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 measuring-pmfgit 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/measuring-pmf)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/measuring-pmf"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/measuring-pmf/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/measuring-pmf"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/measuring-pmf.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 | $0.00033 | $0.01546 |
| Opus 5 | $0.00016 | $0.00773 |
| Sonnet 5 | $0.00007 | $0.00309 |
| Haiku 4.5 | $0.00003 | $0.00155 |
Grade A, and why
measuring-pmf 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measuring Product-Market Fit
Transition from pushing your product to feeling the market pull it out of you.
Help the user with measuring product-market fit using insights from 16 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Diagnose the current stage - Determine where the user is on the journey from pre-product validation to scalable growth.
- Apply quantitative benchmarks - Evaluate cohort retention and Sean Ellis scores against industry standards.
- Analyze market pull - Identify signals of organic demand versus founder-led momentum.
- Iterate based on feedback - Help prioritize roadmap changes that specifically drive toward retention for the most passionate users.
Core Principles
Standardize Quantitative Validation
Jag Duggal: "We rarely scale a project, a product we've launched, until we know the Sean Ellis score and we know that it's hit a threshold that we find really compelling."
Use an objective threshold like the Sean Ellis score to validate fit before scaling. Set a strict target of at least 40 percent of users feeling very disappointed if the product disappeared.
Look for Urgent Feedback
Raaz Herzberg: "We really felt the type of questions change, right? Silly. The call sounded like, again, "How are you pricing this, or when can we start doing a POV?" I think naturally, as human beings, you have a bias to look for affirmation, versus a bias for what you don't want to hear."
True fit is revealed when customer feedback shifts from polite interest to urgent, practical questions about pricing and implementation. Treat generic interest as a negative signal.
Listen Selectively
Rahul Vohra: "You have to deliberately not act on the feedback of many of your early users, and this is at the same time as listening to people intensely and building what people want."
Ignore feedback from most users to focus exclusively on the segment that would be very disappointed without your product. Double down on what that specific cohort loves.
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 · 95 lines · 33 tokens per session scan A 8b472c31c107
measuring-pmf is a skill published in the GitHub repository RefoundAI/lenny-skills (1,318 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 1,546 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 2026-08-30.
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.