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-modelgit 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-model)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/growth-model"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/growth-model/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-model"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/growth-model.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 | $0.00028 | $0.01556 |
| Opus 5 | $0.00014 | $0.00778 |
| Sonnet 5 | $0.00006 | $0.00311 |
| Haiku 4.5 | $0.00003 | $0.00156 |
Grade A, and why
growth-model 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 11d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building a Sustainable Growth Model
Move beyond linear funnels to build compounding loops that drive scalable, long-term product growth.
Help the user with building a sustainable growth model using insights from 16 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Audit current mechanics - Assist the user in identifying whether their current growth is driven by SEO, paid ads, sales, or virality based on product characteristics.
- Visualize the model - Guide the user to map out their growth as a system of interconnected, reinforcing loops rather than a one-way funnel.
- Quantify the loops - Help the user build a mathematical representation of their growth variables in a spreadsheet to stress-test their assumptions.
- Identify constraints - Analyze where growth is stalling by diagnosing failure points in the user journey from acquisition to retention.
Core Principles
Loops over funnels
Shishir Mehrotra: "But I highly encourage drawing a diagram like this for your business. I'll flash it up on screen for a second and I'll describe it, but this is what the diagram looks like, black loop, blue loop, and it's basically the two different ways that our product spreads. The Black Loop is someone comes in, they make a doc, they share with a group of people, some subset of the people turn around and make another doc, and the process repeats itself over and over again."
Visualize growth as recurring loops where the output of one cycle becomes the input for the next, creating organic mechanisms for scaling.
Mathematical reconciliation
Dan Hockenmaier: "And that's how I think about a growth model, so the analytical representation of how the business grows and it's typically built in a spreadsheet which has a really nice feature of being very hard to fake. You can talk about a business conceptually, but when you actually have to get it to line up and link in a model, it's very hard to not force yourself to understand how the business works."
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.
- 11d ago First seen · 89 lines · 28 tokens per session scan A 7070c3a19073
growth-model is a skill published in the GitHub repository RefoundAI/lenny-skills (1,318 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 1,556 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.