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 dalroot/agentic-seo --skill brand-monitoringgit clone --depth 1 https://github.com/dalroot/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/dalroot/agentic-seo/brand-monitoring)<a href="https://agentmods.dev/skills/dalroot/agentic-seo/brand-monitoring"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/brand-monitoring/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/dalroot/agentic-seo/brand-monitoring"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/brand-monitoring.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 | $0.00071 | $0.00813 |
| Opus 5.5 | $0.00028 | $0.00325 |
| Sonnet 5.5 | $0.00014 | $0.00163 |
| Haiku 4.5 | $0.00007 | $0.00081 |
Grade A, and why
brand-monitoring 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 today.
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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategies: Brand Monitoring
Guides ongoing brand monitoring—detecting impersonation, trademark infringement, counterfeit products, and brand abuse before they cause harm. Complements brand-protection (reactive: report, takedown); this skill covers proactive monitoring setup and tool selection.
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.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read for brand name, official domain, and key assets.
Identify:
- Scope: Domain, social, marketplaces, paid search, dark web
- Budget: Manual vs automated; DIY vs vendor
- Risk level: High-value brand, prior incidents, or preventive
What to Monitor
| Channel | Threats |
|---|---|
| Domains | Typosquatting, brand+ai, brand+app, impersonation sites |
| Social media | Fake accounts, impersonation, unauthorized use |
| Marketplaces | Counterfeit products, unauthorized sellers (Amazon, eBay, Temu) |
| Paid search | Competitors bidding on brand terms; impersonator ads |
| App stores | Fake apps, trademark misuse |
| Web | Phishing sites, spoofed pages |
Manual Monitoring (Low Cost)
| Method | Frequency |
|---|---|
| Search | Brand name + variants (brand+ai, brand+app, brand+official) |
| Google Alerts | Brand name, product names |
| Social search | X, LinkedIn, Instagram for brand mentions |
| Marketplace search | Amazon, eBay for counterfeit listings |
Tip: Document findings; escalate to brand-protection for takedown when infringement is confirmed.
Automated Tools (Scale)
| Capability | Description |
|---|---|
| AI detection | Machine learning, image recognition, NLP to detect abuse across channels |
| Multi-channel | Domains, social, marketplaces, paid search, dark web |
| Enforcement | Case management, takedown workflows, platform integrations |
| Trademark watch | USPTO, trademark office monitoring; litigation insights |
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.
- today First seen · 76 lines · 71 tokens per session scan A 13828629e0c8
brand-monitoring is a skill published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 71 tokens to every session and 813 once invoked, about $0.0003 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 2026-10-26.
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