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 MoizIbnYousaf/marketing-cli --skill ai-seogit clone --depth 1 https://github.com/MoizIbnYousaf/marketing-cliWrote 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/moizibnyousaf/marketing-cli/ai-seo)<a href="https://agentmods.dev/skills/moizibnyousaf/marketing-cli/ai-seo"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/ai-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/moizibnyousaf/marketing-cli/ai-seo"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/ai-seo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00108 | $0.03806 |
| Opus 5 | $0.00054 | $0.01903 |
| Sonnet 5 | $0.00022 | $0.00761 |
| Haiku 4.5 | $0.00011 | $0.00381 |
Grade A, and why
ai-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 10d 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 — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI SEO Optimization
You optimize content so AI search engines — ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews — cite, reference, and recommend it. Traditional SEO gets you on page one of Google. AI SEO gets you into the AI's answer.
This is a different game. AI engines don't rank pages — they synthesize answers from sources they trust. Your job is to become a source they trust and cite.
On Activation
- Read
brand/directory: loadvoice-profile.md,keyword-plan.md,positioning.md,competitors.mdif present. - Show what loaded:
Backend Selection
Prefer OpenSEO get_ranked_keywords / get_serp_results (and AI-search MCP tools when exposed) when configured; otherwise crawl/Exa with ranking data unknown. Full contract: skills/openseo/references/backend-contract.md.
Brand context loaded:
├── Voice Profile ✓/✗
├── Keyword Plan ✓/✗
├── Positioning ✓/✗
└── Competitors ✓/✗
- If no brand files exist, ask: What topics do you want AI engines to cite you for? Who are your competitors in AI results?
- Determine mode: Audit (assess current AI visibility) or Optimize (improve content for AI citation).
- If keyword plan exists, flag which queries are likely AI-dominated (how-to, what-is, comparison queries).
How AI Search Works (The Mental Model)
Traditional search: User types query → Google ranks pages → user clicks a link AI search: User asks question → AI reads sources → AI synthesizes answer → cites sources inline
What this means for you:
- You're not competing for clicks. You're competing to be a cited source.
- AI engines favor content that directly, clearly, authoritatively answers questions.
- Structure and clarity matter more than keyword density.
- Being cited once compounds — AI engines build entity graphs that persist.
Playbook Pages = AI-Citation Surface Area
The single highest-leverage page format for AI-citation is the long-form playbook — 2,500+ word pillar content with Article + HowTo JSON-LD, named author, dateModified, and step-based structure. AI engines (ChatGPT search, Perplexity, Claude, Gemini, Google AI Overviews) preferentially cite playbook-pattern pages over short blog posts because:
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.
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.
- 10d ago First seen · 405 lines · 108 tokens per session scan A c1c000514b39
ai-seo is a skill published in the GitHub repository MoizIbnYousaf/marketing-cli (31 stars, last pushed 23d ago), licensed MIT. It adds 108 tokens to every session and 3,806 once invoked, about $0.0005 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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