geo-llmstxt

name	geo-llmstxt
description	Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.
allowed-tools	["Read","Grep","Glob","Bash","WebFetch","Write"]

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) that lets a website give AI systems structured guidance about its 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.

The full format spec, the llms-full.txt variant, a fill-in template, and best practices live in references/spec.md — read it before validating or generating a file.

Why llms.txt Matters

AI models must work out which pages matter, what a site is about, and how content is organized — usually by crawling many pages and inferring structure. llms.txt solves this with an explicit, machine- and human-readable summary.

1. Faster AI comprehension: understand the site's purpose and structure from one file rather than dozens of pages.
2. Controlled narrative: you choose which pages and facts AI systems see first, shaping how they represent your brand.
3. Higher citation accuracy: AI systems that consult llms.txt cite the correct, authoritative page for each topic.
4. Reduced misrepresentation: key facts (pricing, features, locations) are stated explicitly, reducing hallucination.
5. Early-adopter advantage: only a small minority of sites have an llms.txt today, so it remains a differentiator.

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Analysis Mode

When checking an existing llms.txt file.

Step 1: Fetch the File

1. Use WebFetch to retrieve [domain]/llms.txt. Also check [domain]/llms-full.txt.
2. Record HTTP status: 200 → validate; 404 → recommend generation; 403 → file blocked, flag as misconfiguration; 301/302 → follow and note the redirect.

Step 2: Validate Format

Check each structural element against the format rules in references/spec.md:

Element	Check	Severity if Missing
H1 Title	Present, matches business name	Critical
Blockquote description	Present, under 200 chars, factual	High
At least one H2 section	Present	Critical
Page entries with URLs	At least 5 entries present	High
URLs are absolute	All URLs use full https:// paths	High
URLs are valid	All URLs return 200 status	Medium
Descriptions present	Every entry has a description after the colon	Medium
Key Facts section	Present with business information	Medium
Contact section	Present with at least email	Low
Reasonable length	30-200 lines	Low
No broken Markdown	Proper formatting throughout	Medium

Step 3: Assess Content Quality

Rate three dimensions 0-100:

• Completeness: covers all major site sections? most important/highest-traffic pages? Key Facts present and accurate? recent content?
• Accuracy: descriptions reflect page content? URLs valid and correct? Key Facts verifiable and current? business description accurate?
• Usefulness: would an AI understand the site from this file alone? descriptions specific enough to differentiate pages? citation-worthy pages highlighted? logical organization?

Overall llms.txt Score = (Completeness * 0.40) + (Accuracy * 0.35) + (Usefulness * 0.25)

Step 4: Compare Against Site Content

Crawl the main navigation and sitemap; identify important pages NOT listed; flag broken/redirected URLs, a business description that no longer matches the homepage, and stale entries (pages significantly updated since the llms.txt was written).

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Generation Mode

When creating a new llms.txt from scratch.

Step 1: Site Discovery

Fetch the homepage and extract the site name (<title>, og:site_name, or H1), business description (meta description or hero), and main navigation + footer links. Fetch /sitemap.xml for all public pages. Identify the business type (SaaS, E-commerce, Local, Publisher, Agency).

Step 2: Page Prioritization

• Always include: homepage, About, pricing (if any), top 3-5 product/service pages, contact, docs landing.
• Include if high quality: top blog posts, case studies, key guides, FAQ, careers (large companies).
• Skip: thin category/tag pages, pagination, login/signup, legal boilerplate (unless relevant), duplicates, minimal-content pages.

Step 3: Write Descriptions

For each selected page, fetch it and read the H1, meta description, and first 2-3 paragraphs, then write a 10-30 word description that states what information is on the page and the specific topics/data/features covered. Use factual language; avoid "best/leading/revolutionary."

• Good: Explains the three pricing tiers (Free, Pro, Enterprise) with feature comparison and annual/monthly costs.
• Bad: Our amazing pricing page! / Learn more about our company.

Step 4: Compile Key Facts

Gather: year founded, founder(s), HQ, employee count (if public), customer/user count (if public), top 3-5 products/services, industry, notable clients/partnerships (if public), key differentiators, recent milestones (last 12 months).

Step 5: Assemble the File

Build the file from the template in references/spec.md.

Step 6: Validate the Generated File

Verify all URLs return 200; entry count is 10-30; no description exceeds 50 words; total length is 50-150 lines; Markdown is clean and consistent.

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Output

Use the templates in references/output-template.md:

• Analysis mode: GEO-LLMSTXT-ANALYSIS.md — score, format-validation table, missing pages, recommendations, and a suggested rewrite if needed.
• Generation mode: the complete llms.txt, plus a short GEO-LLMSTXT-GENERATION.md noting pages discovered vs. selected, prioritization rationale, borderline pages, and recommended update frequency.