250+ real-world TypeScript AI projects: workflows, agents, and multi-agent systems with production-ready architecture, not chatbot demos.

Systematic framework for planning and writing academic papers using Claude Code. Includes strategist (planning) and composer (writing) skills with quality checkpoints.

Collection of Claude Skills for DSPy framework - program language models, optimize prompts, and build RAG pipelines systematically

A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing

An advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.

Comprehensive MCP server exposing dozens of capabilities to AI agents: multi-provider LLM delegation, browser automation, document processing, vector ops, and cognitive memory systems

Claude Code skills for Reddit automation (AppleScript + Chrome). Build karma, post to subreddits — undetectable by anti-bot systems.

All in one plugin for Hermes Agent ⚚ the coding intelligence, a long-term memory system and model optimized workflow packages

Threat hunting command system for agentic IDEs

List MCP Server configurations in your system used by AI applications like Cursor, Claude Desktop, VS Code and others

A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases.

Reverse-engineering Claude Code's 512K LOC TypeScript source: agent loop, tool system, permission model, Grove training pipeline, anti-distillation defense

Learn Anything is an AI-powered recursive learning system — Socratic deep-dives and TDD-style exercises integrated directly into your coding assistant.

Samurai-inspired multi-agent system for Claude Code. Orchestrate parallel AI tasks via tmux with shogun → karo → ashigaru hierarchy.

The system of action for AI-native cybersecurity—where intent becomes governed execution, evidence becomes operational memory, and every operation improves the next.

Model router for agentic systems. Routes every prompt to the right model in <50ms. Cut costs 40-70% with just an endpoint change.

An awesome Claude skill for structured marketing research system. Covers competitor analysis, avatar profiling, positioning, value propositions, and mental models.

Lightweight Long-Term Memory for LLM Agents.

A memory system using mem0 for AI applications. Enables long-term memory for AI agents as a drop-in MCP server.

A selective learning and memory substrate for agentic systems — typed, revisable, decayable memory with competence learning and trust-aware retrieval.

A Model Context Protocol (MCP) server that provides file system context to Large Language Models (LLMs). This server enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.

A modular Python framework implementing the Model Context Protocol (MCP). It features a standardized client-server architecture over StdIO, integrating LLMs with external tools, real-time weather data fetching, and an advanced RAG (Retrieval-Augmented Generation) system.

CVE hunting harness for Claude Code - 20 skills, 5-agent team, systematic vulnerability research with false positive elimination