Geo Writer

GEO Writer

Makes public-facing text extractable and citable by LLM-based search engines. Classic SEO is losing ~25% of traffic to AI Overviews, ChatGPT, Perplexity, Gemini, and Claude — content must be structured for extraction, not just for reading.

Based on the GEO-SFE framework (arXiv 2603.29979) and 2026 practitioner sources. Full guide in geo-writing-guide.md. Pre-publish checklist in pre-publish-checklist.md.

When to Use

• Blog posts, articles, pillar guides
• Landing pages, help articles, glossary entries
• FAQ blocks, comparison pages, tutorials
• Programmatic SEO templates
• Any text meant to be surfaced or cited by an AI engine

Do NOT apply to: internal docs, ADRs, code comments, commit messages, AI asset files.

Core Principles

1. Write for extraction, not just reading. LLMs lift 30-180 word chunks. Every section must stand alone.
2. Answer first, context second. The first sentence of each H2/H3 fully answers the implied question in 30-60 words.
3. One topic per section. One H2 = one entity or question. Do not merge sub-topics.
4. Entity-first, not keyword-first. Name the product, brand, person, or concept explicitly. Never start paragraphs with It, This, They referring to prior sections.
5. Verifiable > persuasive. Every non-trivial claim needs a statistic, date, source, or named example.

Process

1. Read project brand context — marketing/MARKETING.md at project root (if exists) for canonical brand terms, product name, category label, metric names, audience. Use these verbatim.
2. Draft macro structure — single literal H1, 5-9 H2 sections each phrased as a standalone question or declarative topic, TL;DR section at end.
3. Apply meso chunking — paragraphs 40-80 words (hard cap 120), sections 120-180 words, one idea per paragraph, statistic/named source every 150-200 words.
4. Apply micro emphasis — bold 3-5 most quotable phrases per section, bullet lists for 3+ items, tables for any 2+ entity comparison across 2+ attributes.
5. Insert high-leverage formats — FAQ block (4-8 Q&A), comparison table where natural, named stats with year, direct quotes from named experts.
6. Attach schema — Organization + Article + Person on every article; FAQPage for any Q&A block; HowTo for tutorials; Product/SoftwareApplication for tool pages; DefinedTerm for glossary.
7. Run @humanizer — remove AI-sounding patterns. GEO and humanizer are complementary: GEO optimizes structure, humanizer optimizes voice.
8. Run pre-publish checklist — see pre-publish-checklist.md. Every item must pass or be explicitly waived.

Answer-First Template

## <Question phrased as H2>

<30-60 word direct answer, no preamble, entity named explicitly.>

<Optional 1-2 supporting paragraphs, 40-80 words each, with at least one number or named source.>

<Optional bullet list, table, or code block.>

Do: ## What is an AI-Readiness Score? → first sentence fully defines the term, names the product, lists the dimensions.

Do not: ## Understanding the Score → first sentence says When it comes to AI search, there are many things to consider....

Format Patterns That Get Cited

Ranked by observed citation lift:

Comparison articles lead citation rates at ~32.5% across all formats.

Schema / JSON-LD (Mandatory)

On every page

• Organization (homepage + sameAs on every article)
• WebSite with potentialAction for sitelinks search
• BreadcrumbList on every non-homepage URL

By content type

FAQPage is the single highest-leverage schema: 4-8 questions per page, 8-20 words each; answers 40-80 words, self-contained, matching the prompt phrasing; never duplicate marketing copy. Validate in Google Rich Results Test and schema.org/validator.

Schema templates

Pre-built JSON-LD templates with realistic example values live in assets/jsonld-templates/. Copy the file matching your content type, replace example values with project-specific data (canonical brand from marketing/MARKETING.md, real URLs, real datePublished/dateModified), and embed inside <script type="application/ld+json">. Each template has a valid @context + @type root and validates conceptually in Google Rich Results Test before any project data is added.

Available templates: Article.json, FAQPage.json, HowTo.json, Person.json, Organization.json, WebSite.json, BreadcrumbList.json, Product.json, SoftwareApplication.json, DefinedTerm.json.

Entities and Brand Signals

Off-site brand mentions correlate with AI visibility at 0.67 — stronger than any on-site factor. Mentions beat backlinks 3:1 for AI Overviews.

Rules:

• Use the canonical brand form verbatim — from marketing/MARKETING.md. No variant spellings, no acronyms mid-article.
• Use the canonical category name and metric names — invented variants fragment the entity graph.
• Link first mention of any external entity to its canonical page (Wikipedia, official docs).
• Internal links — every pillar page must interlink to glossary terms and at least 3 related articles.
• Never change terminology mid-article. GEO score in one paragraph and AI-readiness rating in the next confuses retrieval.

If marketing/MARKETING.md is absent — ask the user for canonical brand form, category name, and metric names before writing.

Freshness Rules

Pages not refreshed quarterly lose AI citations at ~3× the normal rate.

On every refresh: update dateModified in schema, add visible Updated on YYYY-MM-DD line under H1, replace at least one stat or example with a fresher one. Perplexity materially favors updates within the last 2-3 days.

Platform Differentiation

Off-site portfolio for pillar topics: 1 Reddit post/comment in a relevant subreddit, 1 YouTube video with descriptive chapters (YouTube overtook Reddit as the #1 LLM citation source in October 2025 — ~16% vs ~10%), 1 LinkedIn long-form post for B2B visibility.

Anti-Patterns

• Burying the lede. In this post we will explore... openings.
• Pronoun drift. Paragraphs starting with It, This, They referring to earlier entities.
• Walls of text. Paragraphs over 120 words.
• Opinion without a named source. Experts say is invisible to RAG.
• Duplicate FAQ between body prose and FAQPage schema. LLMs de-duplicate aggressively.
• Undated statistics. Studies show +30% with no year or link.
• AI-generated filler. Always run @humanizer before publishing.
• Changing terminology mid-article. Confuses the entity graph.

Output Format

When producing GEO-optimized text, deliver:

1. Draft — macro/meso/micro structure applied
2. Checklist pass — run pre-publish-checklist.md, report each item
3. Schema block — JSON-LD for the content type, ready to embed
4. Humanizer pass — applied before final delivery
5. Follow-ups — off-site companion assets suggested (Reddit / YouTube / LinkedIn), freshness cadence noted

Integration

• Rule: geo-content rule auto-activates this skill for public-facing content meant to be cited by AI engines
• Companion skill: @humanizer — GEO structures extraction, humanizer removes AI voice. Always run both.
• Used by workflows: /content-creation (Step 4 of blog pipeline; merged from former /blog-post), /marketing (content operations; absorbed former marketing-operations skill), /docs and /docs-pack (public-facing content), /seo-review (GEO audit alongside classic SEO)
• Used by skills: content-creation (structural gate), social-media-manager (LLM-quotable post framing)
• Companion resources: geo-writing-guide.md (full reference), pre-publish-checklist.md (PR checklist template)
• Brand context source: marketing/MARKETING.md at project root — canonical terms, audience, category