Running Agentic SEO on 14 Sites: The 10-Check AEO Scorecard 2026

This agentic SEO audit ran Addy Osmani's open-source agentic-seo CLI against fourteen production properties on 12 May 2026. Two were ours: forkoff.xyz and clips.forkoff.xyz. Twelve were AEO and SEO industry references: Backlinko, Search Engine Land, Ahrefs, Semrush, Moz, BrightEdge, seo.com, Perplexity, Anthropic, OpenAI, generative-ai-search.com, and schema.org. The median score was 9 of 100. Every site graded F. clips.forkoff.xyz scored 16, top of the FORKOFF stack. seo.com scored 20, the only outlier above 17. The receipts live in this post.

Agentic engine optimization is the new acronym for a real engineering shift. Agents fetch pages with a single GET, parse semantic HTML, count tokens, and decide whether to cite you inside a context window measured in thousands of tokens. The conventional SEO stack was tuned for crawlers that load JavaScript, render CSS, and click through link graphs. Agents do none of that. Addy Osmani, lead engineer on Google Chrome, open-sourced agentic-seo on 11 April 2026 to give operators a 10-check scorecard for the new mechanics. The repo cleared 195 stars in 31 days. The CLI runs in 90 seconds. The output is a JSON blob with category-level scores, per-check findings, and remediation hints.

This post answers one question the SERP does not currently address: when you run the tool on more than one site, what does the score distribution actually look like, and what does it imply about production AEO readiness?

What agentic SEO scoring actually measures

The framework grades a site across five categories totaling 100 points. Each category answers a question an AI agent has to resolve before it cites you, recommends you, or silently drops you out of context. Every check is a HEAD or GET request away from a verdict, which is why this is engineering rather than punditry.

The grading rubric is mechanical: A for 90 or above, B for 75 to 89, C for 60 to 74, D for 40 to 59, F below 40. A small site that ships a credible robots.txt allowlist, an llms.txt, and four of the ten checks honestly will land in C territory before it touches the harder structural work. URL-only mode runs four of the ten; full filesystem mode runs all ten. The CLI exits non-zero if you fail your threshold, outputs JSON, and slots into continuous integration. At FORKOFF we run it in our pre-deploy gate so any score drop blocks a Vercel build. The command is two words: npx agentic-seo --url https://yourdomain.com --json --threshold 60. The shape of the scorecard tells you which work compounds first. Discovery is a one-week sprint: ship a credible robots.txt, an llms.txt, and an AGENTS.md. Content Structure is a two-week sprint: heading hierarchy audit across the corpus, markdown variants for every documentation page, semantic HTML on every component. Token Economics is a per-page edit: count tokens, trim pages over 25,000 tokens, add AI-friendly meta tags. Capability Signaling and the UX Bridge are smaller surface area but disproportionate signal for any team that ships APIs or tooling. The framework is built so the first 60 of 100 points are achievable in roughly four weeks for a small site.

The 14-site scorecard, ranked

We ran the same command against every site in the test set on 12 May 2026 in URL-only mode. Median page response time: 1.4 seconds. Median CLI runtime: 87 seconds. No site graded above F.

Three numbers stand out. Nine of fourteen sites are tied at exactly nine points. They all ship a robots.txt that does not block AI crawlers, they all expose an agent-permissions partial credit, and they all skip the other eight checks. This is the standard 2026 site posture in May. The pattern is uniform across SEO authority publishers (Ahrefs, Semrush, Moz, Backlinko), AI labs (Anthropic, OpenAI, Perplexity), and the schema standard body itself. seo.com (20) and Search Engine Land (17) are the only two pages that ship a credible llms.txt with descriptions. Their llms.txt files include structured links in the [title](url) format and at least partial token-count annotations. Both files come in under the 5,000-token recommendation. The combined seven-point lift is what carries them above the median. clips.forkoff.xyz sits at 16, third on the table, ahead of every authority publisher we tested. Score breakdown: full 10 of 10 on robots.txt (explicit allow rules for ClaudeBot, GPTBot, PerplexityBot, Amazonbot, cohere-ai, Bytespider), 5 of 10 on llms.txt (found but missing descriptions and token annotations), and the standard 1 point on agent-permissions. forkoff.xyz scores 14 with a similar profile and a slightly weaker robots.txt grade. The honest read is that the entire SEO industry is at zero on eight of the ten checks the next-generation citation index will measure. Including the AI labs whose models do the citing.

Princeton GEO maps onto the scorecard

The structural failure pattern is not academic. Princeton's Generative Engine Optimization paper (KDD 2024, n=10,000 AI-generated responses across Perplexity AI) found that AI engines pull 33 percent of their citations from comparison-content pages and another large share from pages that ship structured data, expert quotations, and statistics with dates. The lift per pattern was published as a table that maps almost directly onto Addy Osmani's check list.

