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 retention-engagementgit 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/retention-engagement)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/retention-engagement"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/retention-engagement/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/retention-engagement"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/retention-engagement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00044 | $0.00981 |
| Opus 5 | $0.00022 | $0.00491 |
| Sonnet 5 | $0.00009 | $0.00196 |
| Haiku 4.5 | $0.00004 | $0.00098 |
Grade A, and why
retention-engagement 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 9d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retention and Engagement Mastery
Build sustainable growth by embedding products into workflows and creating compounding user value.
Help the user with retention and engagement mastery using insights from 11 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Define metrics - Guide the user in identifying their specific activation milestones and setting up cohort-based retention tracking.
- Audit friction - Analyze the onboarding process and core user flows to eliminate technical and mental hurdles that cause early drop-off.
- Design habits - Suggest psychological mechanics like streaks, loss aversion, or mounting value to increase switching costs.
- Benchmark performance - Compare current churn and retention rates against industry-specific 'Good' and 'Great' standards to prioritize interventions.
Core Principles
Retention before monetization
Albert Cheng: "User retention is gold for consumer subscription companies. If you don't retain your users, then a lot of the onus is on getting them to pay on day one."
Prioritize a foundation of high user retention over aggressive early monetization to ensure sustainable long term growth.
Onboarding as a retention driver
Crystal W: "Well, based on our data about a third of people will consider switching to another company after just one bad experience during onboarding. So if your CSV importer doesn't work right, which is super common, considering customer files are chalked full of unexpected data and formatting, they'll leave."
Treat the first experience as the most critical point for retention because users are prone to abandon a product at the first sign of friction.
Survival curve measurement
Dan Hockenmaier: "Second would be retention. So at what rate are these customers activating? And then have some kind of basic monthly retention curve. So how long are they staying around? What's the survival rate in each of these? And those kind of stack over time."
Effective retention management requires tracking the long term survival curve of user cohorts rather than just high level active user counts.
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.
- 9d ago First seen · 77 lines · 44 tokens per session scan A abd0251fb68b
retention-engagement is a skill published in the GitHub repository RefoundAI/lenny-skills (1,321 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 981 once invoked, about $0.0002 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 2026-09-03.
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.