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 Omar-Obando/qwen-orchestrator --skill seo-llmgit clone --depth 1 https://github.com/Omar-Obando/qwen-orchestratorWrote 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/omar-obando/qwen-orchestrator/seo-llm)<a href="https://agentmods.dev/skills/omar-obando/qwen-orchestrator/seo-llm"><img src="https://agentmods.dev/badge/skills/omar-obando/qwen-orchestrator/seo-llm/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/omar-obando/qwen-orchestrator/seo-llm"><img src="https://agentmods.dev/badge/skills/omar-obando/qwen-orchestrator/seo-llm.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 | $0.00079 | $0.04024 |
| Opus 5 | $0.00039 | $0.02012 |
| Sonnet 5 | $0.00016 | $0.00805 |
| Haiku 4.5 | $0.00008 | $0.00402 |
Grade A, and why
seo-llm 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.
How it starts
The opening of the file, as written. The whole thing — 614 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO LLM Skill — Search Engine Optimization for AI-Driven Search
Overview
This skill provides comprehensive guidance for optimizing content for LLM-powered search engines including ChatGPT, Perplexity, Gemini, Claude, Bing AI, and Qwen. It includes RAG optimization, prompt engineering for search visibility, semantic SEO, and ensuring content ranks highly in AI-driven search results. Based on prompt engineering best practices and RAG optimization techniques.
When to Use
Use this skill when:
- Optimizing content for ChatGPT search results
- Optimizing content for Perplexity AI search
- Optimizing content for Gemini search
- Optimizing content for Claude search
- Optimizing content for Bing AI search
- Optimizing content for Qwen search
- Implementing RAG optimization techniques
- Designing semantic SEO strategies for AI search
- Creating content that ranks in AI-powered search results
- Implementing prompt engineering for search visibility
- Optimizing for ChatGPT SEO (OpenAI's search)
- Optimizing for Perplexity Authority (authoritative sources)
- Optimizing for Gemini Rich Results (structured data)
- Optimizing for Claude Source Attribution (source credibility)
- Implementing semantic keyword targeting
- Optimizing for conversational search queries
- Creating content that AI search engines prioritize
- Implementing entity-based SEO for AI search
- Optimizing for voice search and natural language queries
- Creating content with clear answer structures for AI search
- Implementing schema markup for AI search visibility
- Optimizing for multi-turn conversation search
- Creating content that ranks in AI-powered knowledge graphs
Do NOT use this skill when:
- Optimizing for traditional search engines (Google, Bing, Yahoo) - use traditional SEO skills
- Designing database schema (use database-design skill)
- Creating UI components (use frontend-design skill)
- Implementing basic keyword targeting without AI context (use traditional SEO skill)
- Managing non-search-related content strategy (use content-strategy skill)
- Building LLM applications without search optimization needs (use llm-integrations skill)
- Creating technical documentation without search visibility requirements (use documentation-best-practices skill)
What ships with it
1 file 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.
- 7d ago First seen · 614 lines · 79 tokens per session scan A e6fd68fe66c7
seo-llm is a skill published in the GitHub repository Omar-Obando/qwen-orchestrator (49 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 4,024 once invoked, about $0.0004 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.
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