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llm-parsability

Audit informational pages so AI assistants can extract and understand their content. Review headings, HTML structure, content availability without JavaScript, and structured data, then suggest changes that make individual sections clearer when read on their own.

OriginalShow the original description

Use when auditing content pages for AI discoverability. Applies to any informational page intended to appear in AI-generated answers, search snippets, or knowledge base extraction.

AI rating100/ 100See how to install

Good for

  • Review pages intended for AI-generated answers
  • Make headings and sections understandable on their own
  • Check whether key content is available without JavaScript
  • Review structured data and FAQ sections

Overview

What it does

llm-parsability checks whether a web page is structured so AI assistants can extract its main text and understand its meaning. It reviews the heading hierarchy, HTML for paragraphs, lists, and tables, and whether the main content is available without JavaScript. It also checks for FAQs and JSON-LD structured data, which tells machines what type of content the page contains.

When it helps

Use it to review explanatory pages intended for AI-generated answers, search result snippets, or information extraction into a knowledge base. It also helps when headings and opening sentences need to make sense outside the full page, or when you want to check text that appears only after an interaction, such as opening a tab.

Example requests

  • “Check whether AI assistants can easily read this explanatory page.”
  • “Revise the headings so each section makes sense as an answer on its own.”
  • “Return the main content in the initial HTML and consider Article, FAQPage, or HowTo structured data that fits the content.”

Notes

There are no special notes.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt explicitly defines the audit targets for pages intended for AI content extraction.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt specifies checks and changes for heading hierarchy, initial HTML, and JSON-LD.
SafetyAre sending data, deleting files, and running commands disclosed?
AGoodThe instructions cover HTML structure and content edits, with no deletion or credential operations.
Easy setupDoes it need few tools or API keys, and are they documented?
AGoodThe work centers on HTML and content review, with no dedicated tools or API keys specified.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 23, 2026. Treat it as a guide and read the original before installing.

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Files included (2)

  • SKILL.md2.7 KB
  • references/rule.md5.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Make content easy for LLMs to parse

AI assistants and answer engines (including Google's AI Overviews) extract and cite content from web pages—pages with clear structure and explicit context are more likely to be accurately cited and surfaced in AI-generated responses.

Quick Reference

  • Use semantic HTML headings, paragraphs, and lists — LLMs prefer structured markup
  • Avoid content locked behind JavaScript rendering or requiring user interaction
  • Write clear, self-contained sections that make sense out of full-page context
  • Structured data (JSON-LD) provides machine-readable context alongside human-readable text

Check

Evaluate whether the page content is parseable by an LLM. Check: (1) Is content in semantic HTML tags (<h1>–<h6>, <p>, <ul>, <ol>, <table>)? (2) Is key content accessible without JavaScript? (3) Are section headings descriptive enough to stand alone? (4) Does the page have JSON-LD structured data? (5) Are there FAQ sections or explicit Q&A patterns that match common search queries?

Fix

Restructure content into explicit HTML sections with descriptive headings. Replace JavaScript-rendered content with server-side rendered HTML. Add JSON-LD schema (Article, FAQPage, HowTo) to annotate the content type. Write headings and lead sentences that work as standalone answers—assume the reader only sees one paragraph.

Explain

Large language models and answer engines process web content by extracting text from HTML. Pages that use semantic markup, clear headings, and server-rendered content are parsed more accurately than JavaScript-heavy or visually-structured pages. As AI-generated answers increasingly cite specific web sources, well-structured content is more likely to be accurately quoted and linked.

Code Review

Check the page's rendered HTML for: (1) proper heading hierarchy (h1→h2→h3), (2) content wrapped in semantic elements (<article>, <section>, <main>), (3) key content visible in initial HTML response (not injected by JS), (4) presence of FAQPage, HowTo, or Article JSON-LD schema, (5) absence of content hidden behind modals, tabs, or accordions that require JS interaction.


For full implementation details, code examples, and framework-specific guidance, see references/rule.md.

Rule page: https://frontendchecklist.io/rules/seo/llm-parsability

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