Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JakeLabate/Claude-SEO-Skills --skill schema-markup-auditgit clone --depth 1 https://github.com/JakeLabate/Claude-SEO-SkillsWrote 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/jakelabate/claude-seo-skills/schema-markup-audit)<a href="https://agentmods.dev/skills/jakelabate/claude-seo-skills/schema-markup-audit"><img src="https://agentmods.dev/badge/skills/jakelabate/claude-seo-skills/schema-markup-audit/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/jakelabate/claude-seo-skills/schema-markup-audit"><img src="https://agentmods.dev/badge/skills/jakelabate/claude-seo-skills/schema-markup-audit.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 | $0.00087 | $0.01579 |
| Opus 5 | $0.00044 | $0.00790 |
| Sonnet 5 | $0.00017 | $0.00316 |
| Haiku 4.5 | $0.00009 | $0.00158 |
Grade A, and why
schema-markup-audit 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 11d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Schema Markup Audit
Audit the structured data (schema markup) of a website and produce an actionable SEO report.
When to use this skill
Use this skill when the user asks to:
- Audit or validate schema markup / structured data on a website
- Check rich result eligibility (e.g., Product, Article, FAQ, Review snippets)
- Find missing, invalid, or incomplete JSON-LD, Microdata, or RDFa
- Recommend which schema types to add to which pages
- Fix errors or warnings reported by Google's Rich Results Test or Search Console
Inputs to collect
Before starting, confirm with the user:
- Site URL or local files — a live site root URL (e.g.,
https://example.com), a sitemap URL, a list of URLs, or a local folder of HTML files. - Scope — full site, a specific section (e.g.,
/products/), or a list of URLs. - Crawl limits — max pages (default 200) for live crawls.
- Business context — site type (e-commerce, blog, local business, SaaS, etc.) so page-type-to-schema-type expectations can be set.
Workflow
Step 1: Gather the page set
- If a sitemap is available (
/sitemap.xml), fetch it to get the canonical page list. - Otherwise, crawl from the homepage following only same-host links.
- For local HTML folders, enumerate all
.htmlfiles.
Step 2: Extract structured data
Use scripts/extract_schema.py to fetch pages and extract all structured data (JSON-LD, Microdata, RDFa) into a JSON inventory:
python3 scripts/extract_schema.py https://example.com --max-pages 200 --output schema_inventory.json
# already crawled once (e.g. in a full SEO audit)? skip the crawl and reuse the shared cache:
# python3 scripts/fetch_pages.py https://example.com --output page_cache.json
# python3 scripts/extract_schema.py --from-cache page_cache.json --output schema_inventory.json
For every page, record:
- Each structured data block: format (
json-ld,microdata,rdfa),@type, raw parsed object - JSON parse errors in
<script type="application/ld+json">blocks - Page metadata: title, canonical URL, meta robots
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 136 lines · 87 tokens per session scan A adf714dd7b6c
schema-markup-audit is a skill published in the GitHub repository JakeLabate/Claude-SEO-Skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 87 tokens to every session and 1,579 once invoked, about $0.0004 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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