MCP to explore websites with llms.txt files
An MCP server that provides LLMs access to other LLMs
Skill for a persistent LLM-managed wiki — the LLM writes and cross-references while you curate sources.
One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.
LLM-powered knowledge base from your Claude Code, Codex CLI, Copilot, Cursor & Gemini sessions. Karpathy's LLM Wiki pattern — implemented and shipped.
Reusable Skills for LLMQuant Agent, Claude Code, Claude.ai, Cursor, Hermes Agent, OpenClaw and Codex, grounded in LLMQuant Data
Agent-driven proactive memory CLI for AI agents — autonomously recall, maintain, and evolve persistent, source-grounded knowledge across sessions.
Expose llms-txt to IDEs for development
LLM-maintained personal knowledge base for Obsidian. Based on Andrej Karpathy's LLM Wiki pattern.
Beautiful, AI-native markdown IDE and LLM wiki
Go web crawler to scrape documentation sites and convert content to clean Markdown for LLM ingestion (RAG, training data).

Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
An MCP server implementation enabling LLMs to work with new APIs and frameworks
docx ↔ LLM translator. Projects .docx office files to Markdown for editing. Projects edits back to OOXML as tracked changes (redlines). Python and Node.js implementations.
An agent skill to evolve the quality of LLM-Wiki (Graphify) at test time.
SimpleMem: Efficient Lifelong Memory for LLM Agents — Text & Multimodal
An autonomous agent that conducts deep research on any data using any LLM providers
A MCP server to allow the LLM in Cursor to access Rust Analyzer, Crate Docs and Cargo Commands.
Tool-to-Agent Protocol: tools can be smart without embedded LLM calls.
A MCP server that supports mainstream eBook formats including EPUB, PDF and more. Simplify your eBook user experience with LLM.
Templates and workflow for generating PRDs, Tech Designs, and MVP and more using LLMs for AI IDEs
CTX: a tool that solves the context management gap when working with LLMs like ChatGPT or Claude. It helps developers organize and automatically collect information from their codebase into structured documents that can be easily shared with AI assistants.
A curated guide to convention files AI agents read, write, and act on: AGENTS.md, CLAUDE.md, SKILL.md, llms.txt, MCP configs, rules, and examples.
Markdown that steers an LLM is code. Genesis is the architectural layer for designing multi-agent, multi-skill systems -- with named patterns, contracts, and substrate portability, before you write them.