MCP server for semantic code research and context generation on real-time using LLM patterns | Search naturally across public & private repos based on your permissions | Transform any accessible codebase/s into AI-optimized knowledge on simple and complex flows | Find real implementations and live docs from anywhere

A Claude Code plugin that optimizes documentation for AI coding assistants like Claude, GitHub Copilot, and other LLMs. Makes your docs more effective through c7score optimization, llms.txt generation, question-driven restructuring, and automated quality scoring.

🚀 Lightweight Python library for building production LLM applications with smart context management and automatic token optimization. Save 10-20% on API costs while fitting RAG docs, chat history, and prompts into your token budget.

🧠 Context Engineering is an open-source toolkit to visualize, test, and optimize how LLMs process context windows. Includes a Streamlit app, RAG simulation, educational docs, and Docker support—perfect for prompt engineers, researchers, and AI developers.

A Claude Skill collection designed for task automation, document parsing, and intelligent dispatch. such as utilities for webpage-to-Markdown conversion, API doc parsing, and structured JSON extraction—optimized for LLM-driven workflows

AI_Doc_Editor — A developer-focused AI-powered document editor that leverages NLP and LLMs for intelligent text manipulation. Features include auto-formatting, context-aware editing, summarization, and content optimization through an interactive, extensible interface.

AI-first React component library for coding agents. LLM-optimized docs, sensible defaults, zero config. Built on shadcn patterns, Base UI, and Tailwind CSS v4.

From GitHub docs to LLM-ready context, optimized for code generation.

Tools and repository of LLM optimized docs to use in context

Community operational docs for nanobot — deployment paths, LLM setup, skills, governance, cost optimization, and recovery guides.

React web app that writes technical docs for your project using the evaluator-optimizer pattern, an LLM-based agent-like looping workflow.

📚 GitHub Action to generate LLM-optimized documentation (llms.txt + markdown.zip) from Docusaurus sites. Make your docs accessible to AI assistants.

🚀 Optimize your documentation for AI coding assistants with improved quality and navigation, making it easier for developers to get help.

tasuku_llm_docs_parser is a Phoenix LiveView and JSON API service that accepts raw documentation text and returns LLM-optimized, structured docs in real time.

Generate token-optimized documentation context for LLMs. Synthesize docs from web search, website crawling, and GitHub repositories.

docs.cobru.co — Official Cobru API documentation. Bilingual (es/en), OpenAPI 3.1, LLM-optimized.

JabbarRoot AI extension transforms ideas into structured software artifacts (code, docs) via intelligent workflows. It optimizes context for LLMs, manages complexity, and industrializes development for augmented software cognition.

A Token Gater system that selects only relevant past chat messages and external docs per query to optimize LLM context windows, reducing noise, cost, and improving memory relevance.

Transform any documentation website into AI-ready skill files. High-performance CLI tool built in Rust for crawling docs and generating structured SKILL.md files optimized for LLM agents.

Financial Document Analyzer is an AI-powered tool using CrewAI + LLMs to extract and summarize insights from PDFs/DOCs. Features include bug fixes, optimized prompts, PDF report generation, SQLite database for storing results, and Celery + Redis for concurrent processing.

Financial Document Analyzer is an AI-powered tool using CrewAI + LLMs to extract and summarize insights from PDFs/DOCs. Features include bug fixes, optimized prompts, PDF report generation, SQLite database for storing results, and Celery + Redis for concurrent processing.

Extracts a structured summary (including doc comments) of all classes, structs, protocols, enums, typealiases, variables, and functions from all .swift files in a directory (recursively). Optimized for LLM ingestion and codebase documentation.

backend system that powers a Knowledge Assistant API, which uses an LLM to answer user questions based on a provided external knowledge base (PDFs, Docs, or Markdown files). The goal is to optimize responses by reducing hallucinations and improving relevance using retrieval-based techniques.