Optimizes documentation, skills, and prompt structures to enhance AI assistant performance and token efficiency.
LLM Boost is a comprehensive toolkit designed to refine how AI models interact with your codebase and documentation. It provides standardized workflows for improving documentation using the c7score metric, generating LLM-optimized project manifests (llms.txt), and enforcing best practices for Claude Code skills like the 500-line rule. By implementing progressive disclosure and semantic XML tagging, the skill ensures that your prompts and references are token-efficient and highly performant across various LLM tasks, from code generation to complex reasoning.
Key Features
01Standardized llms.txt generation for AI-friendly project maps
02CLAUDE.md verification and LLM parameter tuning guidance
03Automated c7score documentation auditing and optimization
04Skill footprint reduction using the 500-line rule and external references
050 GitHub stars
06Semantic XML tag structuring for improved prompt engineering
Use Cases
01Preparing a repository for consumption by AI coding assistants like Claude Code
02Improving the accuracy of AI-generated code by structuring documentation into searchable snippets
03Reducing token usage in custom skills through progressive disclosure patterns
