Lenny Skills is a collection of product-management and engineering workflows for Claude Code and other AI agents, covering areas such as strategy, research, planning, shipping, growth, and hiring. Each skill gives an agent specialized guidance, frameworks, checklists, or templates for product work, and the catalogue contains many of these skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add RefoundAI/lenny-skills --skill growth-experimentationgit clone --depth 1 https://github.com/RefoundAI/lenny-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/refoundai/lenny-skills/growth-experimentation)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/growth-experimentation"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/growth-experimentation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/refoundai/lenny-skills/growth-experimentation"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/growth-experimentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.01224 |
| Opus 5 | $0.00015 | $0.00612 |
| Sonnet 5 | $0.00006 | $0.00245 |
| Haiku 4.5 | $0.00003 | $0.00122 |
Grade A, and why
growth-experimentation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Establish the Baseline - Analyze current conversion funnels and identify the single North Star metric to focus on.
- Prioritize and Plan - Use frameworks like ICE or RICE to rank experiments by impact and engineering cost.
- Execute and Iterate - Launch scrappy tests quickly to find signals of life before scaling into robust features.
- 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."
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 85 lines · 29 tokens per session scan A e4a585010fb0
growth-experimentation is a skill published in the GitHub repository RefoundAI/lenny-skills (1,321 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 1,224 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
llama-cpp
Run LLM inference with llama.cpp on CPU, Apple Silicon, AMD/Intel GPUs, or NVIDIA — plus GGUF model conversion and quantization (2–8 bit with K-quants and imatrix). Covers CLI, Python bindings, OpenAI-compatible server, and Ollama/LM Studio integration. Use for edge deployment, M1/M2/M3/M4 Macs, CUDA-less…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.
github-auth
Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.
ideation
Generate project ideas through creative constraints. Use when the user says 'I want to build something', 'give me a project idea', 'I'm bored', 'what should I make', 'inspire me', or any variant of 'I have tools but no direction'. Works for code, art, hardware, writing, tools, and anything that can be made.
notion
Notion API for creating and managing pages, databases, and blocks via curl. Search, create, update, and query Notion workspaces directly from the terminal.
ocr-and-documents
Extract text from PDFs and scanned documents. Use webextract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.