Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/techhorizonlabs/thl-opennpx agentmods add skills/techhorizonlabs/thl-open/geo-citabilityWrote 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-citability)<a href="https://agentmods.dev/skills/techhorizonlabs/thl-open/geo-citability"><img src="https://agentmods.dev/badge/skills/techhorizonlabs/thl-open/geo-citability/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-citability"><img src="https://agentmods.dev/badge/skills/techhorizonlabs/thl-open/geo-citability.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 | $0.00061 | $0.03750 |
| Opus 5.5 | $0.00024 | $0.01500 |
| Sonnet 5.5 | $0.00012 | $0.00750 |
| Haiku 4.5 | $0.00006 | $0.00375 |
Grade A, and why
geo-citability 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
86% identical to geo-citability — 15 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 — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Citability Scoring Skill
Core Insight
AI language models cite passages that meet specific structural criteria. Research on Generative Engine Optimization (Aggarwal et al., KDD 2024) found GEO tactics can lift visibility in AI answers by roughly 30-115% depending on the tactic and query set (see docs/SOURCES.md for provenance on this and the length figure). The pattern: AI systems preferentially extract passages that are ~134-167 words long, self-contained (understandable without surrounding context), fact-rich (specific statistics, dates, named entities), and directly answer a question in the first 1-2 sentences.
This score is a proxy, not a measurement. It scores how extractable your passages are against these research-derived criteria. It does not ask an AI engine whether it actually quoted you. Any "expected lift" is a research-informed estimate, never a promise. For the live check of whether the engines name you, run the free scan at areyoufoundbyai.com (two buyer questions on ChatGPT and Gemini; the trial and paid tiers cover all seven engines).
This is fundamentally different from traditional SEO copywriting, which optimizes for keyword density and user engagement metrics. GEO citability optimizes for extractability -- the ease with which an AI system can pull a passage from your content and present it as a direct answer.
Citability Scoring Rubric (0-100)
Category 1: Answer Block Quality (30% of total score)
This measures whether content contains clear, quotable answer passages that AI systems can extract verbatim.
Scoring Criteria:
| Score | Criteria |
|---|---|
| 90-100 | Every major section opens with a 1-2 sentence direct answer. Uses "X is..." or "X refers to..." patterns. First 40-60 words of each section can stand alone as a complete answer. |
| 70-89 | Most sections have clear answer openings. Some definition patterns present. Answers are identifiable but may need minor context. |
| 50-69 | Some sections have answer-like openings but many bury the answer in the middle or end of paragraphs. Few explicit definition patterns. |
| 30-49 | Answers are generally buried in long paragraphs. No consistent definition patterns. Content is narrative-driven rather than answer-driven. |
| 0-29 | No identifiable answer blocks. Content is entirely narrative, conversational, or fragmented. AI would struggle to extract any quotable passage. |
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 875639e511e1
- 1mo ago First seen · 323 lines · 61 tokens per session scan A ace8626ceda4
geo-citability is a skill published in the GitHub repository techhorizonlabs/thl-open (18 stars, last pushed 18d ago), licensed MIT. It adds 61 tokens to every session and 3,750 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to geo-citability, differing in 15 lines, and is treated as a copy.
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