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When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'llms-full.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' 'agent-readable site,' 'agent readiness,' 'is my site agent-ready,' 'WebMCP,' 'do listicles still work for AI,' 'ChatGPT stopped citing comparison pages,' 'how do LLMs see our brand,' 'LinkedIn for AEO,' or 'AI citation format shift.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

Use this Skill: https://skilld.dev/gh/coreyhaines31/marketingskills/ai-seo

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referencesokf.md

≈1.5k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Open Knowledge Format (OKF)

Google's v0.1 markdown spec for representing site content as an agent-readable bundle. Introduced on the Google Cloud blog on 2026-06-12 and shipped inside Knowledge Catalog.

What it is

OKF is a directory of cross-linked markdown files. Each file has:

  • A YAML frontmatter block (type required; title, description, resource, tags, timestamp recommended)
  • A standard markdown body
  • Standard markdown links to other files in the bundle (which the spec treats as concept relationships)

An optional index.md lists the files for progressive disclosure. The bundle can be distributed as a git repo (recommended), a tarball/zip, or a subdirectory of a larger repo.

The full spec fits on one page. The repo lives under GoogleCloudPlatform (the "not an official Google product" disclaimer is Google's standard open-source boilerplate, not a denial — it appears on most of Google's open-source repos including their main AI samples repo).

A minimal concept file

---
type: Article
title: How to Connect the Ahrefs MCP Server to Manus
description: The official MCP servers, why they did not connect, and the fix.
resource: https://yoursite.com/blog/ahrefs-mcp-manus/
tags: [mcp, ahrefs]
---

# How to Connect the Ahrefs MCP Server to Manus

The body of the post, as clean markdown.

Add an index.md that lists all files so an agent can see the bundle's shape before opening each file, and that is the entire format.

Honest framing

Google built OKF for data teams sharing catalog metadata — BigQuery tables, API endpoints, metrics, playbooks. Most of the spec's examples are data-team artifacts, not blog posts. Google's blog post framing: "improve data sharing" and "standardized documentation" for collaboration across teams.

Pointing OKF at a marketing site is a clever repurposing popularized by Suganthan Mohanadasan. It's a legitimate use case for the format but not Google's primary one. Frame it accurately when explaining it to founders or marketing teams.

What it does for AI search today

Nothing immediate. Nothing crawls the web for OKF bundles yet — the spec is weeks old, no AI engine has announced integration, and Knowledge Catalog ingests bundles only for paying enterprise customers' data teams.

Treat OKF as protocol-layer registration — the same shape of bet as early schema.org adoption was a decade ago. Schema took the better part of ten years to pay off; people who shipped it early are still glad they did.

A secondary benefit that pays off today regardless: generating the bundle is itself an internal-linking audit. Suganthan's tool draws every page as a node and every internal link as an edge, so islands and orphans become obvious at a glance.

Where OKF fits in the agent-readable stack

Layer Purpose
sitemap.xml Tells a crawler which URLs exist
robots.txt (with AI bot rules) Permits or blocks AI crawlers
llms.txt Points an agent at the handful of pages you most want read
/pricing.md Structured pricing for agent-buyer comparisons
/okf/ bundle Hands over the content itself as cross-linked concepts
Schema markup Per-page structured data (Article, FAQPage, Product, etc.)

These stack rather than compete. llms.txt is a signpost, OKF is the library.

How to ship one

Three options, ordered by how much effort they take:

1. Suganthan's free web tool (recommended for most sites)

suganthan.com/okf-generator — paste a URL or sitemap, crawls up to 100 pages, returns a downloadable bundle. Also draws the resulting page graph so you can spot disconnected pages before publishing.

2. WordPress plugin (pending wp.org approval)

Suganthan's plugin (free, GPL, awaiting wp.org approval at time of writing) installs in a minute, serves the bundle at /okf/, and rebuilds on every publish or edit so it stays in sync. Direct download link is in his blog post. Requires WordPress 6.0+ and PHP 7.4+. Read-only — never edits posts or settings.

3. By hand

Only practical for a handful of pages. Each post becomes a markdown file with frontmatter that you cross-link manually. Miserable for a whole site.

