4 items
Turn public comment threads into clean CSV/JSON exports and AI-ready signal. Reddit, YouTube, Hacker News, Steam & more. You found a thread full of how your customers actually talk. Now you're copy-pasting comments one at a time into a spreadsheet. CommentGrab pulls them all in one click. Open any supported page, click the floating widget, and export every comment and reply as clean CSV, JSON, or Markdown. Then point your own OpenAI key at it and get back the objections, hooks, and exact phrases buried in the replies. It's free. It's open source (MIT). No account, no server. The comments only leave your device when you send them to OpenAI yourself. Works where your research already lives: • Reddit threads and user profiles • Hacker News comment trees • Steam reviews • YouTube video and Shorts comments, with replies • Amazon, Trustpilot, Product Hunt, Etsy, Quora What you get: • One-click capture with a floating widget and live progress • Author, timestamp, score, permalink, in-comment links, and thread depth. Toggle any of them. • A dashboard to save threads, search them, and come back later • AI analysis with your own key: objections, ad hooks, emotional triggers, failed solutions, verbatim customer language, top themes, and sentiment • Everything stored on your device Most comment exporters are single-platform, ad-heavy, and stop at a CSV. They also want $45 a month. CommentGrab is free, open source, and goes all the way to insight. Source code: https://github.com/maribol/commentgrab.app
Jun 29, 2026
rating_count is the Chrome Web Store ratings count, not a written-review count.
Media assets
Screenshots and videos on the listing.
Has promo video
Whether the listing includes at least one video.
Languages
Declared language locales.
Developer website
Listing exposes a developer website URL.
Contact email
Listing exposes a contact email.
Keyword in name
Case-insensitive substring match in the name.
Keyword in description
Case-insensitive substring match in the description.
Keyword occurrences in description
Count of case-insensitive occurrences in the description.
Category user-count percentile
Share of same-category extensions with fewer users (null if unknown).
These are transparent listing completeness / keyword signals, not a prediction of Chrome Web Store search ranking.