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 product-tastegit 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/product-taste)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/product-taste"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/product-taste/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/product-taste"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/product-taste.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 | $0.00037 | $0.01610 |
| Opus 5 | $0.00018 | $0.00805 |
| Sonnet 5 | $0.00007 | $0.00322 |
| Haiku 4.5 | $0.00004 | $0.00161 |
Grade A, and why
product-taste 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Taste and Intuition
Develop a reliable internal compass to recognize and build world-class products.
Help the user with product taste and intuition using insights from 35 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Deconstruct experiences - Guide the user through analyzing specific product details to identify why they evoke certain emotional responses.
- Calibrate intuition - Help the user document and review their decision rationales against real-world outcomes to refine their judgment.
- Challenge assumptions - Use first-principles thinking to question standard industry metrics and focus on core user utility.
- Refine the feel - Assist in articulating the specific polish, speed, and usability elements that contribute to a high-quality product feel.
Core Principles
Learn Through Model Primitives
Howie Liu: "I think to really understand the solution space of what's possible, you have to be in the details. I mean, literally, you can't just look at screenshots or a pre-recorded video of a new product feature. AI is something you have to play with, and ideally you're playing with both the packaged up app or solution that you've built with it, but you're also playing around directly with the underlying primitives who are using the models either via API or via a chat interface."
True intuition is built by playing directly with the underlying technology and APIs rather than observing demos. Understanding the raw boundaries of your ingredients allows you to imagine new possibilities.
Articulate Emotional Responses
Jessica Hische: "Most people are better at understanding the feelings and sensations that typography and logos give us than they give themselves credit for, because what we are as people are endless absorbers of patterns, and information, and all this kind of stuff as we move throughout the world. We don't take time to sit and digest it, but it's still coming in and getting logged, and so even as a non-designer, I think you can look at examples of logos where something's not quite right and be like, 'Something's not right here, I just don't know how to name it.'"
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 · 91 lines · 37 tokens per session scan A e351cc12894e
product-taste is a skill published in the GitHub repository RefoundAI/lenny-skills (1,321 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 1,610 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.
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