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-llmstxtgit 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-llmstxt)<a href="https://agentmods.dev/skills/bytefer/geo-seo-codex/geo-llmstxt"><img src="https://agentmods.dev/badge/skills/bytefer/geo-seo-codex/geo-llmstxt/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/bytefer/geo-seo-codex/geo-llmstxt"><img src="https://agentmods.dev/badge/skills/bytefer/geo-seo-codex/geo-llmstxt.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 | $0.00046 | $0.03666 |
| Opus 5.5 | $0.00018 | $0.01466 |
| Sonnet 5.5 | $0.00009 | $0.00733 |
| Haiku 4.5 | $0.00005 | $0.00367 |
Grade A, and why
geo-llmstxt 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 1mo 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-llmstxt — 9 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 — 426 lines — stays where its author put it; the contents beside it link to each section on GitHub.
llms.txt Standard Analysis and Generation Skill
Purpose
This skill handles everything related to the llms.txt standard -- an emerging convention (proposed by Jeremy Howard in September 2024, gaining adoption through 2025-2026) that allows websites to provide structured guidance to AI systems about their content, structure, and key information. It is analogous to robots.txt (which tells crawlers what NOT to access) but instead tells AI systems what IS most useful to understand about the site.
Why llms.txt Matters
AI language models face a fundamental challenge when processing websites: they must determine which pages are most important, what the site is about, and how content is organized -- typically by crawling many pages and inferring structure. llms.txt solves this by providing an explicit, machine-readable (and human-readable) summary.
Benefits of having a well-crafted llms.txt:
- Faster AI comprehension: AI systems can understand your site's purpose and structure from a single file rather than crawling dozens of pages.
- Controlled narrative: You choose which pages and facts AI systems see first, shaping how they represent your brand.
- Higher citation accuracy: AI systems that consult llms.txt can cite the correct, authoritative page for each topic.
- Reduced misrepresentation: Key facts (pricing, features, locations) are stated explicitly, reducing AI hallucination about your business.
- Early adopter advantage: As of early 2026, fewer than 5% of websites have an llms.txt file, making it a differentiator.
The llms.txt Specification
File Location
The file MUST be located at the root of the domain:
https://example.com/llms.txt
Format Specification
The file uses Markdown formatting with specific conventions:
# [Site Name]
> [One-sentence description of what the site/business does. Keep under 200 characters.]
## Docs
- [Page Title](https://example.com/page-url): Concise description of what this page covers and why it matters.
- [Another Page](https://example.com/another-page): Description of content.
## Optional
- [Less Critical Page](https://example.com/optional-page): Description.
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.
- 1mo ago First seen · 426 lines · 46 tokens per session scan A 783fa8d61fb5
geo-llmstxt is a skill published in the GitHub repository bytefer/geo-seo-codex (13 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 3,666 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to geo-llmstxt, differing in 9 lines, and is treated as a copy.
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