Geo Brand Mentions

Brand Mention Scanner Skill

Core Insight

Brand mentions correlate approximately 3x more strongly with AI visibility than traditional backlinks. An Ahrefs study published in December 2025, analyzing 75,000 brands across AI search platforms, found that unlinked brand mentions -- references to a brand name without a hyperlink -- are a stronger predictor of whether AI systems cite and recommend a brand than Domain Rating or backlink count.

The critical finding: the platform where the mention appears matters enormously. Not all mentions are equal. A mention on YouTube or Reddit carries far more weight for AI citation than a mention on a low-authority blog, because AI training data and retrieval systems disproportionately index high-engagement platforms.

This inverts a core assumption of traditional SEO. In traditional SEO, a backlink from a high-DR site is the gold standard. In GEO, an unlinked mention on Reddit or a YouTube video description may be more valuable than a dofollow backlink from a DR 70 blog.

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Platform Importance Ranking for AI Citations

Based on the Ahrefs December 2025 study and corroborating research from Profound (2025) and Terakeet (2025):

1. YouTube Mentions -- Correlation ~0.737 (STRONGEST)

Why YouTube matters most:

• YouTube is the second-largest search engine and the largest video platform globally (2.5B+ monthly users).
• AI training datasets heavily incorporate YouTube transcripts, descriptions, and metadata.
• Google's Gemini and AI Overviews directly reference YouTube content.
• Perplexity and ChatGPT both index and cite YouTube video content.
• YouTube transcripts are particularly valuable because they contain natural language mentions in conversational context, which aligns with how AI models process and generate text.

What to check:

• Brand YouTube channel: Does the brand have an active YouTube channel? How many subscribers? Video count? Upload frequency?
• Third-party video mentions: Are other YouTubers or channels mentioning the brand? In what context (reviews, tutorials, comparisons)?
• Video descriptions: Does the brand name appear in video descriptions of industry-relevant content?
• Video transcripts: Is the brand mentioned in spoken content of relevant videos? (AI models index transcripts)
• YouTube search presence: When searching "[brand name]" on YouTube, do results appear? Are they positive?
• Comment mentions: Is the brand mentioned in comments on relevant industry videos?

Scoring for YouTube (0-100):

| Score | Criteria | |---|---| | 90-100 | Active channel with 10K+ subscribers, regular uploads, brand mentioned in 20+ third-party videos, appears in YouTube search results for industry terms | | 70-89 | Active channel with 1K+ subscribers, brand mentioned in 10-19 third-party videos, some YouTube search presence | | 50-69 | Channel exists with some content, brand mentioned in 5-9 third-party videos, limited YouTube search presence | | 30-49 | Channel exists but inactive, brand mentioned in 1-4 third-party videos | | 10-29 | No channel or empty channel, brand mentioned in 1-2 videos only | | 0-9 | No YouTube presence whatsoever |

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2. Reddit Mentions -- High Correlation

Why Reddit matters:

• Reddit is one of the most heavily indexed platforms in AI training data (confirmed in Google's $60M/year Reddit licensing deal, 2024).
• AI systems heavily weight Reddit for product recommendations, comparisons, and user sentiment.
• "Reddit" is now appended to an estimated 10-15% of Google searches by users seeking authentic opinions.
• Perplexity frequently cites Reddit threads as sources.
• ChatGPT and Claude both reference Reddit discussions when answering product/service questions.

What to check:

• Subreddit presence: Is the brand discussed in relevant subreddits? Which ones?
• Mention volume: How many Reddit threads mention the brand? What is the trend (increasing/decreasing)?
• Sentiment: Are mentions mostly positive, negative, or neutral? What are common praise points and complaints?
• Official presence: Does the brand have an official Reddit account? Do they participate in discussions? Have they done AMAs?
• Recommendation threads: Does the brand appear in "What do you recommend for X?" threads? Is it the top recommendation or an also-ran?
• Subreddit community: Does the brand have its own subreddit? How active is it?

