ChatGPT, Claude, Perplexity, and AI Overviews increasingly answer instead of linking. Optimising for them is a different job from ranking — here is what the tooling actually has to do, and what SEOAgent automates in your repo.
Ranking and being quoted are related, but they are not the same target.
An answer engine either cites you or it does not. Position-based tooling has nothing to measure, so the usual dashboards go quiet exactly where this work happens.
A page that looks clear to a person can be shapeless to a model: no schema, no clear question-and-answer blocks, no plain-text mirror. The information is there and still not extractable.
llms.txt and knowledge bundles are usually written once by hand. The next article ships and the file is already lying about what the site contains.
Four jobs, all of which SEOAgent performs as edits in your repository.
Models quote passages that stand on their own. That means direct answers near the question, real FAQ blocks, comparison tables, and specific figures rather than hedged prose — structured so a paragraph can be lifted without losing meaning.
FAQ, Article, and Organization JSON-LD tell a model what a page is. SEOAgent writes the schema into your components and keeps it consistent across pages rather than bolting it onto one at a time.
llms.txt is a map of the site for language models; the Open Knowledge Format bundle describes your organisation to AI agents. SEOAgent generates both from what your site actually contains and regenerates them on every sync, so they cannot drift.
GPTBot, ClaudeBot, PerplexityBot, and Google-Extended each read robots.txt. Blocking them by accident is common and silent — nothing breaks, you simply stop being quoted.
Answers a model can quote without rewriting them
llms.txt and OKF regenerated from the live site, not maintained by hand
The bots that build answers are allowed in and can parse what they find
Tools that make a site legible and quotable to answer engines — ChatGPT, Claude, Perplexity, Google AI Overviews — rather than only ranked by classic search. In practice that means structured answers, schema markup, machine-readable site descriptions such as llms.txt and OKF, and making sure AI crawlers are not blocked.
It overlaps but is not identical. Both reward genuinely useful, well-structured content. The difference is the target: classic SEO competes for a position in a list, while AI search optimization competes to be the passage a model quotes. Structure and extractability matter more; link position matters less.
It is a Markdown map of your site written for language models, served at /llms.txt. It is worth having, but only if it stays accurate — a hand-written one is out of date the moment you publish again. SEOAgent generates it from the pages and articles your site actually contains and regenerates it on every sync.
Referral traffic from assistant domains shows up in analytics, though it undercounts badly because many answers are consumed without a click. Treat citation presence as the primary signal and referrals as a bonus, and check periodically whether assistants describe your product accurately when asked about your category.
SEOAgent focuses on the optimisation side — making pages extractable, generating the machine-readable files, and keeping AI crawlers unblocked. Citation monitoring is a separate category of product, and pairing one with this work is reasonable if being quoted is a core channel for you.
Install the free SEOAgent Skill for Claude Code, Cursor, or Codex. It audits your structure, writes the schema, and generates your llms.txt and OKF bundle as changes you approve.
Install the Skill — free