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llms.txt 检查器

A free llms.txt validator. Check a live URL or paste a draft, and the checker parses the file against the llmstxt.org specification: H1 title, blockquote summary, detail blocks and H2 link lists. You get a score out of 100, a grade, and the exact lines to fix.

Free · no sign-up · URL or pasted content

A bare domain works — we add https:// and look for /llms.txt and /llms-full.txt at the site root.

What is an llms.txt checker?

An llms.txt checker is a tool that tests whether an llms.txt file follows the llmstxt.org specification. This free validator fetches the file from your site, or reads a draft you paste, and checks five rules: Markdown format, one H1 title, a blockquote summary, heading-free detail blocks and H2 link lists. It returns a score out of 100, a grade and line-by-line fixes, with no sign-up.

The five rules the validator checks

Each rule maps to a requirement in the llmstxt.org specification. Every finding is marked as an error, a warning or a note.

Markdown format

The file is plain markdown that parses cleanly, with nothing the spec doesn't allow.

H1 title

Exactly one H1 with the site or project name, at the top. It is the only required section.

Blockquote summary

A short > summary directly under the H1, so the links that follow have context.

Detail blocks

Optional paragraphs or lists after the summary, with no headings before the first H2.

H2 file lists

Each H2 section holds - [name](url): note items, and a section named Optional comes last.

Score, grade and preview

Generate a file

A 0 to 100 score with an A to F grade, findings grouped by severity, and a rendered preview of the file.

How to validate an llms.txt file

Check, fix, publish, and check again.

  1. Pick an input mode

    Choose Check by URL to validate a file that is already live, or Paste content to validate a draft before you publish it.

  2. Enter your URL or file

    Type a bare domain, a site root, or a direct /llms.txt URL — the protocol is added for you. In paste mode, drop the full markdown into the text box.

  3. Run the check

    Press Check or Validate. The file is fetched, split into specification blocks, and scored against the five structural rules.

  4. Read the score and grade

    The score ring shows weighted compliance out of 100 with a letter grade, plus how many rules passed, warned, or failed.

  5. Work through the findings

    Each finding names the rule, the line number, and the exact change to make. Expand a rule card to see every detail behind its status.

  6. Fix, publish, and re-check

    Apply the fixes, upload the file to your site root so it is served at yourdomain.com/llms.txt, then run the URL back through the checker to confirm it passes.

How to read the llms.txt report

Start with the errors, then the warnings. Notes are suggestions, not failures.

Score and grade

A weighted score from 0 to 100 with a letter grade from A to F. Each of the five rules carries part of the weight.

Passed, warned, failed

How many of the five rules the file passes outright, passes with warnings, or fails.

Errors

Breaks the specification, such as a missing H1 or link lines that aren't list items. Fix these first.

Warnings

Allowed but likely to confuse a parser, such as a missing summary or an Optional section that isn't last.

Line numbers

Each finding points at the line to change, so you can fix the file in any text editor.

Preview

A rendered view of the title, summary and link sections, the way a program would read them.

什么是 llms.txt?

llms.txt is a markdown file published at the root of a website — yourdomain.com/llms.txt — that gives large language models a short, curated map of the site. Instead of asking a model to crawl navigation, scripts and boilerplate, the file hands it a title, a summary and a list of the pages that actually matter.

The format is defined by the specification at llmstxt.org. It is deliberately small: only H1 and H2 headings exist, and the body of each section is a list of links. That constraint is what makes the file both human-readable and trivially parseable by a program.

The spec also describes a companion convention, /llms-full.txt, which contains the expanded content itself rather than an index of links.

符合规范要求的顺序

  1. An optional byte-order mark at the very start of the file.
  2. An H1 with the name of the project or site — the only required section.
  3. A blockquote with a short summary containing the key information needed to understand the rest of the file.
  4. Zero or more markdown blocks — paragraphs, lists, tables — of any type except headings.
  5. Zero or more sections delimited by H2 headers, each containing a file list of `- [name](url)` items with optional `: notes`.
  6. A section named exactly “Optional” has special meaning: its URLs may be skipped when a shorter context is needed.

我们检查的 5 项验证规则

每一项检查都直接对应 llmstxt.org 规范中的一个条款。每个规则都会评分,加权 后的总分即为您的合规得分和字母评级。

Markdown 格式 markdown-format

文件必须是 Markdown 格式,且其中只能包含 H1 和 H2 标题。

在 /llms.txt 提供纯 Markdown 内容——不得包含 HTML、YAML 前置元数据,也不得包含 H3–H6 标题。任何深于 H2 的标题均不符合规范。

H1 项目标题 h1-title

包含项目或网站名称的 H1 标题。这是唯一必需的部分。

以单行 `# 您的网站名称` 开始文件。这是规范中强制要求的部分,且必须且只能出现一次。

引用块摘要 blockquote-summary

一个包含项目简短摘要的引用块,其中包含理解其余内容所需的关键信息。

在 H1 下方直接放置一个 `> 单段落摘要`。说明网站的用途及其服务对象,以便 AI 模型能够理解其下方的每个链接。

无标题的详情块 project-details

零个或多个任意类型的 Markdown 部分(例如段落、列表、表格,但不能包含标题),其中包含更详细的信息。

在摘要后添加可选的上下文,可使用段落、列表或表格形式。此处请勿使用标题——文件中的下一个标题应当是您的第一个 H2 部分。

带有文件列表的 H2 部分 file-lists

由 H2 标题分隔的零个或多个 Markdown 部分,包含可获取更多详细信息的 URL 文件列表。

将链接分组到 `## 部分名称` 标题下,每行一个链接,格式为 `- [名称](https://绝对网址):可选备注`。当需要更简短的上下文时,可以跳过名称恰好为 `Optional` 的部分,因此请将其放在最后。

