ReapX

Read TikTok publishing patterns across posts. Start with TikTok post URLs; each returned post keeps text, authors, publication dates, room ID, and room title.

Compare TikTok products, offers, and identifiers. Start with TikTok product URLs; each returned product keeps product identifiers, titles, prices, review volume, and ratings.

Read TikTok reviews as a source-linked corpus. Start with TikTok review or listing URLs; each returned review keeps review identifiers, reviewer names, ratings, and content.

Keep TikTok search results in rows you can compare. Start with search terms, TikTok searches or result URLs, and result count; each returned search result keeps search term, author handles, author nickname, music author, and author identifiers.

Trace TikTok sounds through the posts using them. Start with TikTok sound URLs; each returned sound keeps sound video count, sound identifiers, sound title, author handles, and sound cover URL.

Put TikTok spoken content into searchable text. Start with TikTok video URLs; each returned transcript keeps text, search term, video identifiers, language, and caption source.

Collect TikTok videos and their publishing signals. Start with TikTok video URLs; each returned video keeps video identifiers, titles, authors, author nickname, and author bio.

Compare TripAdvisor places, stays, and travel-market facts. Start with TripAdvisor URLs; each returned record keeps addresses, price level, overall rating, review volume, and place identifiers.

Read TripAdvisor reviews as a source-linked corpus. Start with TripAdvisor review or listing URLs; each returned review keeps review date, review identifiers, review title, review text, and author handles.

Read Trustpilot reviews as a source-linked corpus. Start with Trustpilot review or listing URLs; each returned review keeps review date, review volume, review identifiers, review title, and review text.

Compare Twitch channels and their publishing record. Start with Twitch channel URLs; each returned channel keeps channel names, channel identifiers, country, joined date, and titles.

Read Twitter comment threads as a corpus. Start with Twitter post or video URLs; each returned comment keeps comment identifiers, text, author handles, author names, and verification status.

Keep Twitter accounts and publishing activity in one research file. Start with Twitter URLs; each returned record keeps text, engagement count, author identifiers, author handles, and author names.

Map Twitter (X) audiences for account research. Start with Twitter follower URLs; each returned follower keeps audience size, handles, names, following count, and posts count.

Read Twitter publishing patterns across posts. Start with Twitter post URLs; each returned post keeps post identifiers, text, conversation count, author names, and author handles.

Keep Twitter (X) search results in rows you can compare. Start with Twitter searches or result URLs; each returned search result keeps search query, text, author identifiers, author handles, and author names.

Read exact X or Twitter status URLs into a dated post file. Keep text, author, replies, hashtags, mentions, links, media, edit state, and source URL attached to each tweet.

Collect Twitter user records for account research. Start with Twitter user URLs; each returned user keeps handles, user identifiers, names, protected account, and audience size.

Build a working file of Upwork jobs and market signals. Start with Upwork URLs; each returned record keeps titles, authors, price min, date posted, and descriptions.

Compare open roles on Upwork. Start with Upwork job URLs; each returned job keeps job identifiers, titles, companies, price min, and locations.

Compare Walmart products, offers, and identifiers. Start with Walmart product URLs; each returned product keeps names, prices, list price, ratings, and review volume.

Keep the web event records in one working calendar file. Start with web page URLs; each returned event keeps event type, event status, names, start date, and end date.

Collect published contact emails from the open web. Start with web page URLs; each returned email record keeps unique email count, email addresses, email sources, site title, and response status.

Build a lead file from the open web. Start with web page URLs; each returned lead keeps company names, contact emails, email sources, phone numbers, and phone sources.