How do I install the LLM Parsability skill?

Run npx skillmds@latest add thedaviddias/llm-parsability in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is LLM Parsability safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with LLM Parsability?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is LLM Parsability free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published LLM Parsability?

thedaviddias (@thedaviddias) published this skill. Their other Agent Skills are listed on their SkillMD profile.

LLM Parsability by thedaviddias — Agent Skill

Name: LLM Parsability
Author: thedaviddias

File contents

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 (–, , , , )? (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 (, , ), (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/en/rules/seo/llm-parsability

---name: llm-parsabilitydescription: 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.---# Make content easy for LLMs to parseAI 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## CheckEvaluate 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?## FixRestructure 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.## ExplainLarge 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 ReviewCheck 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/en/rules/seo/llm-parsability

Frequently asked questions

LLM Parsability