name: geo-content-engineering description: >- Create or rebuild web content so generative engines (ChatGPT, Gemini, Perplexity, Google AI Overviews) extract, cite, and mention it. This is the discipline called GEO (Generative Engine Optimization), also known as AI visibility, LLM SEO, or answer engine optimization. Use this skill whenever the user wants to write, restructure, or optimize any web page, article, guide, landing page, pillar page, or FAQ for AI search visibility, citations, or brand mentions. Trigger it when they mention GEO, generative engine optimization, AI visibility, LLM SEO, answer engine optimization, semantic chunking, getting cited by ChatGPT or Perplexity, or making content "AI-readable"; and also when they ask softer versions like "help me write content that AI will recommend," "why doesn't ChatGPT mention my brand," or "how do I show up in AI answers." Covers both new content creation and rebuilding existing pages, plus the Share-of-Mentions measurement loop. license: CC-BY-4.0 metadata: version: 1.0.0 author: Eugen Ullrich (eullrich.com) attribution: Eugen Ullrich, eullrich.com source: https://github.com/eullr/okf-llm-content-erstellung

GEO Content Engineering

Build web content that generative engines will pull into their answers. The goal is not ranking in a list of blue links. The goal is being the block of text a model extracts, the source it cites, and the brand it names when a user asks a question.

Why this changes how you write

Generative systems do not read a page top to bottom. They split it into semantic blocks (chunks), turn each block into a vector embedding, and for any given prompt they retrieve the single block whose embedding is closest to the prompt. That retrieved block is what gets quoted, summarized, or cited. One sharp, self-contained paragraph often beats an authoritative domain, because the model scores the block, not the whole site.

Everything in this skill follows from that mechanic. Full explanation in references/mechanics.md. Read it once if the user is new to GEO.

Three layers have to be right, in this order. A perfect text on a page a bot cannot read is invisible. So gate access first, then structure, then reputation.

1. Access. The bot must reach and render the content. See references/crawler-access.md.
2. Content and structure. The text must be chunkable, quotable, complete, and current. This is the bulk of the work below.
3. Off-page grounding. External mentions confirm the claims. See references/offpage-mentions.md.

Pick the mode

Mode A: New content. The user is creating a page that does not exist yet. Go straight to Build.

Mode B: Rebuild an existing page. The user has a live URL that underperforms in AI answers. Do not touch the text until you have a baseline, or you will not be able to prove the rebuild worked. Run these first:

1. Measure a baseline Share of Mentions for the target topic cluster before any edit. See references/measurement.md.
2. Check the access gate on the live URL. See references/crawler-access.md. If the bot cannot render the page, fix that first. Rewriting an unreadable page is wasted effort.

Then both modes converge on Build.

Build

Use the page skeleton in assets/page-structure.md as the frame. Fill it by applying six content rules. Each rule has a full reference file; read the file when you need depth, not before.

1. Semantic chunking. One block, one claim. Answer first, then evidence. Every block has to make sense lifted out of the page on its own. Name the entity instead of using pronouns ("the Model X battery," not "it"). Phrase H2 and H3 headings as the questions users actually ask. → references/chunking.md

2. Dense, plain language. Active voice, short sentences, no filler. Every wasted word blurs the block's embedding and makes it lose retrieval to a tighter competing block. When there is no blanket answer, state the condition directly ("if you need A, choose option 1") instead of hedging. → references/language.md

3. Citation-worthiness. Give the model something it cannot get elsewhere: concrete numbers, ranges, prices, technical parameters, and your own data. "42 percent faster" is quotable; "much faster" is not. Duplicate the key figure of any chart or calculator in plain text, or the model cannot read it. → references/citation-worthiness.md

4. Query fan-out coverage. Engines split one complex prompt into sub-questions and answer each separately. Cover the adjacent questions (price, risks, alternatives, step-by-step, common mistakes) as their own blocks and FAQ entries, so one page can feed several parts of one answer. → references/query-fan-out.md

5. Recency. Models favor fresh content, sometimes over better but older content. Put a visible date marker in the text ("As of 03/2026: ...") and keep a short "What changed" box. A CMS timestamp alone does not count, and changing the date without a real update is detectable and increasingly penalized. → references/recency.md