Hosting & discovery

Serve the bundle at yoursite.com/okf/, starting with yoursite.com/okf/index.md:

  • Static hosts / Cloudflare: drag and drop
  • WordPress: Suganthan's plugin handles the serving
  • Static sites with custom paths: upload the directory to /okf/
  • Closed platforms (Wix, Squarespace, most page-builders): you usually can't serve files at custom paths — skip OKF entirely

After it's serving, add a line to llms.txt pointing to the bundle so agents that read llms.txt (today) can discover the bundle (later).

When to skip

  • Site is <10 pages — overhead exceeds payoff
  • Site is on a closed platform that won't allow custom paths
  • You're not maintaining llms.txt, schema markup, or other machine-readable files (OKF compounds with those; alone it does nothing)
  • You can't budget the 30 minutes a quarter to refresh the bundle as content changes

What to watch

OKF is v0.1, weeks old. Worth tracking, not worth obsessing over:

  • Whether Google announces OKF support in AI Overviews / Knowledge Graph (currently no signal)
  • Whether non-Google engines (ChatGPT, Perplexity, Claude) announce OKF reading
  • Whether the spec moves to v1.0 (breaking changes are possible at <1.0)
  • Whether Knowledge Catalog adds public ingestion endpoints
  • Adoption signals — search GitHub for okf/index.md to see who's shipping bundles

Source: SKILL.md on GitHub

skilld matched fixed text patterns in SKILL.md and file names. Patterns miss obfuscated code.

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1 warning1d5 checks · Risk SAFE
  • Gen Agent Trust Hub1d

    The skill provides expert guidance on AI Search Engine Optimization (AEO). The main security consideration is a low-severity risk of indirect prompt injection, as the agent is tasked with analyzing external websites and AI search outputs that may contain untrusted content.

  • Socket3d

    No alerts

  • Snyk3d

    Risk: LOW · No issues

  • Runlayer7mo

    2/3 files flagged

  • ZeroLeaks6mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at e85f3cb. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 4 days ago.

Activeupdated 5 days ago
metadata
{
  "version": "2.10.1"
}
  • ai-seo
  • aeo
  • llm-optimization
  • answer-engine
  • content-marketing
  • structured-data
  • generative-search
  • ai-visibility

README badge

README badge for coreyhaines31/marketingskills/ai-seo

Optimizes content to be discovered and cited by AI search engines like ChatGPT, Perplexity, Claude, Google AI Overviews, and Copilot. Covers content structure for extractability, authority signals, platform-specific ranking factors, and AI bot access configuration.

Generated from the current SKILL.md.

Does this skill help with Google AI Overviews?
Yes, but the approach differs from other AI platforms. For Google AI Overviews, follow standard SEO practices and E-E-A-T — Google explicitly states no special markup or AI-specific optimization is required. For ChatGPT, Claude, and Perplexity, layer on extractable structure (definition blocks, FAQs, comparison tables) and llms.txt files.
What if my site is blocked from AI crawlers?
You won't be cited by that platform. Check your robots.txt for blocks on GPTBot, PerplexityBot, ClaudeBot, Google-Extended, and Bingbot. You can allow search/citation crawlers while blocking training-only crawlers like CCBot.
Does AI SEO require creating separate content from traditional SEO?
No. Write for people and organize with clear headings, paragraphs, and structure. This satisfies both traditional search and AI platforms. Avoid creating scaled or duplicate content specifically for AI, which violates Google's spam policies.
Which optimization methods boost AI visibility the most?
According to Princeton GEO research, citing sources (+40%), adding statistics with sources (+37%), and including expert quotations (+30%) deliver the largest visibility boosts across AI platforms like Perplexity. Combining fluency improvements with statistics yields the highest gains.
Can I rank on page 2 or 3 and still get cited in AI answers?
Yes. AI systems select sources based on content quality, structure, and relevance — not just rank position. A well-structured page with clear answers and authority signals can be cited even if it ranks lower in traditional search.

Generated from the current SKILL.md. These answers refresh after source changes.