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 bytefer/geo-seo-codex --skill geo-reportgit clone --depth 1 https://github.com/bytefer/geo-seo-codexWrote 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/bytefer/geo-seo-codex/geo-report)<a href="https://agentmods.dev/skills/bytefer/geo-seo-codex/geo-report"><img src="https://agentmods.dev/badge/skills/bytefer/geo-seo-codex/geo-report.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.04205 |
| Opus 5 | $0.00014 | $0.02103 |
| Sonnet 5 | $0.00006 | $0.00841 |
| Haiku 4.5 | $0.00003 | $0.00421 |
Grade A, and why
geo-report 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 8d 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
98% identical to geo-report — 6 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 — 396 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Client Report Generator
Purpose
This skill aggregates outputs from all GEO audit skills into a single, professional report that can be delivered directly to a client or stakeholder. The report is written for business owners and marketing leaders, not developers — technical findings are translated into business impact and clear action items with priority levels.
How to Use This Skill
- Run the following audits first (or use existing report data):
geo-platform-optimizer-> GEO-PLATFORM-OPTIMIZATION.mdgeo-schema-> GEO-SCHEMA-REPORT.mdgeo-technical-> GEO-TECHNICAL-AUDIT.mdgeo-content-> GEO-CONTENT-ANALYSIS.md- (Optional)
geo-llmstxt-> llms.txt assessment - (Optional)
geo-brand-mentions-> brand authority data
- Collect all scores and findings
- Calculate the composite GEO Readiness Score
- Generate the client report using the template below
- Output: GEO-CLIENT-REPORT.md
GEO Readiness Score Calculation
Component Weights
| Component | Weight | Source Skill |
|---|---|---|
| AI Platform Readiness | 25% | geo-platform-optimizer |
| Content Quality & E-E-A-T | 25% | geo-content |
| Technical Foundation | 20% | geo-technical |
| Schema & Structured Data | 15% | geo-schema |
| Brand Authority & Entity Presence | 15% | geo-platform-optimizer (entity signals) |
Score Formula
GEO Score = (Platform Score * 0.25) + (Content Score * 0.25) + (Technical Score * 0.20) + (Schema Score * 0.15) + (Brand Score * 0.15)
Round to the nearest integer. Cap at 100.
Score Interpretation for Clients
| Score Range | Label | Client-Facing Description |
|---|---|---|
| 85-100 | Excellent | Your site is well-positioned for AI search. Focus on maintaining and expanding your advantage. |
| 70-84 | Good | Solid foundation with clear opportunities to improve AI visibility. Targeted optimizations will yield significant results. |
| 55-69 | Moderate | Your site has gaps in AI readiness that competitors may be exploiting. A structured optimization plan will close these gaps. |
| 40-54 | Below Average | Significant barriers to AI search visibility exist. Without action, your brand risks being invisible in AI-generated answers. |
| 0-39 | Needs Attention | Critical AI readiness issues require immediate action. Your competitors are likely capturing the AI search traffic your brand should own. |
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.
- 8d ago First seen · 396 lines · 28 tokens per session scan A 2f1cb290873f
geo-report is a skill published in the GitHub repository bytefer/geo-seo-codex (11 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 4,205 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to geo-report, differing in 6 lines, and is treated as a copy.
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