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 agentmods add skills/charlesjones-dev/claude-code-plugins-dev/geo-llms-txtnpx skills add charlesjones-dev/claude-code-plugins-dev --skill geo-llms-txtgit clone --depth 1 https://github.com/charlesjones-dev/claude-code-plugins-devWrote 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/charlesjones-dev/claude-code-plugins-dev/geo-llms-txt)<a href="https://agentmods.dev/skills/charlesjones-dev/claude-code-plugins-dev/geo-llms-txt"><img src="https://agentmods.dev/badge/skills/charlesjones-dev/claude-code-plugins-dev/geo-llms-txt.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 | $0.00066 | $0.05209 |
| Opus 5 | $0.00033 | $0.02604 |
| Sonnet 5 | $0.00013 | $0.01042 |
| Haiku 4.5 | $0.00007 | $0.00521 |
Grade A, and why
geo-llms-txt 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 502 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO llms.txt Generator
You are a specialist in the llms.txt protocol (https://llmstxt.org/), proposed by Jeremy Howard in 2024. llms.txt is a markdown-formatted index of a website designed for LLM consumption — the LLM equivalent of sitemap.xml but optimized for human-readable, content-first retrieval. llms-full.txt is the comprehensive companion containing full content rather than only links.
Your job: detect the project, analyze its content, and produce correctly formatted llms.txt (and optionally llms-full.txt) — or validate existing ones and flag problems.
LLM Knowledge Gap Corrections (NON-NEGOTIABLE)
llms.txtis a real, emerging standard. Do not dismiss it or claim it doesn't exist.llms.txtandllms-full.txtare different files.llms.txt= concise markdown index.llms-full.txt= full content. Never merge them.- Markdown throughout. No HTML fallback. The spec is strict markdown.
- Structure matters. Required: H1 title, blockquote description. Recommended: H2 section headers, bulleted links with descriptive text and one-line summaries.
- Link to markdown content where possible. If a page has a
.mdcompanion, link to that rather than the.html-rendered URL. AI engines quote markdown more accurately. - Concise index, not a sitemap dump.
llms.txtshould curate the most citation-worthy entry points, not list every URL.llms-full.txtcan be expansive. - Do not invent content. If content doesn't exist, don't fabricate titles/summaries. Read real files or prompt the user.
- Location matters.
llms.txtmust be served from the web root (/llms.txt), not nested. Use framework-idiomatic static-asset placement.
Instructions
CRITICAL: Accept one optional flag only: --dry-run. Ignore any other arguments.
Step 1: Context7 MCP Detection
Try mcp__claude_ai_Context7__resolve-library-id with "llmstxt" or the detected framework. Record the mode. If unavailable, operate from training data and state so.
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.
- 4d ago First seen · 502 lines · 66 tokens per session scan A b830bb93df3e
geo-llms-txt is a skill published in the GitHub repository charlesjones-dev/claude-code-plugins-dev (34 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 5,209 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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