3 items
Extract LinkedIn posts and all their comments, then send them straight to Claude, ChatGPT or Gemini to draft your reply. Riff extracts a LinkedIn post and every one of its comments into clean, structured markdown, then opens Claude, ChatGPT or Gemini with the message box already filled. You read it and press Enter. How it works: Open any LinkedIn post Click the Riff icon Hit "Extract Post + Comments". Riff expands the whole comment thread for you Click "Send to Claude" (or ChatGPT, Gemini, or Clipboard only) Press Enter in the AI tab Get a draft reply that matches the conversation context Two modes: REPLY mode: Detected on your own posts. Extracts all comments so you can draft replies. COMMENT mode: Detected on others' posts. Extracts the post and comments so you can draft a comment that adds unique value. What gets extracted: Post author and headline Full post text (including "see more" content) All visible comments with author names, headlines, and timestamps Nested replies Post type detection (text, image, video, article, poll) Privacy first: Zero data collection. Everything runs locally in your browser. No analytics, no tracking, no servers. Content goes only to your clipboard or to the AI tab you chose to open. Open source: https://github.com/mshadmanrahman/riff Works on: LinkedIn feed page Individual post pages Posts with expanded comments Built for creators, founders, and professionals who want to engage authentically on LinkedIn without spending 30 minutes per comment.
Sep 8, 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.