Scoring for Reddit (0-100):

| Score | Criteria | |---|---| | 90-100 | Frequently recommended in relevant subreddits, predominantly positive sentiment, active official presence, own subreddit with 5K+ members, appears in top recommendations for industry queries | | 70-89 | Regularly mentioned in relevant subreddits, mostly positive sentiment, some official presence, appears in multiple recommendation threads | | 50-69 | Mentioned in several relevant threads, mixed sentiment, brand name is recognized by community members | | 30-49 | Occasional mentions, limited to 1-2 subreddits, no official presence | | 10-29 | Rare mentions, brand largely unknown on Reddit | | 0-9 | No Reddit presence |

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3. Wikipedia Presence -- High Correlation

Why Wikipedia matters:

• Wikipedia is one of the highest-authority sources in AI training data. All major AI models have been trained on Wikipedia dumps.
• AI systems use Wikipedia as a primary source for entity recognition -- determining whether a brand is a "real" entity worth knowing about.
• Wikidata (Wikipedia's structured data sibling) provides machine-readable facts that AI models use for knowledge graph construction.
• Having a Wikipedia page is a strong signal of notability, which correlates with AI systems treating the brand as an authoritative entity.

What to check:

• Wikipedia page: Does the brand or company have its own Wikipedia article? Is it marked for deletion or quality issues?
• Founder page: Does the founder/CEO have a Wikipedia page? (Strong authority signal)
• Wikipedia citations: Is the brand's website cited as a reference in any Wikipedia articles?
• Wikidata entry: Does the brand have a Wikidata item (Q-number)? How complete is it?
• Wikipedia mentions: Is the brand mentioned in other Wikipedia articles (industry articles, competitor pages, category pages)?
• Article quality: If a Wikipedia page exists, is it a stub, start-class, or higher quality?

Scoring for Wikipedia (0-100):

| Score | Criteria | |---|---| | 90-100 | Detailed Wikipedia article (B-class or higher), Wikidata entry with complete properties, brand cited as reference in multiple articles, founder has Wikipedia page | | 70-89 | Wikipedia article exists (start-class or higher), Wikidata entry exists, brand mentioned in 2+ other Wikipedia articles | | 50-69 | Wikipedia article exists (stub or start), basic Wikidata entry, limited mentions in other articles | | 30-49 | No Wikipedia article but brand is mentioned in other articles or cited as reference; Wikidata entry may exist | | 10-29 | Brand mentioned in 1-2 Wikipedia articles as a passing reference only | | 0-9 | No Wikipedia or Wikidata presence of any kind |

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4. LinkedIn Presence -- Moderate Correlation

Why LinkedIn matters:

• LinkedIn content is increasingly indexed by AI systems for professional and B2B context.
• Company LinkedIn pages and employee thought leadership posts build brand entity signals.
• AI models reference LinkedIn for company information, team credentials, and professional authority.
• LinkedIn articles and posts are indexed by search engines and AI crawlers.

What to check:

• Company page: Does the brand have a LinkedIn company page? Follower count? Post frequency?
• Employee thought leadership: Are employees (especially leadership) posting thought leadership content that mentions the brand?
• Company mentions: Is the brand mentioned in LinkedIn posts by non-employees? Industry analysts? Customers?
• LinkedIn articles: Are there long-form LinkedIn articles about or mentioning the brand?
• Employee profiles: Do employees list the company with detailed descriptions? Do they have strong professional profiles?
• Engagement metrics: What is the typical engagement (likes, comments, shares) on company posts?

Scoring for LinkedIn (0-100):

| Score | Criteria | |---|---| | 90-100 | Active company page with 10K+ followers, leadership regularly posts thought leadership, brand frequently mentioned by industry professionals, strong employee profiles | | 70-89 | Active company page with 5K+ followers, some employee thought leadership, occasional third-party mentions | | 50-69 | Company page exists with 1K+ followers, irregular posting, limited third-party mentions | | 30-49 | Company page exists but is sparse or inactive, few followers, no third-party mentions | | 10-29 | Basic company page with minimal information | | 0-9 | No LinkedIn company page |

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5. Other Platform Presence -- Supplementary

These platforms have lower but still meaningful correlation with AI visibility:

Quora

• Relevance: Quora answers are frequently included in AI training data and cited by Perplexity.
• What to check: Is the brand mentioned in Quora answers to industry-relevant questions? Does the brand have an official Quora presence?
• Signal strength: Moderate for B2C, lower for B2B.

