From brightdata-plugin
Collects live mentions of a brand across social platforms, news, and reviews using Bright Data, then classifies sentiment and clusters themes into a cited digest with recommendations.
How this skill is triggered — by the user, by Claude, or both
Slash command
/brightdata-plugin:brand-listeningThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Find out what people are *actually* saying about a brand across social platforms, news, and reviews — powered by live web data, not stale training knowledge. Combines the Bright Data CLI (`bdata`) for collection with a sentiment + theme analysis layer to deliver a cited, actionable digest.
Find out what people are actually saying about a brand across social platforms, news, and reviews — powered by live web data, not stale training knowledge. Combines the Bright Data CLI (bdata) for collection with a sentiment + theme analysis layer to deliver a cited, actionable digest.
Never answer brand-sentiment questions from training knowledge alone. Public sentiment changes daily. Always collect live mentions first, then classify and synthesize.
curl -fsSL https://cli.brightdata.com/install.sh | bash
bdata login # or: bdata login --device (SSH / headless)
Verify before collecting:
if ! command -v bdata >/dev/null 2>&1; then
echo "bdata CLI not installed — see skills/bright-data-best-practices/references/cli-setup.md"
elif ! bdata zones >/dev/null 2>&1; then
echo "bdata not authenticated — run: bdata login"
fi
Halt and route to setup if either check fails.
bdata search to find where the brand is being discussed, then bdata pipelines to pull structured mentions from each platform. Parallelize independent calls.bdata search "<brand> site:reddit.com" --json (and per-platform variants) to find threads, profiles, and articles, then feed those URLs to pipelines.bdata pipelines over bdata scrape whenever a pipeline exists for the platform — pipelines return clean structured JSON (author, date, engagement, text).--json when you need to parse or pipe output.bdata calls, not 50. Pull the highest-signal threads/profiles, not everything.Pick the platforms that fit the brand. Consumer/cultural brands skew TikTok/Instagram/Reddit; B2B/SaaS skews Reddit/X/review sites; local businesses skew Google Maps reviews.
# Discover relevant threads
bdata search "<brand> site:reddit.com" --json
bdata search "<brand> review reddit" --json
# Pull structured post + comment data from the threads found
bdata pipelines reddit_posts "<reddit-thread-url>" --json -o reddit.json
Reddit is the single best source for candid opinions brand channels hide. Prioritize it.
bdata search "<brand>" --json # find recent discussion
bdata pipelines x_posts "<x-profile-or-post-url>" --json -o x.json
bdata pipelines instagram_posts "https://www.instagram.com/<brand>/" --json -o ig_posts.json
bdata pipelines instagram_comments "<instagram-post-url>" --json -o ig_comments.json
bdata pipelines tiktok_posts "https://www.tiktok.com/@<brand>" --json -o tt_posts.json
bdata pipelines tiktok_comments "<tiktok-video-url>" --json -o tt_comments.json
bdata search "<brand> review youtube" --json
bdata pipelines youtube_videos "<video-url>" --json -o yt_videos.json
bdata pipelines youtube_comments "<video-url>" 100 --json -o yt_comments.json # url + num_comments
# App-based products
bdata pipelines google_play_store "<play-store-url>" --json -o play.json
bdata pipelines apple_app_store "<app-store-url>" --json -o appstore.json
# Local / physical businesses
bdata pipelines google_maps_reviews "<maps-url>" 90 --json -o gmaps.json # url + days_limit
# Facebook page reviews
bdata pipelines facebook_company_reviews "<fb-page-url>" 50 --json -o fb_reviews.json # url + num
# SaaS / software — discover then scrape (no pipeline)
bdata search "<brand> site:g2.com" --json
bdata search "<brand> site:capterra.com" --json
bdata scrape "<g2-or-capterra-url>"
bdata search "<brand>" --json # general SERP, scan for news
bdata scrape "<article-url>" # pull full article text for tone
Pipeline names change. Always confirm with
bdata pipelines listbefore hardcoding a type. Names are inconsistent across platforms (tiktok_postsplural,reddit_postsplural,x_posts). Thedata-feedsskill has the verified list.
| User says... | Collect from |
|---|---|
| "What are people saying about us / my brand" | Reddit + X + reviews + news (broad sweep) |
| "How did our launch land" / "buzz around X" | TikTok + Instagram + X + YouTube (recency-focused) |
| "Find complaints / what people hate" | Reddit + reviews (G2/Capterra/app stores) + YouTube comments |
| "Who's advocating for us / fans" | Instagram + TikTok + X (high-engagement positive posts) |
| "Reputation / sentiment over time" | Same sources, two windows — compare prior vs current |
| Local business reputation | Google Maps reviews + Facebook reviews |
Read references/sentiment-and-output.md for:
mixed; always report counts and percentages with the denominator).npx claudepluginhub brightdata/skills --plugin brightdata-pluginMonitors brand reputation and sentiment across Twitter/X, Reddit, Instagram, YouTube, and LinkedIn using the anysite MCP server. Tracks mentions, analyzes sentiment, and detects reputation risks.
Monitors public social media and news for brand/product/launch mentions across X, Reddit, YouTube, TikTok, Threads. Returns concise brief of themes and example posts.
Scrapes reviews, ratings, and brand mentions from Google Maps, Booking.com, TripAdvisor, Facebook, Instagram, YouTube, and TikTok using Apify Actors.