The mapping holds because Addy Osmani built agentic-seo to grade exactly the structural signals Princeton found to drive citations. The CLI is the operational layer underneath the academic finding. A site that scores high on Addy's framework is, in aggregate, the same site Princeton's data says gets cited. The 33-percent comparison-content finding is particularly leverageable. Most marketing sites publish three categories of content: brand explainers (low citation rate), product walkthroughs (medium citation rate), and comparison pieces (high citation rate). The cheapest agentic AEO win for any team in 2026 is to rebalance the content calendar toward comparisons. At FORKOFF the rule of thumb we ship is one comparison piece for every three explainer pieces, with the comparison anchored in first-party data wherever possible. This is also why nobody has rolled native llms.txt support yet. Aryan from Hex went public asking Framer to ship it without a paywall.

The platform-level llms.txt fight is one of the most underrated AEO unlocks for non-technical operators in 2026. Until Framer, Webflow, Squarespace, and Wix all ship native llms.txt, the founders using those platforms will lose the citation race to founders running custom Next.js stacks who can author one in an afternoon.

The 4 wins, the 6 unrunnable, the 2 real gaps on forkoff.xyz

Walking through the forkoff.xyz audit specifically. Score 14 of 100. URL-only mode skipped six of ten checks because they need filesystem access. Of the four that ran, two passed solidly and two flagged real issues. The 4 wins:

1. robots.txt with explicit AI allowlist. The file ships User-agent: ClaudeBot Allow: /, the same for GPTBot, PerplexityBot, Amazonbot, cohere-ai, Bytespider, and a half dozen others. The CLI flagged "no AI crawlers explicitly blocked" as a partial-credit pass.
2. llms.txt found at the canonical path. The file is at https://forkoff.xyz/llms.txt. The CLI scored 5 of 10. The presence-only credit lifted us above the nine-point floor that nine of our test sites hit.
3. llms.txt under the 5,000-token recommendation. The file weighs 789 tokens. Under budget.
4. llms.txt organized into 13 sections. The structural sectioning passed.

1. AGENTS.md missing. No AGENTS.md at the repository root. No CLAUDE.md either. This is a hard zero out of 5 in Discovery. The fix is a one-afternoon write, but the file matters because both OpenAI's coding agents and Anthropic's Claude Code read it on session start.
2. llms.txt missing structured links and token annotations. The CLI flagged three warnings: no [title](url) links, no descriptions after links, and no token-count annotations. Each warning is a fixable line of markdown.

Our read is that llms.txt is not currently a strong ranking signal, but the file is cheap to ship, the convention is hardening, and Anthropic and OpenAI have both started reading it for agent-mode context. Treat it as the agentic-era robots.txt for answer engine optimization: build it now because the cost of not having one when the standard solidifies is much higher than the cost of having one early. The full-mode score for forkoff.xyz, when we ran the CLI against the local Next.js build, sat at 47 of 100. That number is the gap between URL-only auditing (what an outside auditor sees) and full-filesystem auditing (what an inside team can see). Most of the gap closed because the content-structure checks ran against real HTML rather than guessing from page-level signals. Heading hierarchy passed because the site uses one H1 per page, with H2 and H3 nested correctly. Semantic HTML passed because the Next.js components emit article, section, nav, and main tags rather than divs. The markdown-availability check flagged that we do not currently expose .md versions of pages at the same URL, which is the easiest remaining win.

The AI citation SEO gap: every site fails the same checks

The cross-site pattern is more interesting than any single score. AGENTS.md: zero out of fourteen. Content-structure: zero out of fourteen. Token-budget: zero out of fourteen. Meta-tags: zero out of fourteen. Skill.md: zero out of fourteen. Copy-for-AI: zero out of fourteen. Distribution of total scores across the 14 sites: one at 20, one at 17, one at 16, one at 14, nine clustered at 9, one at 6. The mode is 9. The standard deviation is 3.5. The same data drawn as a histogram looks like a spike at 9 with three small bumps to the right and one to the left. This is what early-adoption distributions tend to look like: a long flat run while the standard converges, then a small lead pack starts pulling ahead as the cheap structural checks get shipped. Six of the ten checks scored zero across every site we tested. URL-only mode contributes to that uniformity because filesystem-access checks cannot run remotely, but the AGENTS.md miss is real: it shows up as a hard zero regardless of mode because the file would resolve at a public path if it existed. The operator-level signal from the AGENTS.md gap is loud. The convention is two months old, the spec lives at agents.md, Anthropic and OpenAI both read the file by default, and every major SEO publisher we tested is missing it. Whoever ships an AGENTS.md describing their service catalog by the end of Q2 will be early to a convention that is converging fast. The operator reality is also funnier than the spec implies, because the file is now a context-loading dependency graph that agents follow recursively.

The 40-minute debug was the agent correctly following a rule the operator forgot they had written. The file is not configuration. The file is a runtime artifact that you orchestrate with the same care you would give a production service. This thread caught Google's official confirmation that traditional SEO still drives AI Overview citations.

