---
title: "App review analysis for PMs"
canonical: "https://agenticfeats.com/c/app-review-analysis-for-pms-cursor/"
agents: ["Cursor"]
evidence_grade: "A"
outcome: "completed"
job_type: "Research"
roles: ["Product"]
source_url: "https://amankhan1.substack.com/p/cursor-for-product-managers"
evidence_url: "https://maven.com/p/caee21/cursor-for-product-managers"
source_date: "2026-01-05"
published: "2026-10-13"
updated: "2026-10-13"
---

> From Agentic Feats: sourced examples of AI agents at work. Map of the site for agents: https://agenticfeats.com/llms.txt

# App review analysis for PMs

Cursor analyzed a CSV of a couple thousand Walmart App Store reviews and returned takeaways with ASCII diagrams.

Result: In 30 seconds it returned top complaints ranked by frequency, a severity versus frequency matrix, and a user journey map showing where frustration peaks.

## How it was set up

Cursor with the review CSV in the project. For brainstorming, an AGENTS.md file the agent generated from the cloned codebase. Models mentioned: Composer 1, Claude Opus 4.5, Gemini 3 Flash.

## Worth knowing

Review data was public and the demo was shown for teaching purposes, not tied to a shipped product change.

## Prompt

```text
Help me analyze the [review data file] and give me high level takeaways. Break the data into clear, concise insights in as few words as possible. Give me high level diagrams using ASCII.
```

## Evidence note

Guest post with screenshots and a recorded lightning lesson; no repo

## Sources

- Original: https://amankhan1.substack.com/p/cursor-for-product-managers
- Evidence: https://maven.com/p/caee21/cursor-for-product-managers

## The receipt

- Agent: Cursor
- Evidence: Shown (grade A, artifact linked)
- Outcome: Completed
- Job type: Research
- Roles: Product
- Source: Blog
- Published: 2026-01-05
- Added: 2026-10-13