geo-content

name

geo-content

description

Make a page more likely to be cited and quoted by AI assistants. Applies a 9-pattern citability checklist (answer-first, descriptive headings, standalone sections, tables, lists, fact density, entity naming, clean HTML) and adds llms.txt plus FAQPage schema. Use when someone runs /growth-os:geo-content, says "optimize for AI citations," "make this quotable," "GEO content," "help ChatGPT quote my page," or after geo-audit shows the brand is invisible or out-cited.

geo-content

geo-audit tells you whether AI assistants cite you. geo-content is how you earn more of those citations — by restructuring content so an assistant can lift a clean, self-contained, factual answer straight from your page. It's not a growth hack; it's disciplined information design that happens to also help human readers and traditional SEO.

What it does

Takes a page (or a content plan) and applies a concrete checklist that makes it more extractable and quotable, then adds the two machine-readable affordances assistants and their crawlers look for:

• A 9-pattern citability pass on the content itself.
• An llms.txt file describing the site for AI crawlers.
• FAQPage / structured data where the content is genuinely Q&A shaped.

When to use

• After geo-audit surfaces queries where the brand is invisible or losing citations to competitors.
• When writing or rewriting a cornerstone page that should become the quotable answer for a topic.
• As the content-side follow-through on a growth audit finding.

The 9 citability patterns

Work through each. They compound — a page that does all nine is dramatically easier for an assistant to quote confidently.

1. Answer-first. Put the direct answer in the first sentence or two under the heading, before context or story. Assistants extract the top of a section; bury the answer and it gets skipped.
2. Descriptive H2/H3. Headings that state the question or the claim ("How much does X cost?" / "X reduces onboarding time by 40%"), not clever labels. The heading is the retrieval hook.
3. Standalone sections. Each section should make sense lifted out of the page with no surrounding context. No "as mentioned above," no dangling pronouns referring to earlier sections.
4. Tables for structured comparisons. Pricing, feature comparisons, specs — put them in real HTML tables. Assistants parse and reproduce tables cleanly.
5. Lists for steps and enumerations. Ordered lists for processes, unordered for sets. Extractable, scannable, quotable as-is.
6. Fact density. Concrete numbers, dates, named specifics over adjectives. "Ships in 3 business days" beats "fast shipping." Facts are what gets quoted; vibes get paraphrased away or dropped.
7. Entity naming. Name the product, company, people, and category explicitly and consistently — don't rely on "we," "our platform," "it." Assistants attribute to named entities; unnamed subjects lose the citation.
8. Clean semantic HTML. Proper heading hierarchy, real <table>/<ul>/<ol>, no critical content trapped in images or rendered only by client-side JS an crawler won't run. If it isn't in the served HTML, it can't be cited.
9. Freshness signals. Visible published/updated dates and current figures. Assistants prefer sources that look maintained.

The two machine affordances

• llms.txt. Author a root llms.txt that gives AI crawlers a clean map: what the site is, the canonical pages worth reading, short descriptions. It's the AI-era analogue of a curated sitemap-for-reasoning. Keep it honest and concise.
• FAQPage schema. Where a section is genuinely a set of questions and answers, add valid FAQPage structured data. Don't fake it — schema that doesn't match visible content is a liability, not a lever. For other structured-data types, hand off to the schema capability.

Steps

1. Take the target page(s) — often the gaps geo-audit flagged.
2. Run the 9-pattern checklist, producing specific, line-level edits (rewrite this heading, front-load this answer, convert this paragraph to a table). Show the before/after; don't just describe.
3. Draft or update llms.txt for the site.
4. Add FAQPage schema only where the content truly is Q&A.
5. Note what to re-measure: point the user back to geo-audit in a few weeks to see if citations moved.

Which adapters / CLI it calls

None directly. It's a content + markup skill. It reads the page (web fetch), reads geo-audit's gap list if available, and outputs edits, an llms.txt, and schema. It changes nothing on the live site — it produces the changes for the user to ship.

How it delegates

• In: consumes geo-audit's citation-domain gaps and invisible-query list to prioritize which pages to work on first.
• Out: deep structured-data work beyond FAQPage → schema capability; broader content planning → content-strategy skills; the actual publishing → the user's own workflow. Growth OS is read-side and advisory here — it hands over finished edits, it doesn't push them.

Guardrails — read this

• GEO is SEO's sibling, not its replacement. These patterns help citations and traditional ranking. Never sell "AI visibility" as a magic channel that bypasses fundamentals — a page still needs to be good, indexable, and genuinely useful. Frame every recommendation as "this helps both."
• No fabricated schema. Structured data must match visible content, always.
• No manipulation. The goal is being genuinely the best, most quotable answer — not gaming assistants. Anything else gets unwound the moment models update.
• Measure, don't promise. Recommend re-running geo-audit to verify impact instead of claiming a guaranteed lift.