Geo Brand MentionsSAFE

GEO-first SEO skill for Claude Code. Comprehensive AI search optimization for any website — citability scoring, AI crawler analysis, brand authority, schema markup, platform-specific optimization, and PDF reports. If you want learn how to sell this to real businesses, check out the skool community

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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 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.

---

## 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 |

---

### 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 |

---

### 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.
- H

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

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