App review analysis for PMs
Cursor analyzed a CSV of a couple thousand Walmart App Store reviews and returned takeaways with ASCII diagrams.
In 30 seconds it returned top complaints ranked by frequency, a severity versus frequency matrix, and a user journey map showing where frustration peaks.
Overview
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.
A prompt to start from
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.Written by Agentic Feats from the source. Not the author's exact prompt.
Sources 1
History
- Source published
- 2026-01-05
- Added to Agentic Feats
- 2026-10-13
- Pick
- No
- Markdown copy
- /c/app-review-analysis-for-pms-cursor.md
More in research
36 featsResultIt created the text file as asked, but after clicking a result it took a screenshot too early and hallucinated the answer.
ResultOperator handled tour search and lesson plan but was slow; adding three grocery items took 15 minutes and an Uber price check took five.
ResultThe draft was usable but weaker than accepted NSF summaries; thinking mode helped, though it began hallucinating and misreading the sources.