How to Scan for Brand Mentions Across AI-Cited Platforms Using GEO-SEO Claude

The GEO-SEO Claude repository includes a dedicated Brand Mention Scanner that evaluates brand visibility across high-authority platforms to predict and improve AI-driven citation likelihood.

Brands seeking to optimize for generative engine optimization (GEO) must monitor their presence on platforms that AI models frequently cite. The zubair-trabzada/geo-seo-claude repository provides a systematic approach to scan for brand mentions across AI-cited platforms, implementing correlation-weighted scoring based on empirical research from 75,000 brand analyses.

Architecture of the Brand Mention Scanner

The scanner operates as a specialized sub-skill within the repository's modular architecture, routing commands through the skill orchestrator to platform-specific detection functions.

Skill Orchestration and Routing

The geo-brand-mentions skill, defined in skills/geo-brand-mentions/SKILL.md, serves as the entry point for brand visibility audits. The repository's architecture diagram in README.md positions this skill among thirteen specialized sub-skills under the GEO framework. When invoked, the orchestrator routes execution to scripts/brand_scanner.py, which contains the core detection logic and report generation capabilities.

Core Detection Functions

The brand_scanner.py script implements discrete functions for each high-impact platform:

• check_youtube_presence() (lines 35-66): Evaluates channel existence and content presence
• check_reddit_presence() (lines 70-99): Assesses brand mentions across subreddits
• check_wikipedia_presence() (lines 104-56): Verifies entity pages and citations
• check_linkedin_presence() (lines 60-90): Checks company page existence
• check_other_platforms() (lines 94-28): Monitors additional citation sources

Each function returns a structured dictionary containing detection instructions, correlation weights, and platform-specific recommendations.

Platform Prioritization Based on Citation Correlation

Not all platforms contribute equally to AI citation likelihood. The scanner incorporates quantitative correlation weights derived from an Ahrefs study (December 2025) analyzing 75,000 brands.

High-Impact Platforms

YouTube carries the strongest correlation coefficient (0.737) with AI citations, followed closely by Reddit. These platforms function as primary training data sources for large language models, making presence detection critical for GEO strategies.

Wikipedia and LinkedIn provide secondary but significant signals, particularly for entity recognition and authority establishment. The correlation values are hardcoded in the script header (lines 8-13) and propagated throughout the report generation process.

Executing Brand Mention Scans

The scanner supports both command-line invocation and programmatic integration, generating standardized JSON reports suitable for downstream processing.

Command-Line Interface

Run the scanner directly via CLI to audit a specific brand:

python scripts/brand_scanner.py "Acme Corp" acmecorp.com

This executes the full platform check suite and outputs a formatted JSON report containing detection results, correlation scores, and prioritized recommendations.

Programmatic Integration

Embed the scanner within existing Python workflows by importing the report generator:

from scripts.brand_scanner import generate_brand_report

report = generate_brand_report("Acme Corp", "acmecorp.com")

# Process report for PDF generation or dashboard display

The generate_brand_report() function (lines 33-64) aggregates per-platform results into a cohesive analysis object ready for consumption by the geo-report and geo-report-pdf sub-skills.

Understanding Scanner Output Structure

The JSON report contains two primary sections: a nested platforms object and an overall_recommendations array.

The platforms object includes entries for YouTube, Reddit, Wikipedia, LinkedIn, and other sources, each containing:

• correlation: Numerical weight (e.g., 0.737 for YouTube)
• weight: Percentage representation of total impact (e.g., "25%")
• search_url: Direct link for manual verification
• check_instructions: Step-by-step validation guidelines

The overall_recommendations array provides prioritized action items, ranking platforms by their AI citation influence to guide resource allocation.

Integration with AI Visibility Workflows

The brand scanner connects to broader GEO strategies through the geo-ai-visibility agent defined in agents/geo-ai-visibility.md. This agent coordinates citability assessments, crawler configurations, and brand mention audits into unified visibility reports.

Because the scanner builds search URLs and verification checklists rather than requiring API authentication, it operates effectively in environments with restricted access credentials while still providing actionable intelligence.

Summary

• The zubair-trabzada/geo-seo-claude repository implements brand mention scanning through scripts/brand_scanner.py and the geo-brand-mentions skill.
• Platform-specific functions (check_youtube_presence, check_reddit_presence, etc.) evaluate visibility across five critical channels.
• YouTube (correlation: 0.737) and Reddit provide the strongest AI citation signals according to the embedded scoring methodology.
• The scanner outputs standardized JSON reports via generate_brand_report(), consumable by PDF generators and dashboard systems.
• Manual verification URLs and checklists enable operation without API keys, making the tool accessible for immediate deployment.

Frequently Asked Questions

What platforms does the brand scanner monitor?

The scanner evaluates five platform categories: YouTube, Reddit, Wikipedia/Wikidata, LinkedIn, and additional "Other" platforms. Each category receives specific detection logic in scripts/brand_scanner.py, with YouTube and Reddit receiving prioritized weighting due to their high correlation with AI citation patterns.

How does the scanner calculate AI citation correlation scores?

Correlation scores derive from an Ahrefs study (December 2025) analyzing 75,000 brands, with values hardcoded in the script header (lines 8-13). YouTube receives a correlation coefficient of 0.737, representing the strongest predictor of AI citation likelihood among monitored platforms.

Can I run the brand scanner without API keys?

Yes. The scanner constructs manual search URLs (e.g., https://www.youtube.com/results?search_query={brand}) and provides detailed checklists for human verification. This design eliminates API dependency while maintaining audit functionality, though automated API integration could enhance throughput in enterprise environments.

How do I integrate the scanner output into existing workflows?

Import generate_brand_report from scripts/brand_scanner.py to capture JSON output programmatically. The resulting report feeds directly into the geo-report and geo-report-pdf sub-skills for client documentation, or you can pipe the JSON into custom dashboard widgets and analytics pipelines.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}