Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add bromso/metapowers/plugin install marketingWrote 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/bromso/metapowers/ai-seo)<a href="https://agentmods.dev/skills/bromso/metapowers/ai-seo"><img src="https://agentmods.dev/badge/skills/bromso/metapowers/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/bromso/metapowers/ai-seo"><img src="https://agentmods.dev/badge/skills/bromso/metapowers/ai-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 | $0.00077 | $0.00800 |
| Opus 5.5 | $0.00031 | $0.00320 |
| Sonnet 5.5 | $0.00015 | $0.00160 |
| Haiku 4.5 | $0.00008 | $0.00080 |
Grade A, and why
ai-seo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI SEO
Optimize "$ARGUMENTS" for AI-powered search — LLM citations, Google AI Overviews, ChatGPT, Perplexity, and other AI search surfaces.
Arguments. $ARGUMENTS may contain the project name and optional flags. Take the first token as <project> and treat --skip-checks as a flag; never include flags in file paths. All paths below use <project>. If --skip-checks is present, skip the prerequisite check and, before writing any artifact, append one line to .metapowers/marketing/<project>/skip-log.md: - <ISO timestamp> — <skill name> skipped prerequisite check for <artifact file> — reason: <reason or "none given">.
Prerequisites
Read .metapowers/marketing/<project>/00-strategy.md. If this file does not exist, tell the user:
Phase 0 (Strategy) has not been completed for "". Run
/marketing:customer-research <project>first, or use--skip-checksto bypass.
Process
-
Read context:
- Read
.metapowers/marketing/<project>/00-strategy.mdfor positioning and audience - Read
.metapowers/marketing/<project>/01-reach.mdif it exists (for content and SEO context) - Read
${CLAUDE_PLUGIN_ROOT}/shared/seo-checklist.mdfor AI search section
- Read
-
Audit current AI visibility:
- Use WebSearch to check if "" appears in AI-generated answers
- Test key queries in multiple AI search tools
- Note which competitors are being cited by AI
-
Analyze AI citation patterns:
- What content structure gets cited by LLMs? (lists, definitions, comparisons, data)
- Which domains are most frequently cited in the category?
- What makes content "LLM-friendly"? (clear structure, authoritative sources, direct answers)
-
Develop optimization strategy:
- Content structure recommendations (clear H2s, direct answers, FAQ format)
- Entity optimization (be clearly identifiable as an entity with consistent naming)
- E-E-A-T signals (experience, expertise, authority, trust markers)
- Structured data to aid AI understanding
- Citation-worthy content types (original research, data, tools, definitions)
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.
- 3d ago First seen · 56 lines · 77 tokens per session scan A b262549adeac
ai-seo is a skill published in the GitHub repository bromso/metapowers (2 stars, last pushed 3d ago), licensed MIT. It adds 77 tokens to every session and 800 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 2026-10-21.
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