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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
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?
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.
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.
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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Create and review custom 404 pages that help visitors recover from broken or moved links through search and navigation. Check the rendered page and HTTP response so missing content returns a proper 404 status.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Audit or create a dedicated About page that explains a website’s purpose and identifies its people or organization. Review the rendered output and supporting information, including expertise and contact details, to find gaps.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review and add automated accessibility checks for components, pages, and continuous integration workflows. Use axe-based tools to detect structural and attribute issues, while identifying checks that still need keyboard or screen reader testing.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review sign-in, registration, multi-factor authentication, and account recovery for accessibility barriers. Find blocked paste, difficult code entry, and inaccessible backup methods, then suggest authentication paths that support password managers, autofill, or device approval.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review form errors, success messages, and live updates so screen reader users receive important notifications. Check the HTML delivered to the browser, announcement settings, display duration, and dismissal controls, then identify specific elements that need changes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review HTML tables so screen reader users can understand how headers and data relate. Check header markup, table descriptions, and merged cells, then identify specific fixes and ways to verify them.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.