如何编写符合规范的 llms.txt

实施指南

确保文件符合规范的六个关键决策。

  1. 以一个 H1 开头. 使用您的网站或项目名称——除可选的字节顺序标记外,其上方不应有任何内容。这是规范要求的唯一部分。
  2. 添加引用块摘要. 在 H1 下方直接写一个 `>` 段落,解释网站的用途,以便下方的链接具备上下文。
  3. 保持详情块无标题. 摘要之后允许出现额外的段落、列表或表格——但在出现第一个 H2 之前不得使用任何标题。
  4. 将链接分组到 H2 章节下. 根据模型会寻找的内容来命名章节:文档、指南、API、博客。此格式中仅存在 H1 和 H2。
  5. 精确编写文件列表项. `- [页面名称](https://example.com/page): 简短说明`。请使用绝对 URL,以便模型在没有基础 URL 的情况下也能解析它们。
  6. 将“可选”放在最后. 当需要更短的上下文时,可以跳过标题恰好为“可选”的章节。将其保留用于次要页面并放在末尾。

规范模板

发布在 llmstxt.org 上的标准示例 — 直接复制并填入您自己的内容。

# Title

> Optional description goes here

Optional details go here

## Section name

- [Link title](https://link_url): Optional link details

## Optional

- [Link title](https://link_url)

· Save it as llms.txt and upload it to your site root so it is served at yourdomain.com/llms.txt, then run it back through this checker.

The specification this checker follows is published and maintained at llmstxt.org.

What this llms.txt validator doesn't do

  • It checks structure against the specification, not whether the pages you list are your best ones or still load.
  • It doesn't fix the file for you; each finding says what to change.
  • A perfect score doesn't mean AI engines will read the file or cite you. llms.txt is a voluntary convention.
  • In URL mode it reads what your server returns at /llms.txt. If a firewall or bot protection blocks the request, paste the file instead.

Questions about llms.txt

Something else? Ask the team

What is llms.txt?

llms.txt is a markdown file published at the root of a website that gives large language models a short, curated map of the site. The specification at llmstxt.org defines its structure: an H1 with the project or site name, a blockquote summary, optional heading-free detail blocks, and H2 sections containing file lists of the form - [name](url): optional notes.

Where does the llms.txt file have to be located?

At the root of your domain, served at yourdomain.com/llms.txt. The specification also describes a companion file, /llms-full.txt, which holds the expanded content itself instead of an index of links. This checker looks for both when you validate by URL.

What does this llms.txt checker validate?

Five structural rules drawn from the specification: that the file is markdown using only H1 and H2 headings, that it opens with a single H1 project title, that a blockquote summary follows it, that any further detail blocks contain no headings, and that each H2 section contains a file list of link items. Each rule is weighted, and the total becomes a score out of 100 with a letter grade.

Is the blockquote summary required in llms.txt?

The H1 with the project name is the only section the specification marks as required. The blockquote summary is described as part of the expected structure and carries the key information needed to understand the rest of the file, so this checker treats a missing summary as a problem worth fixing rather than a hard failure of the format.

What does the Optional section mean in llms.txt?

A section named exactly Optional has special meaning in the specification: the URLs listed under it may be skipped when a shorter context is needed. Use it for secondary pages such as contact, legal and policy pages, and keep it as the last section in the file.

What is the difference between llms.txt and llms-full.txt?

llms.txt is a compact index — a title, a summary, and lists of links to the pages that matter. llms-full.txt is the companion convention that contains the full content itself in one file, for cases where a model should read the material rather than follow links to it.

Does llms.txt replace robots.txt or a sitemap?

No. robots.txt tells crawlers what they are allowed to fetch and a sitemap lists every indexable URL. llms.txt does neither — it is a curated summary that tells a language model which content is worth reading and how the site fits together. They serve different purposes and can all coexist at the site root.

Do I need an account to use this llms.txt checker?

No. The checker is free and requires no sign-up. You can validate a live URL or paste the contents of a draft file, and the results are returned to you without being published anywhere.

How do I validate an llms.txt file?

Enter your site address (the checker fetches /llms.txt) or paste the file's contents, then run the check. Fix each error and warning it lists, publish the file at your site root, and run the check again until the score is where you want it.

Is this llms.txt validator free?

Yes. The checker is free and needs no account. It works on a live URL or on pasted content, so you can test a draft before you publish it.

See whether AI engines use your pages

A valid llms.txt is a map. AI Rank Lab shows whether ChatGPT, Claude, Gemini, Perplexity and Google's AI answers actually mention and cite you.

Brand Visibility

Run the prompts your buyers ask on six AI engines and record whether your brand is mentioned, where, in what tone, and which URLs are cited. Every run is stored so you can compare over time.

  • ChatGPT, Claude, Gemini, Perplexity, AI Overviews and AI Mode
  • Mentions, position, sentiment and cited URLs
  • Runs when you choose, stored for comparison
See brand visibility
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Brand Visibility

12 prompts · last run

Illustrative data
Mentioned in answersshare of prompts
  • ChatGPT42%
  • Claude35%
  • Gemini28%
  • Perplexity51%
  • AI Overviews22%
  • AI Mode19%

Runs when you choose to run it. Every run is stored so you can compare over time.

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