Optimize markdown documentation for LLMs and RAG systems, reducing token consumption by 67-95%.
llm-docs-builder is easy to set up with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
Repository setup guidance
gh repo view mensfeld/llm-docs-builder --webOpen the official repository or website.
Check the README for package manager, auth, and platform requirements.
Try it in a small test task inside your agent workflow.
This tool takes your existing documentation and makes it smaller and cleaner so AI models can understand it better. It removes unnecessary parts like navigation bars and footers, and converts HTML to markdown. This saves up to 95% of the tokens (words) that AI models need to process, making your docs cheaper and faster to use with LLMs.
llm-docs-builder is a Ruby-based CLI tool that transforms and optimizes markdown documentation for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It addresses the problem that LLMs waste 70-90% of their context window on human-oriented HTML content (navigation, footers, JavaScript, CSS). The tool normalizes links, removes unnecessary content, optimizes documents for LLM context windows, and enhances documents for RAG retrieval with hierarchical heading context and metadata. It can automatically convert HTML to markdown, measure token savings by comparing human vs AI versions, and generate llms.txt files for standardized documentation indexing. Real-world tests show an average reduction of 83% fewer tokens. The tool supports Docker and RubyGems installation, and can process local files or remote URLs.
Strong trust signals; still review the README and permissions before production use.
Last commit was about 53 days ago.
92 GitHub stars indicate community interest.
5 open issues signal maintenance load.
MIT license detected.
Optimize project documentation for AI coding assistants like GitHub Copilot or Cursor.
Generate llms.txt files for standardized documentation indexing across multiple projects.
Reduce token costs when using LLMs to answer questions about your software.
Prepare documentation for RAG systems to improve retrieval accuracy.
Compare token efficiency of human vs AI-optimized documentation pages.
The tool modifies documentation files; ensure you have backups before running bulk transformations.
When fetching remote URLs, the tool sends requests to those servers; respect robots.txt and rate limits.
Token reduction may remove content that is important for human readers; review optimized output.
92
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5
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MIT
License
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Optimize markdown documentation for LLMs and RAG systems, reducing token consumption by 67-95%.
Open-source maintainers who want their docs to be AI-friendly., Developers building RAG pipelines that ingest documentation., Technical writers optimizing documentation for LLM consumption.
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
The tool modifies documentation files; ensure you have backups before running bulk transformations. When fetching remote URLs, the tool sends requests to those servers; respect robots.txt and rate limits.
Similar or complementary options to evaluate include geo-optimizer-skill, superseo-skills, mcp-gsc.
Jun 7, 2026
3 security/trust notes recorded.
Setup difficulty is 2/5.