Stack Overflow / Stack Exchange

• Relevance: Critical for developer-facing brands (SaaS, dev tools, APIs).
• What to check: Is the brand's product discussed in Stack Overflow questions/answers? Does the brand have a tag? Do they have an official account answering questions?
• Signal strength: High for technical products, irrelevant for most B2C.

GitHub

• Relevance: Critical for open-source and developer-focused brands.
• What to check: Does the brand have a GitHub organization? Stars on repositories? Mentions in other repos' documentation or discussions?
• Signal strength: High for dev tools and open-source, low for non-technical brands.

Industry Forums and Communities

• Relevance: Niche authority signals that AI models pick up from domain-specific training data.
• What to check: Is the brand discussed in industry-specific forums (e.g., Hacker News for tech, ProductHunt for startups, industry-specific Slack communities)?
• Signal strength: Moderate, but valuable for establishing niche authority.

News and Press

• Relevance: News mentions build entity authority and recency signals.
• What to check: Has the brand been covered by major news outlets or industry publications? How recently? What was the context?
• Signal strength: Moderate. Recency matters -- a mention in the last 6 months is far more valuable than one from 3 years ago.

Podcasts

• Relevance: Growing AI training data source. Transcripts are increasingly indexed.
• What to check: Has the brand or its leadership appeared on podcasts? Are podcast transcripts mentioning the brand indexed by search engines?
• Signal strength: Moderate and growing.

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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 correlation; 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 signals from 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 | Brand is a well-recognized entity across AI platforms. Highly likely to be cited and recommended by AI systems. | | 70-84 | Strong | Brand has solid cross-platform presence. AI systems likely recognize and cite it for relevant queries. | | 50-69 | Moderate | Brand has presence on some platforms but gaps exist. AI citation is inconsistent. | | 30-49 | Weak | Brand has limited platform presence. AI systems may not recognize it as a distinct entity. | | 0-29 | Minimal | Brand has negligible platform presence. AI systems are unlikely to cite or recommend it. |

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Analysis Procedure

Step 1: Identify Brand Information

Gather the following from the user or from the website:

• Brand name (exact spelling, including any 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):

GEO_ROOT="${CODEX_GEO_ROOT:-}"
if [ -z "$GEO_ROOT" ]; then
  for candidate in "$PWD" "$PWD/.." "$PWD/../.." "${CODEX_SKILLS_DIR:-${CODEX_HOME:-$HOME/.codex}/skills}/geo"; do
    if [ -f "$candidate/scripts/brand_scanner.py" ]; then
      GEO_ROOT="$candidate"
      break
    fi
  done
fi
GEO_RUNTIME_DIR="${CODEX_GEO_RUNTIME_DIR:-${CODEX_HOME:-$HOME/.codex}/geo-seo-codex}"
if [ -x "$GEO_ROOT/.venv/bin/python" ]; then
  GEO_PYTHON="$GEO_ROOT/.venv/bin/python"
elif [ -x "$GEO_ROOT/.venv/Scripts/python.exe" ]; then
  GEO_PYTHON="$GEO_ROOT/.venv/Scripts/python.exe"
elif [ -x "$GEO_RUNTIME_DIR/.venv/bin/python" ]; then
  GEO_PYTHON="$GEO_RUNTIME_DIR/.venv/bin/python"
elif [ -x "$GEO_RUNTIME_DIR/.venv/Scripts/python.exe" ]; then
  GEO_PYTHON="$GEO_RUNTIME_DIR/.venv/Scripts/python.exe"
else
  GEO_PYTHON="python3"
fi
if [ -z "$GEO_ROOT" ]; then
  echo "GEO runtime root not found. Set CODEX_GEO_ROOT to the geo-seo-codex plugin/repo root."
  exit 1
fi
"$GEO_PYTHON" -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 the page loads (not a redirect to search)
2. WebFetch: https://en.wikipedia.org/wiki/[Founder_Name] for founder article

**

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: bytefer
• Source: bytefer/geo-seo-codex
• License: MIT
• Homepage: https://www.bestalternative.dev/

Install and usage instructions live in the source repository linked above.