geo-brand-mentions

name	geo-brand-mentions
description	Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
allowed-tools	["Read","Grep","Glob","Bash","WebFetch","Write"]

Brand Mention Scanner Skill

Core Insight

Brand mentions correlate ~3x more strongly with AI visibility than traditional backlinks (Ahrefs, Dec 2025, 75K brands). Unlinked brand mentions predict AI citation better than Domain Rating or backlink count, and the platform matters enormously — a YouTube/Reddit mention outweighs a DR 70 dofollow link, because AI training data and retrieval disproportionately index high-engagement platforms.

This inverts traditional SEO: the signals that matter most for AI visibility (YouTube, Reddit) are nearly irrelevant in classic SEO, and vice versa.

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Composite Brand Authority Score

Scoring Formula

Platform	Weight	Rationale
YouTube Presence	25%	Strongest correlation with AI citation (0.737)
Reddit Presence	25%	Second strongest; critical for product recommendations
Wikipedia / Wikidata	20%	Entity recognition foundation; AI training data cornerstone
LinkedIn Authority	15%	Professional authority signals; B2B relevance
Other Platforms	15%	Supplementary: Quora, GitHub, news, forums, podcasts

Formula:

Brand_Authority_Score = (YouTube * 0.25) + (Reddit * 0.25) + (Wikipedia * 0.20) + (LinkedIn * 0.15) + (Other * 0.15)

Score Interpretation

Score Range	Rating	Interpretation
85-100	Dominant	Well-recognized entity across AI platforms. Highly likely to be cited and recommended.
70-84	Strong	Solid cross-platform presence. AI likely recognizes and cites for relevant queries.
50-69	Moderate	Presence on some platforms but gaps exist. AI citation is inconsistent.
30-49	Weak	Limited platform presence. AI may not recognize as a distinct entity.
0-29	Minimal	Negligible presence. AI unlikely to cite or recommend.

For per-platform 0-100 rubrics, why each platform matters, and what to check on each — see platforms.md.

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Analysis Procedure

Step 1: Identify Brand Information

Gather from the user or the website:

• Brand name (exact spelling + official variants)
• Founder/CEO name(s)
• Domain URL
• Industry/category
• Key products or services (top 3)
• Key competitors (for comparison context)

Step 2: Platform Scanning

For each platform, use WebFetch to search and assess presence.

YouTube Check:

1. Search: [brand name] site:youtube.com
2. Check: youtube.com/@[brand-name] or youtube.com/c/[brand-name] for official channel
3. Search: "[brand name]" site:youtube.com (exact match for mentions in descriptions)
4. Note: Channel subscriber count, video count, latest upload date, third-party mention count

Reddit Check:

1. Search: [brand name] site:reddit.com
2. Search: "[brand name]" site:reddit.com (exact match)
3. Check: reddit.com/r/[brand-name] for official subreddit
4. Check: reddit.com/user/[brand-name] for official account
5. Note: Thread count, dominant subreddits, sentiment (positive/negative/neutral), recommendation frequency

Wikipedia Check (IMPORTANT — use BOTH methods to avoid false negatives):

Method 1 — Python API check (MOST RELIABLE, do this FIRST):

python3 -c "
import requests, json
from urllib.parse import quote_plus
brand = '[Brand_Name]'
# Check Wikipedia API directly
api_url = f'https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch={quote_plus(brand)}&format=json'
r = requests.get(api_url, headers={'User-Agent': 'GEO-Audit/1.0'}, timeout=15)
data = r.json()
results = data.get('query', {}).get('search', [])
if results and brand.lower() in results[0].get('title', '').lower():
    print(f'WIKIPEDIA PAGE EXISTS: {results[0][\"title\"]}')
    print(f'URL: https://en.wikipedia.org/wiki/{results[0][\"title\"].replace(\" \", \"_\")}')
else:
    print('No direct Wikipedia page found')
# Check Wikidata
wd_url = f'https://www.wikidata.org/w/api.php?action=wbsearchentities&search={quote_plus(brand)}&language=en&format=json'
r2 = requests.get(wd_url, headers={'User-Agent': 'GEO-Audit/1.0'}, timeout=15)
wd = r2.json()
entities = wd.get('search', [])
if entities:
    print(f'WIKIDATA ENTRY: {entities[0].get(\"id\", \"\")} — {entities[0].get(\"description\", \"\")}')
"

Method 2 — Direct URL check (backup verification):

1. WebFetch: https://en.wikipedia.org/wiki/[Brand_Name] — check if page loads (not redirect to search)
2. WebFetch: https://en.wikipedia.org/wiki/[Founder_Name] for founder article

Method 3 — Search (least reliable, supplemental only):

1. Search: [brand name] site:wikipedia.org
2. Search: [brand name] site:wikidata.org

CRITICAL: Web search alone is NOT reliable for determining Wikipedia presence. ALWAYS run the Python API check first. If the API says a page exists, it exists — do not override with a search result that fails to find it.

5. Note: Article existence, quality, edit history, Wikidata completeness

LinkedIn Check:

1. Search: [brand name] site:linkedin.com
2. Check: linkedin.com/company/[brand-name] for company page
3. Note: Follower count, post frequency, employee count listed, engagement levels

Other Platforms:

1. Search: [brand name] site:quora.com
2. Search: [brand name] site:stackoverflow.com (if technical brand)
3. Search: [brand name] site:github.com (if technical brand)
4. Search: [brand name] site:news.ycombinator.com (Hacker News)
5. Search: "[brand name]" broadly for news mentions (filter to last 6 months)
6. Note: Presence/absence and quality of mentions on each platform

Step 3: Sentiment Assessment

For Reddit and discussion platforms, assess sentiment from the most recent and most prominent mentions. Use the sentiment indicator table in platforms.md (Positive / Neutral / Negative / Mixed).

Step 4: Competitive Comparison (Optional)

If competitors are identified, do a quick scan of their platform presence for context. A brand with "moderate" Reddit presence in an industry where competitors have zero Reddit presence is relatively strong — context calibrates the score.

Step 5: Score Calculation

1. Score each platform (0-100) using the rubrics in platforms.md.
2. Apply weights to compute the composite Brand Authority Score.
3. Identify the strongest and weakest platforms.
4. Generate specific, actionable recommendations for the weakest platforms — use the platform-specific quick-wins list in platforms.md.

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Output

Write a file named GEO-BRAND-MENTIONS.md using the exact template in templates.md (report header, platform breakdown table, per-platform detail blocks, recommendations by horizon, optional competitive table, key takeaway).