Analyzing User Feedback

by RefoundAI

Analyzing User Feedback is a Data skill for Claude Code, published by RefoundAI in lenny-skills.

1.3K stars161 forkson RefoundAI/lenny-skillsAdded 2026/08/20Repository updated 2026/07/16

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ai-agentsai-assistantclaudeclaude-codelenny-rachitskyllmpm-toolsproduct-managementproduct-skillsskills

Install in seconds

Install Analyzing User Feedback

Copy Analyzing User Feedback into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/RefoundAI/lenny-skills/tree/main/skills/analyzing-user-feedback ~/.claude/skills/analyzing-user-feedback

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/RefoundAI/lenny-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/analyzing-user-feedback/SKILL.md in RefoundAI/lenny-skills
Installs to: ~/.claude/skills/analyzing-user-feedback
Collection: One of 25 skills cataloged from this repository
Category: Data — 668 skills

What Analyzing User Feedback does

Analyzing User Feedback helps product teams turn large volumes of qualitative and quantitative feedback into high-confidence product decisions by categorizing signals, assessing representativeness, and applying AI synthesis.

Analyzing User Feedback is cataloged under Data on DirSkills. Analyzing User Feedback comes from a repository tagged ai-agents, ai-assistant, claude, claude-code and lenny-rachitsky.

Documentation

README

Excerpt

Analyzing User Feedback

Transform raw signals into actionable insights by scaling empathy and synthesis.

Help the user with analyzing user feedback using insights from 19 guests and posts across Lenny's Podcast and Newsletter.

How to Help

Categorize signals - Help the user group disparate feedback into themes or segments based on user influence and frequency.
Assess representativeness - Determine if feedback reflects a vocal minority or a broad user need using representation frameworks.
Set up dogfooding - Design internal processes to experience friction firsthand through audits and mandatory usage programs.
Apply AI synthesis - Guide the user in using LLMs to process large datasets like transcripts, reviews, and support tickets.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Analyzing User Feedback

What else does RefoundAI publish alongside Analyzing User Feedback?
Analyzing User Feedback is one of 25 skills that DirSkills catalogs from RefoundAI/lenny-skills, the repository it ships in. Its siblings there include AI Evals, AI Product Strategy and AI-Assisted Prototyping. Each one is a separate skill with its own page in this directory, installs the same way Analyzing User Feedback does, and is maintained by RefoundAI in that same repository. The rest of the collection is listed on the RefoundAI/lenny-skills page.
How does Analyzing User Feedback compare to other Data skills?
Analyzing User Feedback ranks #425 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Analyzing User Feedback against them. Open each page to compare what they document and how they install.