A tool for checking and creating Schema.org structured data, which is machine-readable information about a website and its business, content, or products.

It audits JSON-LD, Microdata, and RDFa, validates them, suggests relevant schemas, and produces JSON-LD code and a report.

It finds missing or invalid markup that can make it harder for search engines and AI systems to understand a website.

geo-schema — Skill for Codex by bytefer

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.

Any agent

npx skills add bytefer/geo-seo-codex --skill geo-schema

Clone the repo

git clone --depth 1 https://github.com/bytefer/geo-seo-codex

Made for: Codex.

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/skills/bytefer/geo-seo-codex/geo-schema.svg)](https://agentmods.dev/skills/bytefer/geo-seo-codex/geo-schema)

Your own site

<a href="https://agentmods.dev/skills/bytefer/geo-seo-codex/geo-schema"><img src="https://agentmods.dev/badge/skills/bytefer/geo-seo-codex/geo-schema.svg" alt="Measured on agentmods" height="20"></a>

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,037 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 91% copy Near-identical to another mod 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	$0.00026	$0.04037
Opus 5	$0.00013	$0.02018
Sonnet 5	$0.00005	$0.00807
Haiku 4.5	$0.00003	$0.00404

Measured 7d ago against content hash eeb78662bb47, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, 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 7d 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.

Origin

This is a copy

91% identical to geo-schema — 38 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/geo-schema/SKILL.md · 395 lines

How it starts

The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GEO Schema & Structured Data

Purpose

Structured data is the primary machine-readable signal that tells AI systems what an entity IS, what it does, and how it connects to other entities. While schema markup has traditionally been about earning Google rich results, its role in GEO is fundamentally different: structured data is how AI models understand and trust your entity. A complete entity graph in structured data dramatically increases citation probability across all AI search platforms.

How to Use This Skill

Fetch the target page HTML using fetch_page.py (see note below)
Detect all existing structured data (JSON-LD, Microdata, RDFa)
Validate detected schemas against Schema.org specifications
Identify missing recommended schemas based on business type
Generate ready-to-use JSON-LD code blocks
Output GEO-SCHEMA-REPORT.md

Step 1: Detection

IMPORTANT: WebFetch converts HTML to markdown and strips <head> content, which removes JSON-LD blocks. Use fetch_page.py instead.

Resolve GEO_ROOT from CODEX_GEO_ROOT, the loaded plugin/repository root, or the classic installer path ${CODEX_HOME:-$HOME/.codex}/skills/geo. Resolve GEO_PYTHON from the classic installer venv, the plugin runtime venv ${CODEX_GEO_RUNTIME_DIR:-${CODEX_HOME:-$HOME/.codex}/geo-seo-codex}/.venv, or a system Python fallback:

GEO_ROOT="${CODEX_GEO_ROOT:-}"
if [ -z "$GEO_ROOT" ]; then
  for candidate in "$PWD" "$PWD/.." "$PWD/../.." "${CODEX_SKILLS_DIR:-${CODEX_HOME:-$HOME/.codex}/skills}/geo"; do
    if [ -f "$candidate/scripts/fetch_page.py" ]; then
      GEO_ROOT="$candidate"
      break
    fi
  done
fi
GEO_RUNTIME_DIR="${CODEX_GEO_RUNTIME_DIR:-${CODEX_HOME:-$HOME/.codex}/geo-seo-codex}"
if [ -x "$GEO_ROOT/.venv/bin/python" ]; then
  GEO_PYTHON="$GEO_ROOT/.venv/bin/python"
elif [ -x "$GEO_ROOT/.venv/Scripts/python.exe" ]; then
  GEO_PYTHON="$GEO_ROOT/.venv/Scripts/python.exe"
elif [ -x "$GEO_RUNTIME_DIR/.venv/bin/python" ]; then
  GEO_PYTHON="$GEO_RUNTIME_DIR/.venv/bin/python"
elif [ -x "$GEO_RUNTIME_DIR/.venv/Scripts/python.exe" ]; then
  GEO_PYTHON="$GEO_RUNTIME_DIR/.venv/Scripts/python.exe"
else
  GEO_PYTHON="python3"
fi
if [ -z "$GEO_ROOT" ] || [ ! -f "$GEO_ROOT/scripts/fetch_page.py" ]; then
  echo "GEO runtime root not found. Set CODEX_GEO_ROOT to the geo-seo-codex plugin/repo root."
  exit 1
fi
"$GEO_PYTHON" "$GEO_ROOT/scripts/fetch_page.py" <url> page

If dependencies are missing, run $geo-setup first. The output includes a structured_data array with all parsed JSON-LD blocks from the page.

Read the full file on GitHub · 395 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.

7d ago First seen · 395 lines · 26 tokens per session scan A eeb78662bb47

Subscribe to this mod's changes

geo-schema is a skill published in the GitHub repository bytefer/geo-seo-codex (11 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 4,037 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to geo-schema, differing in 38 lines, and is treated as a copy.

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