/brand-listening

name: brand-listening
description: >
  Social listening and brand reputation research using Bright Data's web
  scraping infrastructure. Collects what real people are saying about a brand,
  product, or person across Reddit, X/Twitter, Instagram, TikTok, YouTube,
  news, and review sites — then classifies sentiment, clusters themes, and
  delivers a cited digest with actionable recommendations. Use this skill when
  the user wants to know what people are saying about their brand, monitor
  social media mentions, gauge public sentiment, track online reputation,
  find complaints or advocacy, measure buzz around a launch, or do social
  listening / brand monitoring / sentiment analysis. Also use when the user
  mentions brand mentions, brand health, reputation tracking, or "what's the
  internet saying about us".

Brand Listening

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.

Prerequisites

1. Bright Data CLI installed:

   curl -fsSL https://cli.brightdata.com/install.sh | bash

2. One-time login completed:

   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.

Core Workflow

1. **Clarify scope** — Which brand/product/person? Which platforms? What time window (default: last 30 days)? What does the user want to *do* with it (general health check, launch monitoring, complaint triage, advocacy hunting)? 2. **Discover, then collect** — Use `bdata search` to find where the brand is being discussed, then `bdata pipelines` to pull structured mentions from each platform. Parallelize independent calls. 3. **Normalize** — Collapse every raw result into the single mention schema in [references/sentiment-and-output.md](references/sentiment-and-output.md) before any analysis. 4. **Classify & cluster** — Assign sentiment per mention (with a reason), then group mentions into themes. Follow the sentiment guardrails — never inflate either side. 5. **Deliver** — Produce the cited digest (Output A), optionally with the structured dataset (Output B). Every report ends with a "So what — recommendations" section.

Data Collection Rules

• **Discovery first.** You rarely have the right URLs up front. Run `bdata search "<brand> site:reddit.com" --json` (and per-platform variants) to find threads, profiles, and articles, *then* feed those URLs to pipelines.
• **Prefer `bdata pipelines`** over `bdata scrape` whenever a pipeline exists for the platform — pipelines return clean structured JSON (author, date, engagement, text).
• **Always pass `--json`** when you need to parse or pipe output.
• **Be cost-efficient** — a standard sweep is ~6–12 `bdata` calls, not 50. Pull the highest-signal threads/profiles, not everything.
• **Parallelize** independent calls across multiple Bash tool calls in one response.
• **Every mention needs a source URL.** No unattributed quotes, ever.
• **Never fabricate sentiment or fill gaps.** If a platform returns nothing, report it in "Gaps & caveats".

Platform Modules

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.

Reddit — honest, unfiltered sentiment

# 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.

X / Twitter — real-time reaction

bdata search "<brand>" --json                      # find recent discussion
bdata pipelines x_posts "<x-profile-or-post-url>" --json -o x.json

Instagram — brand aesthetics, comments, advocacy

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

TikTok — cultural relevance, viral sentiment

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

YouTube — reviews, tutorials, long-form opinion (comments are gold)

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

Reviews — structured customer sentiment

# 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>"

News & press — coverage and tone

bdata search "<brand>"