product-reviews

product-reviews is a skill for Claude Code, Codex from RefoundAI/lenny-skills. It costs 30 tokens per session (1,379 once invoked), scanned A, original, MIT.

A guide for running product reviews as focused decision-making sessions instead of routine status meetings. It helps teams examine the problem, possible approaches, trade-offs, and decisions.

In plain words
What is it for?
Assessing review practices, choosing a review format, preparing pre-read materials, facilitating discussion, measuring alignment, and recording clear decisions.
Why use it?
It helps reveal quality gaps and unblock decisions before work advances too far. It gives teams a shared structure for discussing alternatives and handling disagreement.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Assessing review practices, choosing a review format, preparing pre-read materials, facilitating discussion, measuring alignment, and recording clear decisions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/refoundai/lenny-skills/product-reviews
About the project

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.

RefoundAI/lenny-skills · 1,321 stars · on GitHub

Install

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.

Any agent
npx skills add RefoundAI/lenny-skills --skill product-reviews
Clone the repo
git clone --depth 1 https://github.com/RefoundAI/lenny-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for product-reviews

README.md
[![agentmods](https://agentmods.dev/badge/skills/refoundai/lenny-skills/product-reviews/github.svg)](https://agentmods.dev/skills/refoundai/lenny-skills/product-reviews)
Your own site
<a href="https://agentmods.dev/skills/refoundai/lenny-skills/product-reviews"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/product-reviews/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.

agentmods 80×15 button for product-reviews

Your own site · 80×15
<a href="https://agentmods.dev/skills/refoundai/lenny-skills/product-reviews"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/product-reviews.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,379 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00030 $0.01379
Opus 5 $0.00015 $0.00690
Sonnet 5 $0.00006 $0.00276
Haiku 4.5 $0.00003 $0.00138

Measured 13d ago against content hash 7d0fa7c813ba, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

product-reviews 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 13d 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.

skills/product-reviews/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Effective Product Reviews

Run rigorous alignment sessions that raise the quality bar and accelerate decision-making.

Help the user with effective product reviews using insights from 12 guests and posts across Lenny's Podcast and Newsletter.

How to Help

  1. Assess review culture - Evaluate current review formats and identify bottlenecks or gaps in quality standards.
  2. Select a framework - Recommend the appropriate review stage or template based on the project's complexity and lifecycle.
  3. Draft review materials - Assist in framing problem statements, mapping the option space, and preparing pre-read documents.
  4. Facilitate the session - Provide techniques for gauging sentiment, managing dissent, and capturing clear decisions.

Core Principles

Evaluate Frameworks, Not Solutions

From "How Figma builds product": "One thing I encourage for both is to first present the “option space”—it’s really powerful to have a framework that maps all possible solutions or problems and use that as a device to discuss high-level tradeoffs or philosophical differences."

High-quality decisions come from mapping the entire field of possible solutions rather than debating a single proposal in isolation.

Establish a Transparent Cadence

From "How Duolingo builds product": "Product review meetings happen every Tuesday and Thursday for a total of two hours. These two hours are divided into 20-minute slots that product teams sign up to present. The agenda of these meetings is product teams presenting their proposed product changes for the first 5 to 10 minutes and then reviewers asking questions and giving feedback for the rest of the time."

Recurring, short review slots prevent feedback bottlenecks and create a shared understanding of what constitutes quality across the organization.

Separate Problem from Solution

From "How Miro builds product": "Product reviews can focus on different stages of the product development lifecycle: P-Strat: long-term strategy and vision; P0: the opportunity and problem that we want to pursue; P1: the proposed solution; P2: what we launched and how it’s performing."

Read the full file on GitHub · 90 lines

Files

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.

Changes

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.

  1. 13d ago First seen · 90 lines · 30 tokens per session scan A 7d0fa7c813ba

Subscribe to this mod's changes

product-reviews is a skill published in the GitHub repository RefoundAI/lenny-skills (1,321 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,379 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.