LLM Docs Optimizer

About

LLM Docs Optimizer enhances the effectiveness of project documentation for AI tools like Claude and GitHub Copilot by applying the C7Score quality benchmark. It restructures content using a question-driven approach, removes irrelevant metadata, generates standardized llms.txt navigation files, and provides automated quality scoring across metrics like code snippet relevancy and formatting. By transforming static API references into practical, self-contained examples, it ensures that AI assistants can accurately retrieve and apply information from your codebase more effectively during retrieval-augmented generation tasks.

Key Features

• Transformation of API references into runnable, context-rich code examples
• C7Score optimization across five key documentation quality metrics
• Automated generation of LLM-friendly navigation files (llms.txt)
• Question-driven content restructuring based on developer needs
• 34 GitHub stars
• Detailed documentation analysis and quality impact reporting

Use Cases

• Improving README files to ensure AI coding assistants provide accurate code suggestions
• Analyzing and scoring documentation quality against industry-standard AI retrieval benchmarks
• Creating standardized llms.txt files to help LLMs navigate complex project documentation