Monitor brand reputation across review and social platforms

This skill scrapes reviews, ratings, and brand mentions from multiple platforms using Apify Actors, giving a single guided path from choosing a data source to an exported report. Given a monitoring need, it walks through five steps: determine the data source by selecting an Actor, fetch that Actor's live input schema through the mcpc CLI, ask the user for an output format and result count, run the extraction script, and summarize what came back. Actor selection covers a spread of review and mention surfaces: Google Maps reviews (compass/crawler-google-places, or the dedicated compass/Google-Maps-Reviews-Scraper for export), Booking.com hotels and reviews (voyager/booking-scraper, voyager/booking-reviews-scraper), TripAdvisor reviews (maxcopell/tripadvisor-reviews), and a cluster of Facebook, Instagram, and TikTok/YouTube comment and mention scrapers (apify/facebook-reviews-scraper, apify/facebook-comments-scraper, apify/facebook-pages-scraper, apify/facebook-likes-scraper, apify/instagram-comment-scraper, apify/instagram-hashtag-scraper, apify/instagram-search-scraper, apify/instagram-tagged-scraper, apify/export-instagram-comments-posts, apify/instagram-scraper, apify/instagram-api-scraper, streamers/youtube-comments-scraper, clockworks/tiktok-comments-scraper). Once an Actor is chosen, the skill fetches its live schema and README through the mcpc CLI so the run uses the Actor's real, current input fields and output shape rather than a guessed one.

Use this skill when the task is to monitor reviews, ratings, or brand mentions across social, travel, or map platforms, or when you need exported monitoring results plus a summary of reputation signals. It is not a general-purpose scraper picker for unrelated data - the same pattern is built into sibling skills for pricing or lead-generation work instead. As the skill's own limitations state, it should be used only when the task clearly matches this scope, and it should not stand in for environment-specific validation, testing, or expert review; if required inputs, permissions, safety boundaries, or success criteria are missing, the workflow calls for stopping and asking for clarification rather than guessing.

Before running, the skill requires an .env file with APIFY_TOKEN, Node.js 20.6 or newer (for native --env-file support), and the mcpc CLI installed via npm install -g @apify/mcpc. The Actor's schema and details are fetched with:

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"

That call returns the Actor's description and README, its required and optional input parameters, and its output fields where available. The user is then asked to pick an output format - a quick in-chat answer with no file saved, a full CSV export, or a full JSON export - and how many results to pull, based on the use case. The extraction itself runs through run_actor.js with the chosen --actor, --input, and, for file exports, --output and --format flags. The final step reports the number of reviews or mentions found, the output file's location and name, the key fields available in the data, and suggested next steps such as sentiment analysis or filtering.

The skill integrates with the Apify platform through Apify Actors and the mcpc MCP client, and it depends on Node's native .env loading for credentials. Its documented error handling covers a missing APIFY_TOKEN, a missing mcpc install, a misspelled Actor ID, a failed run (with a pointer to the Apify console link in the error output), and timeouts, which it suggests resolving by reducing input size or raising the --timeout value.

Brand and marketing teams, social listening analysts, and anyone who needs a repeatable, guided way to pull reviews, ratings, or mentions from Google Maps, Booking.com, TripAdvisor, Facebook, Instagram, YouTube, or TikTok into a structured export without hand-writing an Actor call for each platform.