Borrowing it
Nothing to install: this file belongs to prashishh/seo-geo-report-engine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/prashishh/seo-geo-report-engine/main/.agents/skills/aeo-content-patterns/SKILL.mdgit clone --depth 1 https://github.com/prashishh/seo-geo-report-engineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/prashishh/seo-geo-report-engine/aeo-content-patterns)<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/aeo-content-patterns"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/aeo-content-patterns/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/aeo-content-patterns"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/aeo-content-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00183 | $0.01731 |
| Opus 5 | $0.00092 | $0.00865 |
| Sonnet 5 | $0.00037 | $0.00346 |
| Haiku 4.5 | $0.00018 | $0.00173 |
Grade A, and why
aeo-content-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aeo-content-patterns
Rewrites one page to be the thing an AI engine lifts verbatim and cites. geo-audit answers
whether AI cites the brand (Brand Radar, share-of-voice); this answers how to fix a page so it
gets cited. It is reasoning + page-content driven (WebFetch the live URL, or read the draft),
not an Ahrefs-keyword task — the only Ahrefs touch is optional context (below). Encode reusable
patterns in playbooks/geo-playbook.md (extend it; don't duplicate the score rubric here).
Methodology (PERCEIVE → ANALYZE → VALIDATE → ACT)
PERCEIVE — gather the page. Resolve the project (./bin/mkt config show --project <client>);
read client.yml for brand, ICP, primary entity, author/expert names. Get the content:
WebFetch the live URL (or Read the draft). Capture: H1/H2/H3 outline, the first 80 words under
each heading, any tables/lists, the <head> (publish date, dateModified, author byline), and
existing JSON-LD. Optional Ahrefs context only: brand-radar-cited-pages / -cited-domains to
see which page structures AI already cites for this topic, and site-explorer-ai-responses-count
for the domain's AI-answer baseline. Skip if Brand Radar isn't configured — it's not required.
ANALYZE — score citability, then map AI queries. Score 0–100 across eight dimensions
(weights in playbooks/geo-playbook.md; keep them identical):
- Answer-first — is the direct answer in the first sentence/paragraph under the heading, or buried after preamble? (highest weight)
- Question-shaped headings — do H2/H3 read as real queries ("What is X?", "How does X work?", "X vs Y", "Is X worth it?") rather than label nouns?
- Standalone definitions — at least one 25–50 word self-contained definition of the primary entity that makes sense lifted out of context, no "this"/"that" backrefs.
- Quotable attributed statements — declarative, citable sentences with a named source/number ("According to 's 2026 data, …") an engine can quote with attribution.
- Factual density / citations — specific numbers, dates, named entities, outbound citations to primary sources vs vague prose.
- Tables & lists over prose — comparison tables, ordered steps, bulleted criteria (the structures AI extracts most reliably).
- Schema match —
FAQPage/HowTo(andArticle) JSON-LD whose Q/A and steps mirror visible on-page content (mismatched/invisible schema scores 0 — it's a violation). - Freshness & author — visible publish +
dateModifieddates, byline tied toPerson/author schema, credentials (E-E-A-T). Stale or anonymous = low.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 97 lines · 183 tokens per session scan A 26ecec0e6c71
aeo-content-patterns is a skill published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 183 tokens to every session and 1,731 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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