amantus-ai

LLM Codes — AI skill for Claude Code

AI community

Transform developer documentation to clean Markdown.

How to install LLM Codes

This entry records only its repository, not the path inside it, so there is no exact command to give. Open amantus-ai/llm-codes and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What LLM Codes does

Transform developer documentation to clean Markdown.

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README

llm.codes 📖 - Transform Developer Documentation for AI Agents

A high-performance web service that converts JavaScript-heavy documentation sites into clean, LLM-optimized Markdown. Built specifically to solve the problem of AI agents being unable to parse modern documentation sites that rely heavily on client-side rendering.

**Why llm.codes exists**: Modern AI agents like Claude Code struggle with JavaScript-heavy documentation sites, particularly Apple's developer docs. This tool bridges that gap by converting dynamic content into clean, parseable Markdown that AI agents can actually use.

📖 **Read the full story**: [How llm.codes Transforms Developer Documentation for AI Agents](https://steipete.me/posts/llm-codes-transform-developer-docs)

![Web Documentation to Markdown Converter](https://img.shields.io/badge/Next.js-15-black?style=flat-square&logo=nextdotjs) ![Tailwind CSS](https://img.shields.io/badge/Tailwind_CSS-v4-38B2AC?style=flat-square&logo=tailwind-css) ![TypeScript](https://img.shields.io/badge/TypeScript-5.8-blue?style=flat-square&logo=typescript) ![License](https://img.shields.io/badge/License-MIT-green?style=flat-square)

Technical Architecture

Core Problem Solved

Modern documentation sites (especially Apple's) use heavy JavaScript rendering that makes content invisible to AI agents. llm.codes solves this by:

  • Using Firecrawl's headless browser to execute JavaScript and capture fully-rendered content
  • Converting dynamic HTML to clean, semantic Markdown
  • Removing noise (navigation, footers, duplicate content) that wastes AI context tokens
  • Providing parallel URL processing for efficient multi-page documentation crawling

Key Features

  • Parallel Processing: Fetches up to 20 URLs concurrently using batched promises
  • Smart Caching: Redis-backed 30-day cache reduces API calls and improves response times
  • Content Filtering: Multiple filtering strategies to remove:
    • Navigation elements and boilerplate
    • Platform ava