geo-seo-claude is a Claude Code skill for improving how websites appear in AI-powered search while retaining traditional search-engine optimization. It is used by marketers and website practitioners for analysis such as citation scoring, crawler review, authority assessment, structured data, and platform-specific recommendations. The catalogue entries are skills and agents that carry out this optimization workflow.
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 zubair-trabzada/geo-seo-claude --skill geo-schemagit clone --depth 1 https://github.com/zubair-trabzada/geo-seo-claudeWrote 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/zubair-trabzada/geo-seo-claude/geo-schema)<a href="https://agentmods.dev/skills/zubair-trabzada/geo-seo-claude/geo-schema"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/geo-seo-claude/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.
<a href="https://agentmods.dev/skills/zubair-trabzada/geo-seo-claude/geo-schema"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/geo-seo-claude/geo-schema.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 7 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.03638 |
| Opus 5 | $0.00013 | $0.01819 |
| Sonnet 5 | $0.00005 | $0.00728 |
| Haiku 4.5 | $0.00003 | $0.00364 |
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 13d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- geo-schema — 94% identical, 6 lines differ
- geo-schema — 91% identical, 38 lines differ
- geo-schema — 86% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 371 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:
python3 ~/.claude/skills/geo/scripts/fetch_page.py <url> page
The output includes a structured_data array with all parsed JSON-LD blocks from the page.
Scan for JSON-LD
Look for <script type="application/ld+json"> blocks in the HTML. Parse each block as JSON. A page may contain multiple JSON-LD blocks — collect all of them.
Scan for Microdata
Look for elements with itemscope, itemtype, and itemprop attributes. Map the hierarchy of nested items. Note: Microdata is harder for AI crawlers to parse than JSON-LD. Flag a recommendation to migrate to JSON-LD if Microdata is the only format found.
Scan for RDFa
Look for elements with typeof, property, and vocab attributes. Similar to Microdata — recommend migration to JSON-LD.
Priority Order
JSON-LD is the strongly recommended format for GEO. Google, Bing, and AI platforms all process JSON-LD most reliably. If the site uses Microdata or RDFa exclusively, flag this as a high-priority migration.
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.
- 13d ago First seen · 371 lines · 26 tokens per session scan A b7dc0a602cce
geo-schema is a skill published in the GitHub repository zubair-trabzada/geo-seo-claude (10,540 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 3,638 once invoked, about $0.0001 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.
Other skills, from other repositories
geo-schema
Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup.
geo-schema
Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup.
geo-schema
Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup.
schema-markup
Add, fix, or optimize schema markup and structured data. Use when the user mentions schema markup, structured data, JSON-LD, rich snippets, schema.org, FAQ schema, product schema, review schema, or breadcrumb schema.
ultimate-seo-geo-skill
Use this skill when running SEO audits, optimizing for AI search engines (AI Overviews, ChatGPT, Perplexity), generating schema markup, diagnosing traffic drops, fixing Core Web Vitals, managing site migrations, or building keyword strategies. Runs scored full-site audits (0-100 Health Score) across 21 modules…
seo-audit
Audit and optimize public-facing web pages for search engines and AI search. Covers technical SEO audits (crawlability, Core Web Vitals, structured data), local SEO (LocalBusiness schema, geo-metadata), on-page signals (title tags, meta descriptions, heading hierarchy), and AI search readiness (GEO, llms.txt…