geo-brand-mentions
🤖 AI Summary
This skill scans for unlinked brand mentions across high-engagement platforms (like YouTube and Reddit) to predict and improve a brand's visibility in AI search citations, leveraging the finding that such mentions are 3x more predictive of AI visibility than traditional backlinks.
How to Install
Claude Code:
git clone --depth 1 https://github.com/zubair-trabzada/geo-seo-claude.git && cp geo-seo-claude/skills/geo-brand-mentions ~/.claude/skills/geo-brand-mentions -r# 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 |
Details
| Category | AI/ML → ml |
| Source | zubair-trabzada/geo-seo-claude |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 8.6K |
| Est. per Skill | 574 (shared across 15 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | N/A |
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