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 T4wroot/agentic-seo --skill multi-domain-brand-seogit clone --depth 1 https://github.com/T4wroot/agentic-seoWrote 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/t4wroot/agentic-seo/multi-domain-brand-seo)<a href="https://agentmods.dev/skills/t4wroot/agentic-seo/multi-domain-brand-seo"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/multi-domain-brand-seo/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/t4wroot/agentic-seo/multi-domain-brand-seo"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/multi-domain-brand-seo.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 | $0.00098 | $0.01265 |
| Opus 5 | $0.00049 | $0.00633 |
| Sonnet 5 | $0.00020 | $0.00253 |
| Haiku 4.5 | $0.00010 | $0.00127 |
Grade A, and why
multi-domain-brand-seo 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 7d 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.
This is a copy
100% identical to multi-domain-brand-seo — 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO: Multi-Domain Brand Search
When a company has multiple domains (e.g., company.com and product.ai), ensure the company/main site ranks first for brand queries. Product sites focus on product keywords and do not compete for brand position. See domain-architecture for structure decisions; rebranding-strategy for domain change and migration.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Typical Scenarios
| Scenario | Description |
|---|---|
| Multiple domains | Company main site (company.com), product site (product.ai / product.io) |
| Brand query competition | Product site or third-party (Crunchbase, LinkedIn, reviews) may outrank main site for brand |
| Entity confusion | Legacy brands, sub-brands, directories dilute brand perception |
| Goal | Brand queries → company.com first; product.ai → product keywords only |
Hub-Spoke Model
| Role | Domain | Responsibility |
|---|---|---|
| Hub | company.com | Brand #1; About, Research, ecosystem, product matrix |
| Spoke | product.ai | Product keywords, features, pricing; visible "by [Company]" and link back to company.com |
Differentiation
| Dimension | Hub (company.com) | Spoke (product.ai) |
|---|---|---|
| Audience | Investors, partners, media, developers | Product users, prospects |
| Keywords | Brand name, company name, industry | Product features, use cases |
| Content | Mission, About, Research, Events, product matrix | Features, Use Cases, Pricing, Sign up |
| Conversion | Contact, Waitlist, Early Access | Sign up, Try free, Pricing |
Avoid Cannibalization
- Hub does not target Spoke product keywords (e.g., "virtual staging," "AI design tool")
- Spoke does not target Hub brand keywords (Title avoids brand-only; add product description)
- Internal links: Hub → Spoke (Products); Spoke → Hub (About, Footer)
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.
- 7d ago First seen · 119 lines · 98 tokens per session scan A b920c4e15b87
multi-domain-brand-seo is a skill published in the GitHub repository T4wroot/agentic-seo (15 stars, last pushed 8d ago), licensed MIT. It adds 98 tokens to every session and 1,265 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to multi-domain-brand-seo, differing in 0 lines, and is treated as a copy.
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