geo-brand-mentions

geo-brand-mentions is a skill for Claude Code from techhorizonlabs/thl-open. It costs 49 tokens per session (1,531 once invoked), scanned A, original, MIT.

A scanner that checks how often a brand is mentioned on websites and platforms that AI systems use to understand businesses and choose sources.

In plain words
What is it for?
Use it to review brand mentions across platforms, receive an overall authority score, and get platform-specific recommendations.
Why use it?
It shows which public references may support or weaken a brand's chance of appearing in AI-generated recommendations, without claiming that an AI system actually mentioned it.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is **Provenance (THL):** tag the score `[scan]` (data fetched this run), `[partial-scan]`, `[heuristic]` (judgement, no data), or `[unmeasured]` — and emit `—` ins.

Part of the thl-open plugin — 17 skills shipped together

Good fit Use it to review brand mentions across platforms, receive an overall authority score, and get platform-specific recommendations.

Compare 6 skills from other repositories ↓
Install

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.

Clone the repo
git clone --depth 1 https://github.com/techhorizonlabs/thl-open
agentmods
npx agentmods add skills/techhorizonlabs/thl-open/geo-brand-mentions

Made for: Claude Code.

Or install thl-open, the plugin that ships this one along with the rest of its 17 skills.

Wrote 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.

agentmods badge for geo-brand-mentions

README.md
[![agentmods](https://agentmods.dev/badge/skills/techhorizonlabs/thl-open/geo-brand-mentions/github.svg)](https://agentmods.dev/skills/techhorizonlabs/thl-open/geo-brand-mentions)
Your own site
<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.

agentmods 80×15 button for geo-brand-mentions

Your own site · 80×15
<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>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,531 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.01531
Opus 5 $0.00024 $0.00766
Sonnet 5 $0.00010 $0.00306
Haiku 4.5 $0.00005 $0.00153

Measured 12d ago against content hash ccad0d986518, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 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.

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.

skills/geo-brand-mentions/SKILL.md · 133 lines

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:

  1. YouTube (~0.737, strongest) — channel + third-party video/description/transcript mentions
  2. Reddit — subreddit discussion, recommendation threads, sentiment
  3. Wikipedia / Wikidata — the entity-recognition foundation
  4. LinkedIn — professional / B2B authority signals
  5. Other — Quora, Stack Overflow, GitHub, forums, news, podcasts (scored as one basket)

Read the full file on GitHub · 133 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 133 lines · 49 tokens per session scan A ccad0d986518

Subscribe to this mod's changes

geo-brand-mentions is a skill published in the GitHub repository techhorizonlabs/thl-open (16 stars, last pushed 3d ago), licensed MIT. It adds 49 tokens to every session and 1,531 once invoked, about $0.0002 per session on Opus 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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