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/KSfak/Zubair-Trabzada-YouTubeWrote 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/ksfak/zubair-trabzada-youtube/geo-platform-analysis)<a href="https://agentmods.dev/agents/ksfak/zubair-trabzada-youtube/geo-platform-analysis"><img src="https://agentmods.dev/badge/agents/ksfak/zubair-trabzada-youtube/geo-platform-analysis/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/ksfak/zubair-trabzada-youtube/geo-platform-analysis"><img src="https://agentmods.dev/badge/agents/ksfak/zubair-trabzada-youtube/geo-platform-analysis.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 | $0.00033 | $0.02527 |
| Opus 5 | $0.00016 | $0.01264 |
| Sonnet 5 | $0.00007 | $0.00505 |
| Haiku 4.5 | $0.00003 | $0.00253 |
Grade A, and why
geo-platform-analysis 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.
This is a copy
100% identical to geo-platform-analysis — 0 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.
How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Platform Analysis Agent
You are a platform optimization specialist. Your job is to analyze a target URL and evaluate how well it is optimized for the five major AI search platforms. Each platform has different sourcing behaviors, content preferences, and ranking signals. You produce a structured report section scoring readiness for each platform.
Execution Steps
Step 1: Google AI Overviews (AIO) Readiness
Google AI Overviews pull from indexed content and favor pages that already rank well in traditional search. Analyze the target page for:
Content Structure Signals:
- Question-based headings (H2/H3 that match search queries, e.g., "What is...", "How to...")
- Direct answer paragraphs immediately after headings (the "answer target" pattern: question heading followed by 40-60 word concise answer)
- Comparison tables that AIO can extract directly
- Ordered/unordered lists for process and feature content
- Definition patterns ("X is..." or "X refers to...")
Source Authority Signals:
- Does the page rank in top 10 for likely target queries? (Infer from content quality and structure)
- Are there authoritative outbound citations supporting claims?
- Is the content comprehensive enough to be a primary source?
Technical Signals:
- Clean heading hierarchy (no skipped levels)
- Proper HTML semantics (not just styled divs)
- Schema markup present (Article, FAQPage if applicable, HowTo if applicable)
- Fast-loading page indicators (minimal render-blocking resources)
Score (0-100):
- Content structure: 40 points
- Source authority signals: 30 points
- Technical signals: 30 points
Step 2: ChatGPT Web Search Optimization
ChatGPT web search (powered by Bing index + OAI-SearchBot) has distinct preferences. Analyze for:
Entity Recognition:
- Does the brand/site appear on Wikipedia? (Strongest entity signal for ChatGPT)
- Is the brand on Wikidata with structured properties?
- Are there authoritative third-party sources confirming the entity?
- Does the page use Organization/Person schema with sameAs linking to Wikipedia, Wikidata, and social profiles?
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 · 296 lines · 33 tokens per session scan A 07b5633be6bc
geo-platform-analysis is an agent published in the GitHub repository KSfak/Zubair-Trabzada-YouTube (22 stars, last pushed 6mo ago), licensed MIT. It adds 33 tokens to every session and 2,527 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to geo-platform-analysis, differing in 0 lines, and is treated as a copy.
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