Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add KSfak/Zubair-Trabzada-YouTube --skill geo-brand-mentionsgit clone --depth 1 https://github.com/KSfak/Zubair-Trabzada-YouTubeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ksfak/zubair-trabzada-youtube/geo-brand-mentions)<a href="https://agentmods.dev/skills/ksfak/zubair-trabzada-youtube/geo-brand-mentions"><img src="https://agentmods.dev/badge/skills/ksfak/zubair-trabzada-youtube/geo-brand-mentions/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ksfak/zubair-trabzada-youtube/geo-brand-mentions"><img src="https://agentmods.dev/badge/skills/ksfak/zubair-trabzada-youtube/geo-brand-mentions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.05469 |
| Opus 5 | $0.00024 | $0.02735 |
| Sonnet 5 | $0.00010 | $0.01094 |
| Haiku 4.5 | $0.00005 | $0.00547 |
Grade A, and why
geo-brand-mentions scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
from urllib.parse import quote_plus This is a copy
100% identical to geo-brand-mentions — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 481 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 481 lines · 49 tokens per session scan A 6a5d55c3e43e
geo-brand-mentions is a skill published in the GitHub repository KSfak/Zubair-Trabzada-YouTube (22 stars, last pushed 6mo ago), licensed MIT. It adds 49 tokens to every session and 5,469 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to geo-brand-mentions, differing in 0 lines, and is treated as a copy.
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