# Geo Content > 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… - **Type:** Skill - **Install:** `agentstack add skill-nocodework-growth-os-geo-content` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [nocodework](https://agentstack.voostack.com/s/nocodework) - **Installs:** 0 - **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [nocodework](https://github.com/nocodework) - **Source:** https://github.com/nocodework/growth-os/tree/main/core/skills/geo-content ## Install ```sh agentstack add skill-nocodework-growth-os-geo-content ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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 ``/``/``, 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. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [nocodework](https://github.com/nocodework) - **Source:** [nocodework/growth-os](https://github.com/nocodework/growth-os) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-nocodework-growth-os-geo-content - Seller: https://agentstack.voostack.com/s/nocodework - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.