# analyzing-user-feedback

This skill guides product teams through synthesizing user feedback, filtering out vocal minorities, and setting up internal dogfooding programs.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2600 installs · ★ 1434
- Author: RefoundAI
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/RefoundAI/lenny-skills/tree/HEAD/skills/analyzing-user-feedback/SKILL.md
- Updated: 2026-07-16
- HTML: https://affpapa.org/en/nevibecode/skills/analyzing-user-feedback

## What it does

The skill provides frameworks to process qualitative and quantitative user feedback into product decisions. It focuses on categorizing signals by user influence and frequency rather than raw volume. Through matrices like depth versus breadth of effect, it helps identify whether a complaint comes from a vocal minority or represents a widespread issue.

It includes operational templates such as internal product testing, core service participation programs, and semantic clustering of support tickets via language models. Extended context and frameworks reside in `references/artifacts.md` and `references/guest-insights.md`.

Teams often fail by jumping straight into brainstorming fixes during audits before properly observing the friction itself.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill analyzing-user-feedback -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/analyzing-user-feedback/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/analyzing-user-feedback/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill analyzing-user-feedback -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/analyzing-user-feedback/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill analyzing-user-feedback -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/analyzing-user-feedback/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install RefoundAI/lenny-skills analyzing-user-feedback
```

2. **Manually: put the files in place**
- File: `.github/skills/analyzing-user-feedback/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill analyzing-user-feedback -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/RefoundAI/lenny-skills --path skills/analyzing-user-feedback
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/analyzing-user-feedback/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill analyzing-user-feedback -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/RefoundAI/lenny-skills/tree/HEAD/skills/analyzing-user-feedback/SKILL.md

## When to use it
- filter out vocal minority complaints from broad user needs
- structure company-wide dogfooding programs to catch friction
- cluster support tickets and transcripts using language models
- evaluate feedback impact with representation and influence matrices

## Example prompts
- Cluster these customer support tickets by theme and user frequency
- Evaluate this feature request using the representation and influence matrix
- Help set up a dogfooding audit for our core user onboarding flow

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: d5d84b07f6b874c1.

BEGIN THIRD-PARTY FILE d5d84b07f6b874c1
```text
---
name: analyzing-user-feedback
description: Help users convert massive volumes of qualitative and quantitative feedback into high-confidence product decisions through rigorous synthesis and internal immersion.
---

# 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

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

## Core Principles

### Experiential Empathy
Jeff Weinstein: "We show up four to eight people total pretend to be some company with some outcome problem. Rule one is you do not work at Stripe and rule two is we're not here to solve any problems. This is just about practicing empathy for the customer."

Build deeper empathy by having internal teams experience product friction firsthand without the distraction of immediate problem solving.

### Mandatory Service Participation
Keith Yandell: "We have a program called WeDash, where, four times, a year all employees are required to go do deliveries. And I love doing it. I do it more than four times a year, and I usually take my daughters with me."

Require every employee to perform the core service of the business to build authentic empathy and surface operational bugs.

### Creator Mindset Immersion
Maya Prohovnik: "If they talk to users all the time, they see the data, but all of them, once they finally start doing their podcast, they're like, I get it. Something clicked and now I feel like I really understand what they need. And I guess building tools for creators is similar to building a B2B product where you really have to understand business, it's their livelihood."

Directly immerse team members in the product to transform abstract data into a deep understanding of complex user workflows.

### Statistical Representation Filtering
From "What 5 years at Reddit taught us about building for a highly opinionated user base": "Just because someone is loud doesn’t mean you should act on their complaints. You need to get good at identifying whom you should pay attention to. That starts with examining who is being loud."

Evaluate feedback based on its statistical representation and the influence of the users providing it to avoid building for a vocal minority.
```
END THIRD-PARTY FILE d5d84b07f6b874c1

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First 2833 of 6535 bytes. © RefoundAI · MIT · https://github.com/RefoundAI/lenny-skills/tree/HEAD/skills/analyzing-user-feedback/SKILL.md

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