .opencode/skills/growth-experimentation in your project.

What does Growth Experimentation need to run?

SKILL.md names no scripts, command-line tools or credentials: Growth Experimentation is instructions for the agent only.

Does Growth Experimentation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Growth Experimentation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Growth Experimentation use?

Growth Experimentation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Growth Experimentation use?

About 1.5k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.3k tokens, read only when the agent opens those files.

What are the alternatives to Growth Experimentation?

Skills that share tags, products or a category with Growth Experimentation: Ab Testing (coreyhaines31/marketingskills, 54k stars), Analytics (Nexus-JPF/note-companion, 870 stars), Ad Test Designer (aaron-he-zhu/aaron-marketing-skills, 2.9k stars) and Ab Test Analyzer (irinabuht12-oss/marketing-skills, 4.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Growth Experimentation?

RefoundAI (a GitHub organization) maintains it in RefoundAI/lenny-skills, which has 1,382 GitHub stars. The repository holds 76 skills in this directory. The repository was last updated on July 16, 2026.

Source: RefoundAI/lenny-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.