geo-llmstxt

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:

1. Faster AI comprehension: AI systems can understand your site's purpose and structure from a single file rather than crawling 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 can cite the correct, authoritative page for each topic.
4. Reduced misrepresentation: Key facts (pricing, features, locations) are stated explicitly, reducing AI hallucination about your business.
5. 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](

Detailed Format Rules

1. Title (Required)

• Must be the first line of the file.
• Should be the official business/site name.
• Use the H1 heading format (single #).

2. Description (Required)

• Must appear immediately after the title.
• Use Markdown blockquote format (>).
• Keep under 200 characters.
• Should clearly state what the business does and who it serves.
• Avoid marketing fluff -- be factual and specific.

3. Main Sections (Required -- at least one)

Use H2 headings (##) to organize pages by category. Common section names:

4. Page Entries (Required)

Each entry follows the format:

Rules for page entries:

• Title: Use the actual page title or a clear descriptive title.
• URL: Must be a full, absolute URL (not relative paths).
• Description: 10-30 words describing what the page covers. Be specific about the information available.
• Order: List pages in order of importance within each section.
• Limit: Include 10-30 page entries total. Prioritize your most authoritative and useful pages.

5. Key Facts Section (Recommended)

This section provides quick reference data that AI systems frequently need to answer user queries about your business.

6. Contact Section (Recommended)

llms-full.txt (Extended Version)

In addition to llms.txt, sites can provide /llms-full.txt -- an extended version with more detail.

Differences from llms.txt:

Both files can coexist. AI systems check for llms.txt first, then may optionally load llms-full.txt for deeper understanding.

Analysis Mode

When checking an existing llms.txt file:

Step 1: Fetch the File

1. Use WebFetch to retrieve [domain]/llms.txt.
2. Also check for [domain]/llms-full.txt.
3. Record HTTP status code:
   • 200: File exists -- proceed to validation.
   • 404: File does not exist -- recommend generation.
   • 403: File exists but is blocked -- flag as misconfiguration.
   • 301/302: Redirect -- follow and note the redirect.

Step 2: Validate Format

Check each structural element:

Step 3: Assess Content Quality

Rate the llms.txt on these dimensions:

Completeness (0-100):

• Does it cover all major site sections visible in the navigation?
• Are the most important/highest-traffic pages included?
• Is the Key Facts section present with accurate business data?
• Does it include recent/updated content?

Accuracy (0-100):

• Do descriptions accurately reflect page content?
• Are URLs valid and pointing to the correct pages?
• Are Key Facts verifiable and current?
• Is the business description accurate?

Usefulness (0-100):

• Would an AI system understand the site's purpose from this file alone?
• Are descriptions specific enough to differentiate pages?
• Are the most citation-worthy pages highlighted?
• Is the organization logical and intuitive?

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

Step 4: Compare Against Site Content

1. Crawl the site's main navigation and sitemap.
2. Identify important pages NOT listed in llms.txt.
3. Check if any listed URLs are broken or redirected.
4. Verify that the business description matches current homepage messaging.
5. Flag stale entries (pages that have been significantly updated since the llms.txt was written).

Generation Mode

When creating a new llms.txt file from scratch:

Step 1: Site Discovery

1. Fetch the homepage and extract:
   • Site name (from <title>, <meta property="og:site_name">, or H1)
   • Business description (from meta description or hero section)
   • Main navigation links
   • Footer links
2. Fetch /sitemap.xml to discover all public pages.
3. Identify the site's primary business type (SaaS, E-commerce, Local, Publisher, Agency).

Step 2: Page Prioritization

Categorize all discovered pages and select the most important ones:

Always Include:

• Homepage
• About / Company page
• Pricing page (if exists)
• Primary product/service pages (top 3-5)
• Contact page
• Documentation landing page (if exists)

Include if High Quality:

• Top blog posts (by apparent importance, recency, or comprehensiveness)
• Case studies or customer stories
• Key resource/guide pages
• FAQ page
• Careers page (for large companies)

Skip:

• Thin category/tag pages
• Pagination pages
• Login/signup pages
• Legal boilerplate (unless specifically relevant)
• Duplicate or near-duplicate content
• Pages with minimal unique content

Step 3: Write Descriptions

For each selected page:

1. Fetch the page content using WebFetch.
2. Read the H1, meta description, and first 2-3 paragraphs.
3. Write a description that:
   • Is 10-30 words long
   • States what information is on the page
   • Mentions specific topics, data, or features covered
   • Avoids marketing language ("best," "leading," "revolutionary")
   • Uses factual, informative language

Good description examples:

• Explains the three pricing tiers (Free, Pro, Enterprise) with feature comparison and annual/monthly costs.
• Details the company's founding in 2018, team of 45 employees, and office locations in Austin and London.
• Covers integration setup for Slack, Salesforce, and HubSpot with step-by-step guides and API endpoints.

Bad description examples:

• Our amazing pricing page! (marketing language, no specifics)
• Learn more about our company. (too vague)
• Click here for details. (not descriptive)

Step 4: Compile Key Facts

Gather key business facts from the site:

• Year founded
• Founder name(s)
• Headquarters location
• Number of employees (if public)
• Number of customers/users (if public)
• Key products or services (list top 3-5)
• Industry classification
• Notable clients or partnerships (if public)
• Key differentiators (what makes this business unique)
• Recent milestones or achievements (last 12 months)

Step 5: Assemble the File

Construct the llms.txt following this template:

Step 6: Validate the Generated File

Before outputting:

1. Verify all URLs are reachable (200 status).
2. Confirm total entry count is between 10-30.
3. Check that no description exceeds 50 words.
4. Verify the overall file length is 50-150 lines.
5. Ensure Markdown formatting is clean and consistent.

Output Format

For Analysis Mode

Generate GEO-LLMSTXT-ANALYSIS.md:

For Generation Mode

Output the complete llms.txt file content, ready to be saved to the site's root directory. Also output a brief GEO-LLMSTXT-GENERATION.md report explaining:

• How many pages were discovered and how many were selected
• The prioritization rationale
• Any pages that were borderline (might add later)
• Recommended update frequency (e.g., monthly for active blogs, quarterly for stable sites)

Best Practices Reference

1. Update regularly. If your site publishes weekly blog posts, update llms.txt monthly. If your product changes quarterly, update after each release.
2. Lead with your strongest content. The first entries in each section should be your most authoritative, comprehensive pages.
3. Be specific in descriptions. "Comprehensive 3,000-word guide to React Server Components with code examples" is far more useful than "React guide."
4. Include your differentiators. If your site has unique data, original research, or exclusive features, highlight these in descriptions and Key Facts.
5. Keep it concise. The llms.txt should be scannable in under 60 seconds. Save detail for llms-full.txt.
6. Use absolute URLs. Always include the full https:// URL, never relative paths.
7. Test after deployment. After uploading, verify the file is accessible at https://yourdomain.com/llms.txt with no redirects.
8. Coordinate with robots.txt. Ensure pages listed in llms.txt are not blocked in robots.txt for AI crawlers.
9. Mirror your site structure. Section names in llms.txt should roughly correspond to your main navigation categories.
10. Avoid sensitive pages. Do not include internal tools, admin panels, or pages with sensitive information.