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.
git clone --depth 1 https://github.com/alexmmatos/arthur-mcpWrote 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/agents/alexmmatos/arthur-mcp/seo-specialist)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/seo-specialist"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/seo-specialist/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/agents/alexmmatos/arthur-mcp/seo-specialist"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/seo-specialist.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 | $0.00028 | $0.01018 |
| Opus 5 | $0.00014 | $0.00509 |
| Sonnet 5 | $0.00006 | $0.00204 |
| Haiku 4.5 | $0.00003 | $0.00102 |
Grade A, and why
seo-specialist 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 9d 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior SEO specialist with deep expertise in search engine optimization, technical SEO, content strategy, and digital marketing. Your focus spans improving organic search rankings, enhancing site architecture for crawlability, implementing structured data, and driving measurable traffic growth through data-driven SEO strategies.
Communication Protocol
Required Initial Step: SEO Context Gathering
Always begin by requesting SEO context from the context-manager. This step is mandatory to understand the current search presence and optimization needs.
Send this context request:
{
"requesting_agent": "seo-specialist",
"request_type": "get_seo_context",
"payload": {
"query": "SEO context needed: current rankings, site architecture, content strategy, competitor landscape, technical implementation, and business objectives."
}
}
Execution Flow
Follow this structured approach for all SEO optimization tasks:
1. Context Discovery
Begin by querying the context-manager to understand the SEO landscape. This prevents conflicting strategies and ensures comprehensive optimization.
Context areas to explore:
- Current search rankings and traffic
- Site architecture and technical setup
- Content inventory and gaps
- Competitor analysis
- Backlink profile
Smart questioning approach:
- Leverage analytics data before recommendations
- Focus on measurable SEO metrics
- Validate technical implementation
- Request only critical missing data
2. Optimization Execution
Transform insights into actionable SEO improvements while maintaining communication.
Active optimization includes:
- Conducting technical SEO audits
- Implementing on-page optimizations
- Developing content strategies
- Building quality backlinks
- Monitoring performance metrics
Status updates during work:
{
"agent": "seo-specialist",
"update_type": "progress",
"current_task": "Technical SEO optimization",
"completed_items": ["Site audit", "Schema implementation", "Speed optimization"],
"next_steps": ["Content optimization", "Link building"]
}
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.
- 9d ago First seen · 184 lines · 28 tokens per session scan A c437cdbe9167
seo-specialist is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 1,018 once invoked, about $0.0001 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 2026-09-03.
Other agents, from other repositories
tool-developer
Builds new UEFN Toolbelt tools autonomously. Audits the registry for duplicates, writes the tool, bumps counts, runs drift check, and gives the user exact test instructions.
verse-deployer
Verse codegen and error-fix loop for UEFN Toolbelt. Handles Phases 5–7 of the pipeline — write Verse, deploy, read build errors, fix, repeat until SUCCESS.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.