6. Authorship and trust (E-E-A-T). Named, verifiable authorship beats anonymous content in retrieval. Add an author box with name, credentials, and a profile link, and keep those details consistent with external profiles. → references/author-eeat.md

Technical markup pass

Machines read code structure, not visual layout. Something that looks like a table but is built from div containers is useless for extraction. Before publishing, confirm:

• Semantic HTML tags (article, main, header) mark what is content and what is chrome.
• Comparisons are real table markup, not styled divs. Tables are among the most frequently lifted elements.
• Schema.org via JSON-LD is present: Article for posts, Person for authors, FAQPage for question blocks, Product and Offer for offerings.
• Core content is in the initial server HTML response, not injected by client-side JavaScript.

Full requirements in references/semantic-html.md. Paste-ready JSON-LD in assets/json-ld-snippets.md.

Anti-patterns pass (blocking)

Run the finished text against references/anti-patterns.md before calling it done. Two failure classes matter most:

• Structural failures that break extraction: mechanical length-based cuts, claims trapped in images, fake tables, timestamp gaming.
• Machine-tell language that marks the text as AI-generated and erodes both reader trust and differentiation from mass-produced competitors. This is a registry of specific English words and phrasing patterns to strip out (delve, seamless, tapestry, testament to, navigate the complexities, and more). The irony is real: content built for AI visibility must not read as if a machine wrote it. Treat this pass as blocking, not cosmetic.

Publish and register

When the page goes live, record the URL and publish date in an input register. Without that record you cannot later attribute any change in visibility to this work. The register entry is part of publishing, not an optional follow-up. Format in references/measurement.md.

Measure

Web traffic is a weak KPI here, because users get their answer inside the AI interface without clicking (zero-click). Measure presence in the answers themselves.

• Share of Mentions (SoM): share of tracked prompts whose answers mention the brand. This is the lead metric.
• Share of Direct Citations: share of prompts where the model links your site as a source.
• Indirect Citations: prompts where the model cites an external source that points to the brand.

Prefer shares over raw counts, benchmark the same prompts for competitors, and validate sentiment by hand on a sample. Full algorithm and blind spots in references/measurement.md.

When there are many pages

If the user has to choose which pages to rebuild first, prioritize by prompt demand, competitor gap, business value, and effort, after filtering out any URL the bot cannot reach. See references/prioritization.md.

Reference map

Honest limits

There is no industry standard for optimal chunk length, and different engines cut and score differently, so the work is iterative: publish, measure, sharpen the blocks that fail to get extracted. Retrieval is a black box with no guarantees. This skill improves the odds; it does not promise placement.

Based on the OKF knowledge bundle "LLM-Content-Erstellung" by Eugen Ullrich (eullrich.com), CC BY 4.0. Attribution required: Eugen Ullrich, eullrich.com. Contact: hi@eullrich.com.

GEO Content Engineering (Agent Skill)

An open Agent Skill (SKILL.md format) for creating and rebuilding web content so generative engines (ChatGPT, Gemini, Perplexity, Google AI Overviews) extract, cite, and mention it. This is the discipline called GEO (Generative Engine Optimization), also known as AI visibility, LLM SEO, or answer engine optimization.

What it does

The skill turns a general agent into a GEO content specialist. It walks the agent through the retrieval mechanic (chunk, embed, retrieve, ground, cite) and applies it as a repeatable workflow: gate crawler access, structure text into extractable blocks, force citations with concrete data, cover query fan-out, keep content fresh, add verifiable authorship, apply correct semantic markup and JSON-LD, strip machine-tell language, publish with an input register, and measure Share of Mentions against a competitor benchmark.

It covers two modes: creating new content, and rebuilding existing pages (baseline first).

Structure

geo-content-engineering/
├── SKILL.md                  # workflow core + reference map
├── references/               # deep concept files, loaded on demand
├── assets/                   # paste-ready templates and JSON-LD
└── LICENSE.md                # CC BY 4.0

Install

Place the folder in your agent's skills directory (for example ~/.claude/skills/ for Claude Code, or upload in claude.ai settings). The agent loads the full instructions only when a task matches the description.

License

CC BY 4.0. Based on the OKF bundle "LLM-Content-Erstellung" by Eugen Ullrich (eullrich.com). Attribution required: Eugen Ullrich, eullrich.com.