The point is not that one stack replaces the other. The point is that production AEO in 2026 means shipping both: conventional E-E-A-T signals to keep your organic index health, plus the agentic stack so the next generation of citation engines can find and quote you. The cost of running both is low because the work mostly compounds.

The 4-week remediation playbook

We are running the following four-week sprint on forkoff.xyz starting 13 May 2026. The list comes out of the agentic-seo CLI output, the cross-site failure pattern, the Princeton GEO mapping, and operator validation from the tactics library.

The week-one work is the most leveraged. AGENTS.md alone is a one-afternoon ship that lifts Discovery from 13 of 25 to 18 of 25. The llms.txt split is a token-budget play: keep the index file under 5,000 tokens so agents read the whole thing during page-context resolution, then put verbose content in llms-full.txt for agents with longer windows. The token-annotation pass takes longer but pays compound: every page that ships its own token count helps agents budget context across a full session. The week-two heading-hierarchy audit is the most boring task on the list and the one with the largest aggregate lift across the page corpus. Most marketing sites accumulate H2 and H3 abuse over years of content edits: pages that use H2 for visual styling rather than semantic structure, pages that skip from H1 to H4, pages that bury the actual topic statement inside a paragraph. The CLI grades each page on whether one H1 anchors the topic, whether H2s map to logical sub-sections, and whether H3s nest correctly under their parent H2. An AI agent that fetches a page parses headings first because they are the cheapest tokens to read and the highest-signal structural cue. Markdown availability is the other week-two win. The pattern is simple: for every HTML page, expose the same content at <path>.md. The Next.js implementation is a route handler that serves a markdown variant when the URL ends in .md or when the Accept: text/markdown header is set. Most AI agents will pick up the markdown variant automatically when they have the choice, which means cleaner parsing, fewer tokens consumed per page, and more page coverage inside a single agent session. At FORKOFF we pair this with an under-served keyword pass that the operator @hridoyreh documented for traditional SEO. The pattern transfers directly to AEO: in Google Search Console, find URLs with high impressions but no clicks, pull the queries Google is already trying you for, and inject them into paragraphs, alt text, and FAQ sections. One client list hit 300 under-served keywords. The same mechanic . also covered by our free AI SEO audit tool . finds the queries AI Overviews are surfacing you for and tells you which sections to deepen on a page-by-page basis. The dependency-graph quality of AGENTS.md means a single root file can chain context through your whole repo. Two operator quotes that landed on the same day.

This is the structural upgrade nobody is talking about in the AEO discourse. AGENTS.md is not a static config file. It is a graph traversal target. Architect it like one. If you only have ten minutes, the operator headline is that nobody in our test sample is doing the work yet, the cheapest first wins are robots.txt and llms.txt, and the harder structural work is on the table for any team that ships a marketing site in the next quarter. Three to four weeks of focused execution pulls a small site from F to C and a medium site from F to B.

Why structure without outcome is half the answer

agentic-seo grades readiness, not outcome. A site can score 90 on the framework and earn zero AI citations if the underlying content does not answer the query a buyer would actually type into ChatGPT. A site can score 40 and rank inside three queries because the one canonical page on a topic happens to live there. Princeton's research suggests roughly a third of AI citations go to comparison-content pages. The implication for production AEO is that comparison-content density is at least as important as structural readiness. Most of the sites we audited can ship llms.txt and AGENTS.md inside a week. The harder work is the second layer: producing the comparison content that AI agents actually quote. At FORKOFF we measure both. Every published page carries qualified-view tracking so we can see which agents fetched it, which queries surfaced the page in LLM citations, and which of those citations led to a session, a calendar booking, or a paid engagement. The metric we hold ourselves to is $0.003 CPQV across the AEO vertical, with qualified views defined as either 30 seconds on page or any conversion event. The structural layer is necessary. The outcome layer is sufficient. The instrumentation tells us which agents earn the citation traffic. ClaudeBot fetches the most pages by volume on forkoff.xyz, GPTBot follows, PerplexityBot and Amazonbot are roughly tied, and Bytespider trails. Inside each agent's fetch pattern, the pages that earn the highest citation rate are the comparison-shape pieces, the data-essays with first-party numbers, and the operator playbooks with named tactics. Generic explainer pieces under-perform. The pattern matches Princeton's 33-percent comparison-content finding almost exactly. This is the empirical confirmation that ties Addy Osmani's structural framework to a buyer-active outcome metric. If your team is building this on its own, the tactical sequence is: run agentic-seo this week, ship the four-week remediation, then start instrumenting AI-channel referral traffic via the GA4 channel-group regex so you can see which agents are actually quoting you. If you want managed delivery, the FORKOFF AI SEO service ships the AEO audit, the four-week sprint, and the qualified-view instrumentation as one engagement. We also operate a AI search optimization service that authors the skill.md and AGENTS.md layer for product teams shipping developer-facing surface area.