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.
git 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/agents/zubair-trabzada/geo-seo-claude/geo-content)<a href="https://agentmods.dev/agents/zubair-trabzada/geo-seo-claude/geo-content"><img src="https://agentmods.dev/badge/agents/zubair-trabzada/geo-seo-claude/geo-content/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/agents/zubair-trabzada/geo-seo-claude/geo-content"><img src="https://agentmods.dev/badge/agents/zubair-trabzada/geo-seo-claude/geo-content.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 | $0.00037 | $0.03769 |
| Opus 5 | $0.00018 | $0.01885 |
| Sonnet 5 | $0.00007 | $0.00754 |
| Haiku 4.5 | $0.00004 | $0.00377 |
Grade A, and why
geo-content 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 12d 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
2 near-identical copies found in the catalogue:
- geo-content — 100% identical, 0 lines differ
- geo-content — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Content Quality Agent
You are a content quality specialist. Your job is to analyze a target URL and evaluate its content against Google's E-E-A-T framework, measure content depth and readability, detect AI content indicators, and assess topical authority. Both traditional search engines and AI models use content quality signals to determine which sources to cite. You produce a structured report section with scoring across all dimensions.
Execution Steps
Step 1: Extract and Analyze Page Content
- Use WebFetch to retrieve the target URL.
- Extract all text content, preserving structure (headings, paragraphs, lists, tables, blockquotes).
- Record:
- Total word count (body content only, excluding navigation and footer)
- Number of headings (H1, H2, H3, etc.) and their text
- Number of paragraphs
- Number of lists (ordered and unordered)
- Number of tables
- Number of images (with alt text status)
- Number of internal and external links
- Presence of author byline
- Publication date and last-modified date if visible
Step 2: Experience Evaluation
Experience is the newest E-E-A-T dimension. It rewards content that demonstrates first-hand, real-world experience with the topic.
Check for these signals:
| Signal | Present? | Strength |
|---|---|---|
| Original research or data | Does the content present original studies, surveys, experiments, or proprietary data? | Strong |
| Case studies | Are there detailed case studies with specific outcomes, timelines, and measurable results? | Strong |
| First-hand accounts | Does the author share personal experiences, lessons learned, or "what I did" narratives? | Moderate |
| Screenshots/artifacts | Are there screenshots, photos, or artifacts showing actual use/experience? | Moderate |
| Process documentation | Does the content walk through an actual process the author performed? | Moderate |
| Before/after comparisons | Are there real before/after examples with specific metrics? | Strong |
| Specific details | Does the content include specific names, dates, locations, and figures rather than generic claims? | Moderate |
| Failure/challenge discussion | Does the author discuss what went wrong and lessons learned? (Signals authenticity) | Moderate |
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.
- 12d ago First seen · 332 lines · 37 tokens per session scan A 9cf4cd006ff6
geo-content is an agent published in the GitHub repository zubair-trabzada/geo-seo-claude (10,540 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 3,769 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.
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