geo-schema
Skill9 starsChanged 7 months agoMITView on GitHub
Audit JSON-LD structured data on a single URL. Three layers: (1) parse + validate every JSON-LD block; (2) check expected types per page kind (Article on blog posts, Product on listings, etc.); (3) Enhanced Entity Pages — visible-facts coverage (% of declared facts also present in body), sameAs deref, agent-instructions block. Use when user asks about schema, structured data, JSON-LD, or rich results.
7 other projects use a version of this.
Adopted by
Most recently taken up by
- alpit-patel/seo-aeo-geo-kittheir own version, first seen Nov 8, 2026
- DoddleHQ/doddle-seo-geo-skillstheir own version, first seen Oct 20, 2026
- Ormus-Solutions/LibreGEO-Claude-Codetheir own version, first seen Oct 13, 2026
- techhorizonlabs/thl-opentheir own version, first seen Oct 9, 2026
- HermeticOrmus/LibreGEO-Claude-Codetheir own version, first seen Oct 1, 2026
- zubair-trabzada/geo-seo-claudetheir own version, first seen Aug 4, 2026
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 7
- zubair-trabzada/geo-seo-claude★ 11ksame name
- techhorizonlabs/thl-open★ 16same name
- alpit-patel/seo-aeo-geo-kit★ 2same name
- HermeticOrmus/LibreGEO-Claude-Code★ 1same name
- Ormus-Solutions/LibreGEO-Claude-Code★ 1same name
- DoddleHQ/doddle-seo-geo-skills★ 0same name
- kennyolofsson23-netizen/claude-code-config★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository7 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- geoSkill
- geo-auditSkill
- geo-clientSkill
- geo-content-healthSkill
- geo-crawlersSkill
- geo-rrfSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 7 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 29
Every <script type="application/ld+json"> block parses cleanly
“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
geo-schema — Schema.org / JSON-LD audit
Single-page schema validation + Enhanced Entity Pages methodology check.
Command
python3 -m scripts.checks.schema <url> [--page-kind <kind>]--page-kind is one of: homepage, article, service, about, case-study,
product, faq, generic. If omitted, the module auto-detects.
What it checks
Layer 1 — Validity
- Every
<script type="application/ld+json">block parses cleanly S0cfinding when JSON-LD is invalidS0finding when no JSON-LD is present at allS0tfinding when blocks exceed 20k chars (Vertex AI truncation risk)
Layer 2 — Expected types per page kind
Each page kind expects a baseline set of Schema.org types. Missing ones surface as S1:
| Page kind | Expected types |
homepage | Organization, WebSite, BreadcrumbList |
article | Article, Person, BreadcrumbList |
product | Product, BreadcrumbList |
about | AboutPage, Organization, Person |
faq | FAQPage |
service | Service, Organization |
case-study | CreativeWork, Organization |
Layer 3 — Enhanced Entity Pages (EEP)
EE1— Visible-facts coverage: % of facts declared in JSON-LD that also appear in the page body. Target ≥ 70%.EE2—sameAsderef: HEAD-checks each external link; broken / 404 entries reported.EE3—<section class="agent-instructions">block (emerging pattern, low confidence — flagged as experimental).EE4—BreadcrumbListpopulated with ≥ 2 items.
Reference: docs/STRUCTURED_DATA_BEST_PRACTICES.md (WordLift 2025 + Volpini et al. arXiv:2603.10700 methodology).
Templates
The schema/ directory holds 7 starter JSON-LD templates (Organization, LocalBusiness, Article, Product, WebSite, Person, BreadcrumbList). Copy / adapt rather than writing from scratch. See schema/README.md.
Output
The script prints a structured report to stdout (Markdown). When called from run_audit.py, the findings feed into Annex C of the unified audit.
Dependencies
scripts/checks/schema.py- Python:
requests,beautifulsoup4,lxml
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
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