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Epstein LLM Friendly — AI skill for Claude Code

AI community

Open-source RAG system for analyzing publicly released Epstein investigation documents.

How to install Epstein LLM Friendly

This entry records only its repository, not the path inside it, so there is no exact command to give. Open avenoxai/epstein-llm-friendly and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Epstein LLM Friendly does

Open-source RAG system for analyzing publicly released Epstein investigation documents. MCP server integration for LLM agents.

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README

Epstein Dataset RAG System

An open-source Retrieval-Augmented Generation (RAG) system for analyzing publicly released documents from the Jeffrey Epstein investigation. Built to enable researchers, journalists, and investigators to search, analyze, and find connections across millions of pages of official DOJ, House Committee, and FBI releases.

Overview

This project provides:

Hybrid Search: Combined BM25 keyword + vector semantic search for accurate retrieval
Entity Graph: Extracted people, organizations, and locations with relationship mapping
Evidence-Grade Citations: Every result includes source, page number, and Bates numbers
MCP Server: Integrate with Claude, GPT, and other LLM agents via Model Context Protocol
Multi-Backend Embeddings: Support for Google Gemini API and OpenRouter

Data Sources

This system is designed to work with official public releases:

Source	Description
DOJ Epstein Library	~3.5M pages, 180K images, 2K+ videos
House Oversight Committee	Additional document releases
FBI FOIA Vault	FOIA releases

**Note**: This repository contains processing code and pre-computed embeddings. Raw documents should be downloaded from official sources.

Quick Start

Prerequisites

Python 3.10+
PostgreSQL (optional, SQLite works for development)
Meilisearch (for keyword search)

Installation

# Clone the repository
git clone https://github.com/yourusername/epsteindataset.git
cd epsteindataset

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install dependencies
pip install -r requirements.txt

# Download spaCy model for entity extraction
python -m spacy download en_core_web_sm

# Copy environment template
cp .env.