Brand listening

Skill brightdata/skills/skills/brand-listening

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".From its SKILL.md

Install

npx -y skills add brightdata/skills --skill brand-listening

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SKILL.md

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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

Bright Data CLI installed:

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

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

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)?
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.
Normalize — Collapse every raw result into the single mention schema in references/sentiment-and-output.md before any analysis.
Classify & cluster — Assign sentiment per mention (with a reason), then group mentions into themes. Follow the sentiment guardrails — never inflate either side.
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>" --json                  # general SERP, scan for news
bdata scrape "<article-url>"                    # pull full article text for tone

Pipeline names change. Always confirm with bdata pipelines list before hardcoding a type. Names are inconsistent across platforms (tiktok_posts plural, reddit_posts plural, x_posts). The data-feeds skill has the verified list.

Choosing What to Collect

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

Sentiment, Themes & Output

Read references/sentiment-and-output.md for:

The normalized mention record schema (use it as the row shape for both analysis and the dataset output).
The sentiment method + guardrails (classify from text as written; sarcasm/ambiguous → mixed; always report counts and percentages with the denominator).
Theme clustering — where the actionable insight lives.
Output templates — A (digest report), B (structured dataset), C (both).

Output Quality Standards

Every mention has a source URL — no unattributed claims.
Facts separate from interpretation — the quote is the fact; your sentiment label is interpretation. Keep them distinct.
Percentages always carry N — "61% positive of 142 classified mentions", never a bare percentage.
Be honest about gaps — list platforms that returned nothing this window. Note small samples that can't generalize.
Date-stamp everything — "Data collected on <date> · window: last <N> days".
End with "So what" — every report closes with 2–4 actionable recommendations, each grounded in a theme from the data. Raw mentions without interpretation are not a deliverable.