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/schema-markup/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/schema-markup)<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/schema-markup"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/schema-markup/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/schema-markup"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/schema-markup.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 | $0.00106 | $0.01714 |
| Opus 5 | $0.00053 | $0.00857 |
| Sonnet 5 | $0.00021 | $0.00343 |
| Haiku 4.5 | $0.00011 | $0.00171 |
Grade A, and why
schema-markup 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 12d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
schema-markup
Detects what schema a page already has, recommends the right types for the page's purpose, and
generates valid, ready-to-paste JSON-LD. Ties to GEO: structured data and Q&A markup make a
page easier for AI answer engines to parse and cite (reference the geo-audit skill). Uses Ahrefs
Site Audit to find pages and check existing markup (see knowledge/ahrefs-mcp-map.md).
Methodology (PERCEIVE → ANALYZE → VALIDATE → ACT)
PERCEIVE — detect. Resolve project (./bin/mkt config show --project <client>). Identify the
page(s); read their content with site-audit-page-content / site-audit-page-explorer, or fetch
directly with WebFetch. Detect existing structured data in JSON-LD (<script type= "application/ld+json">), Microdata (itemscope/itemprop), and RDFa. Note page type, primary
entity, and any markup already present (so we extend, not duplicate).
ANALYZE — recommend the right types. Match schema to page purpose:
- Organization / LocalBusiness — home/about/contact (LocalBusiness for a physical/local
business; pairs with the
local-seoskill — include geo-coordinates). - Product (+
Offer,AggregateRating) — product/PDP pages. - Article / BlogPosting — editorial/blog content (author, datePublished, publisher).
- BreadcrumbList — any page with a navigation trail.
- FAQPage / QAPage — genuine Q&A. Note: FAQ rich results are de-emphasized in Google SERPs, but the markup still helps AI answer engines parse and cite the page — keep it for GEO. Recommend only types the page's content truthfully supports. Tie type choice to AI extractability: FAQPage / HowTo / QAPage blocks are the ones AI answer engines most often lift verbatim as the answer — prefer them on any page whose intent is a question or a procedure. Prefer JSON-LD (Google's preferred format) and embed it in server-rendered HTML — JS-injected schema is processed late and can be missed, especially for time-sensitive Product/Offer data.
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.
- 12d ago First seen · 105 lines · 106 tokens per session scan A faefa48289a9
schema-markup is a skill published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 106 tokens to every session and 1,714 once invoked, about $0.0005 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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