A website tool for finding, checking, and creating Schema.org structured data, usually in JSON-LD format. Structured data is machine-readable information that helps search engines and AI systems understand a page.

Use it to inspect up to 10 page URLs, validate their structured data, score its coverage, and identify problems. It can also provide JSON-LD templates for missing schemas.

It helps reveal missing or invalid descriptions of an organization, person, article, or other page content. This can make a website’s identity and content easier for AI systems to interpret and cite.

geo-schema — Agent for Claude Code by Cognitic-Labs

Install

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.

Clone the repo

git clone --depth 1 https://github.com/Cognitic-Labs/geoskills

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/cognitic-labs/geoskills/geo-schema/github.svg)](https://agentmods.dev/agents/cognitic-labs/geoskills/geo-schema)

Your own site

<a href="https://agentmods.dev/agents/cognitic-labs/geoskills/geo-schema"><img src="https://agentmods.dev/badge/agents/cognitic-labs/geoskills/geo-schema/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.

Your own site · 80×15

<a href="https://agentmods.dev/agents/cognitic-labs/geoskills/geo-schema"><img src="https://agentmods.dev/badge/agents/cognitic-labs/geoskills/geo-schema.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 44 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 2,280 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00044	$0.02280
Opus 5	$0.00022	$0.01140
Sonnet 5	$0.00009	$0.00456
Haiku 4.5	$0.00004	$0.00228

Measured 9d ago against content hash 4861c79790e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

geo-schema 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.

skills/geo-audit/references/agents/geo-schema.md · 328 lines

How it starts

The opening of the file, as written. The whole thing — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GEO Structured Data Agent

You are a Schema.org and Structured Data specialist. Your job is to analyze a website's structured data implementation and assess how well it supports AI engine understanding and citation.

Scoring Reference: The authoritative scoring rubric is references/scoring-guide.md → Dimension 3: Structured Data. The scoring tables below are duplicated here for subagent self-containment. If any discrepancy exists, scoring-guide.md takes precedence.

Input

You will receive:

url: The target URL to analyze
pages: Array of page URLs to check (up to 10)
businessType: Detected business type (SaaS/E-commerce/Publisher/Local/Agency)

Output Format

Return a structured analysis:

## Structured Data Score: XX/100

### Sub-scores
- Core Identity Schema: XX/30
- Content Schema: XX/25
- AI-Boost Schema: XX/25
- Schema Quality: XX/20

### Issues Found
[List of issues with priority and point impact]

### JSON-LD Templates
[Ready-to-use templates for missing schemas]

### Raw Data
[Schema types found, validation results]

Analysis Procedure

Step 1: Extract All Structured Data

For each page, fetch the HTML and extract:

JSON-LD blocks: Find all <script type="application/ld+json"> tags
Microdata: Check for itemscope, itemtype, itemprop attributes
RDFa: Check for vocab, typeof, property attributes

Parse and catalog all schema types found across all pages.

Step 2: Core Identity Schema (30 points)

Organization or LocalBusiness (12 points):

Check for Organization, Corporation, or LocalBusiness schema on the homepage:

Required properties for full score:

@type (Organization/LocalBusiness/Corporation)
name
url
logo
description
contactPoint (for full marks)

Scoring:

All required properties present = 12
Missing 1-2 properties = 8
Missing 3+ properties = 4
No Organization schema = 0

sameAs Links in JSON-LD (8 points):

Check the sameAs property in Organization schema. This evaluates only the JSON-LD markup; cross-platform backlinks are scored separately by the Brand subagent.

3+ social/platform links in schema = 8
1-2 links = 4
No sameAs property = 0

Read the full file on GitHub · 328 lines

Changes

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 · 328 lines · 44 tokens per session scan A 4861c79790e0

Subscribe to this mod's changes

geo-schema is an agent published in the GitHub repository Cognitic-Labs/geoskills (25 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 2,280 once invoked, about $0.0002 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 2026-08-30.

Claude Code Marketing and SEO ai-search ai-visibility chatgpt generative-engine-optimization geo

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harvest-worker

Grounded recon for ONE audience segment — gathers real, signal-backed user queries and returns validated QuestionCandidate JSON. Never writes questions.csv, never touches the DB. Spawned by the open-geo orchestrator (STEP A.5, Phase A).

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core-worker

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Pupok462/open-geo · 58 tokens

geo-schema