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 Hainrixz/claude-seo-ai/plugin install claude-seo-aiWrote 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/hainrixz/claude-seo-ai/fix)<a href="https://agentmods.dev/skills/hainrixz/claude-seo-ai/fix"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-seo-ai/fix/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/hainrixz/claude-seo-ai/fix"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-seo-ai/fix.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 | $0.00132 | $0.03050 |
| Opus 5 | $0.00066 | $0.01525 |
| Sonnet 5 | $0.00026 | $0.00610 |
| Haiku 4.5 | $0.00013 | $0.00305 |
Grade A, and why
fix 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 2d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fix (opt-in writer)
disable-model-invocation: true means the model can never trigger this on its own — only the
user running /claude-seo-ai:fix. Writes happen only through the seo-fixer-writer subagent (the
one agent with Write/Edit) and only after explicit confirmation.
--category filters the report's findings and matches on a finding's module id (M5, M7, M17, …),
its axis (search, ai, or both — which matches both of the other two), its fixable class
(auto, proposed, advisory), or its scope (page, site, …), case-insensitively. It is not
a topic vocabulary and it does not split on commas: repeat the flag to widen the filter
(--category M5 --category M17). Translate what the user asks for into those words — "fix my schema"
is --category M5, "just the safe ones" is --category auto — and say which filter you applied.
--target accepts the adapter ids plus the aliases local, shopify, wordpress, manual, and the
provider names webflow/wix/ghost/hubspot/bigcommerce (each expands to page-api).
Two absolutes, before anything else:
- Never fix from memory. Every change comes from a persisted
report.jsonon disk. If there is no report for this target, run the audit first — do not reconstruct findings from the transcript. - You never write. This thread runs read-only ops (
capabilities,plan,preview,verify).apply,publishandrollbackbelong to the writer subagent, and each needs a ticket.
Fixability classes (from each finding's fixable field — see schema/finding.schema.json)
- AUTO — deterministic, additive, machine-verifiable, low-semantic-risk. May be written (with
diff + confirmation): meta
viewport/charset/<html lang>; Tier-1 JSON-LD blocks;sameAs/@id/dateModified(from confirmed inputs only); robots.txt AI-crawler presets +Sitemap:line; self-referential canonical; hreflang link sets; OG/Twitter cards; imagewidth/height; XML sitemap entries;llms.txt(disclosure-gated, scored 0). - PROPOSED — changes prose or meaning; draft the diff and require a per-item accept: generated
<title>and meta description, answer-block/TL;DR rewrites, internal-link insertions, heading restructuring, generated image alt text. - ADVISORY — never written: content/E-E-A-T rewrites, added stats or citations, Core Web Vitals, rendering strategy, redirects/status codes, link-building, Merchant Center/GBP backend data.
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.
- 2d ago Changed · +124 lines 97a31906c3e8
- 4d ago Changed · +3 lines · +33 tokens per session 73771259126d
- 10d ago First seen · 33 lines · 99 tokens per session scan A 9618355cda22
fix is a skill published in the GitHub repository Hainrixz/claude-seo-ai (58 stars, last pushed 3d ago), licensed MIT. It adds 132 tokens to every session and 3,050 once invoked, about $0.0007 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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Use when asked to audit a site's SEO, check AEO or answer-engine readiness, diagnose why a page is not ranking or not being cited by AI, verify structured data, or run pre-deploy discoverability checks on built HTML.
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Audit or generate web content optimized for traditional search (SEO), AI generative answer engines like ChatGPT/Perplexity/Google AI Overviews (GEO), and answer engines / featured snippets / voice (AEO). Use when the user asks to improve a page's ranking or AI-citability, run an SEO/GEO/AEO audit of a URL or file, add…
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Standalone (no-MCP) version of the Ansvisor AEO Coach. Use this only when the user's Claude client cannot connect to the Ansvisor MCP server (e.g. claude.ai web without a Connector configured). Fetches live data from the Ansvisor REST API directly with the user's API key via code execution. For clients that support…