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 techhorizonlabs/thl-open --skill geo-reportgit clone --depth 1 https://github.com/techhorizonlabs/thl-openWrote 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/techhorizonlabs/thl-open/geo-report)<a href="https://agentmods.dev/skills/techhorizonlabs/thl-open/geo-report"><img src="https://agentmods.dev/badge/skills/techhorizonlabs/thl-open/geo-report/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/techhorizonlabs/thl-open/geo-report"><img src="https://agentmods.dev/badge/skills/techhorizonlabs/thl-open/geo-report.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 | $0.00028 | $0.04261 |
| Opus 5.5 | $0.00011 | $0.01704 |
| Sonnet 5.5 | $0.00006 | $0.00852 |
| Haiku 4.5 | $0.00003 | $0.00426 |
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 17d 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
94% identical to geo-report — 80 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 — 400 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-llms-txt-> 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.
- 17d ago Changed 25f2ecf65959
- 1mo ago First seen · 400 lines · 28 tokens per session scan A 236902e259f2
geo-report is a skill published in the GitHub repository techhorizonlabs/thl-open (18 stars, last pushed 18d ago), licensed MIT. It adds 28 tokens to every session and 4,261 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to geo-report, differing in 80 lines, and is treated as a copy.
Other skills, from other repositories
geo-report
Generate a professional, client-facing GEO report combining all audit results into a single deliverable with scores, findings, and prioritized actions.
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geo-report
Generate a professional, client-facing GEO report combining all audit results into a single deliverable with scores, findings, and prioritized actions.
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