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Geo Llmstxt

CommunityPopular
zubair-trabzada
geo-llmstxt

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.

Overview

Publisherzubair-trabzada
Repositorygeo-seo-claude
Skill namegeo-llmstxt
Stars
10.7K
Forks
1.6K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by zubair-trabzada on GitHub. Read the source before you install it.

Installation

Install the Geo Llmstxt AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zubair-trabzada/geo-seo-claude.git /tmp/geo-seo-claude
mkdir -p .claude/skills
cp -r /tmp/geo-seo-claude/skills/geo-llmstxt .claude/skills/geo-llmstxt
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Geo Llmstxt in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Geo Llmstxt on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Geo Llmstxt is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

markdown
# [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.

Detailed Format Rules

1. Title (Required)

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

2. Description (Required)

markdown
> Brief description of the site/business
  • 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:

Section NamePurposeExample Content
## DocsPrimary documentation or key pagesProduct pages, service descriptions, core content
## OptionalSecondary pages worth knowing aboutBlog posts, supplementary resources
## APIAPI documentationAPI reference, authentication guides
## BlogBlog or news contentRecent/popular articles
## ProductsProduct catalogProduct pages, pricing
## ServicesService offeringsService descriptions, process pages
## AboutCompany informationAbout page, team, mission
## ResourcesEducational/reference contentGuides, tutorials, whitepapers
## LegalLegal documentsTerms of service, privacy policy
## ContactContact informationContact page, support channels

4. Page Entries (Required)

Each entry follows the format:

markdown
- [Page Title](URL): Description of page content

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)

markdown
## Key Facts
- Founded in [year] by [founder(s)]
- Headquarters: [City, Country]
- [X] customers/users in [Y] countries
- Key products: [Product A], [Product B], [Product C]
- Industry: [Industry classification]

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

6. Contact Section (Recommended)

markdown
## Contact
- Website: https://example.com
- Email: hello@example.com
- Support: support@example.com
- Phone: +1-555-123-4567
- Address: 123 Main St, City, State, ZIP, Country

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:

Featurellms.txtllms-full.txt
LengthConcise (50-150 lines)Comprehensive (150-500+ lines)
Page entries10-30 key pages30-100+ pages
Descriptions10-30 words per entry30-100 words per entry, may include key facts from each page
AudienceQuick AI comprehensionDeep AI analysis
Sections3-6 sections8-15 sections
Key factsBusiness-level factsPage-level facts and data points

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:

ElementCheckSeverity if Missing
H1 TitlePresent, matches business nameCritical
Blockquote descriptionPresent, under 200 chars, factualHigh
At least one H2 sectionPresentCritical
Page entries with URLsAt least 5 entries presentHigh
URLs are absoluteAll URLs use full https:// pathsHigh
URLs are validAll URLs return 200 statusMedium
Descriptions presentEvery entry has a description after the colonMedium
Key Facts sectionPresent with business informationMedium
Contact sectionPresent with at least emailLow
Reasonable length30-200 linesLow
No broken MarkdownProper formatting throughoutMedium

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:

markdown
# [Site Name]

> [One clear sentence: what the business does, who it serves, and its primary value proposition. Under 200 characters.]

## Docs

- [Most Important Page](https://example.com/page): Description covering the key content on this page.
- [Second Page](https://example.com/page-2): Description of this page's content and value.
- [Third Page](https://example.com/page-3): What users and AI systems will find here.

## Products

- [Product A](https://example.com/product-a): Core features, target users, and pricing model for Product A.
- [Product B](https://example.com/product-b): What Product B does and how it differs from Product A.

## Resources

- [Guide Title](https://example.com/guide): Comprehensive guide covering [topic] with [X] sections and practical examples.
- [Blog Post](https://example.com/blog/post): Analysis of [topic] with original data from [source].

## Key Facts

- Founded in [year] by [name(s)]
- Headquartered in [City, Country]
- [Specific metric: e.g., "Serves 10,000+ businesses in 40 countries"]
- [Key differentiator: e.g., "Only platform offering real-time X and Y integration"]
- Industry: [Classification]

## Contact

- Website: https://example.com
- Email: [primary contact email]
- Support: [support URL or email]

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:

markdown
# llms.txt Analysis: [Domain]

**Analysis Date:** [Date]
**llms.txt Status:** [Found at URL / Not Found / Error]
**llms-full.txt Status:** [Found / Not Found]

---

## Overall llms.txt Score: [X]/100

| Dimension | Score |
|---|---|
| Completeness | [X]/100 |
| Accuracy | [X]/100 |
| Usefulness | [X]/100 |

---

## Format Validation

| Element | Status | Notes |
|---|---|---|
| H1 Title | [Pass/Fail] | [Notes] |
| Description blockquote | [Pass/Fail] | [Notes] |
| H2 Sections | [Pass/Fail] | [X sections found] |
| Page entries | [Pass/Fail] | [X entries found] |
| URL validity | [Pass/Fail] | [X broken URLs] |
| Entry descriptions | [Pass/Fail] | [X missing descriptions] |
| Key Facts | [Pass/Fail] | [Notes] |
| Contact section | [Pass/Fail] | [Notes] |

---

## Missing Pages

These important pages were found on the site but not in llms.txt:

1. [Page Title](URL) -- [Why it should be included]
2. [Page Title](URL) -- [Why it should be included]

## Improvement Recommendations

1. [Specific recommendation]
2. [Specific recommendation]
3. [Specific recommendation]

## Suggested Updated llms.txt

[Complete rewritten llms.txt file if significant improvements are needed]

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.

Frequently asked questions

What does the Geo Llmstxt AI skill do?

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.

Why use Geo Llmstxt on TypingMind?

Because you install it once and use it with any model. Geo Llmstxt is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Geo Llmstxt in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zubair-trabzada/geo-seo-claude/tree/main/skills/geo-llmstxt. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Geo Llmstxt?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Geo Llmstxt?

As many as you like. As long as a model supports skills, you can use Geo Llmstxt with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Geo Llmstxt AI skill free?

Yes. It is published on GitHub by zubair-trabzada under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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