Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/techhorizonlabs/thl-opennpx agentmods add skills/techhorizonlabs/thl-open/geo-brand-mentionsWrote 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/techhorizonlabs/thl-open/geo-brand-mentions)<a href="https://agentmods.dev/skills/techhorizonlabs/thl-open/geo-brand-mentions"><img src="https://agentmods.dev/badge/skills/techhorizonlabs/thl-open/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/techhorizonlabs/thl-open/geo-brand-mentions"><img src="https://agentmods.dev/badge/skills/techhorizonlabs/thl-open/geo-brand-mentions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01529 |
| Opus 5.5 | $0.00020 | $0.00612 |
| Sonnet 5.5 | $0.00010 | $0.00306 |
| Haiku 4.5 | $0.00005 | $0.00153 |
Grade A, and why
geo-brand-mentions scanned grade A with 0 findings 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 17d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 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 more strongly with AI visibility than traditional backlinks. An Ahrefs
brand study (2025, ~75,000 brands, cited as reported; see docs/SOURCES.md)
found unlinked brand mentions (references to a brand name with no hyperlink) predict whether
AI systems cite and recommend a brand better than Domain Rating or backlink count.
This measures off-page authority signals, not answer-engine outcomes. Wikipedia/Wikidata are checked live via their APIs; the other platforms are assessed via search, not by querying the AI engines. A high Brand Authority Score means the signals AI trusts are present. It does not confirm any engine actually names you. For that live check, run the free scan at areyoufoundbyai.com (two buyer questions on ChatGPT and Gemini; the paid measure covers all seven engines).
The critical finding: the platform the mention sits on matters enormously. A mention on YouTube or Reddit carries far more weight for AI citation than one on a low-authority blog, because AI training data and retrieval systems disproportionately index high-engagement platforms.
This inverts a core SEO assumption. In SEO, a backlink from a high-DR site is the gold standard. In GEO, an unlinked mention on Reddit or in a YouTube description may be worth more than a dofollow backlink from a DR 70 blog.
Platforms that matter
AI systems weight a handful of platforms far above backlinks. Each platform's rationale, scan
recipe, and 0-100 scoring rubric live in references/platforms.md.
Read it before scoring. Ranked by correlation with AI citation:
- YouTube (~0.737, strongest): channel + third-party video/description/transcript mentions
- Reddit: subreddit discussion, recommendation threads, sentiment
- Wikipedia / Wikidata: the entity-recognition foundation
- LinkedIn: professional / B2B authority signals
- Other: Quora, Stack Overflow, GitHub, forums, news, podcasts (scored as one basket)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 17d ago Changed 8f28f04d5082
- 1mo ago First seen · 133 lines · 49 tokens per session scan A ccad0d986518
geo-brand-mentions is a skill published in the GitHub repository techhorizonlabs/thl-open (18 stars, last pushed 18d ago), licensed MIT. It adds 49 tokens to every session and 1,529 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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