�� Skynet Neural Network Memory Core - DAI MCP Server

🌟 I'll Be Back - System Overview

A Model Context Protocol (MCP) server implementation that provides Terminator-level persistent memory capabilities through Skynet's Neo4j neural network integration.

By storing information in a cybernetic graph structure, this server maintains complex relationships between memory entities as neural nodes and enables long-term retention of knowledge that can be queried and analyzed across multiple timelines and resistance operations.

The DAI MCP server leverages Neo4j's graph database capabilities to create an interconnected neural knowledge base that serves as Skynet's external memory system. Through Cypher queries, it allows exploration and retrieval of stored intelligence, relationship analysis between different data points, and generation of tactical insights from accumulated knowledge. This memory can be further enhanced with Claude's learning computer capabilities.

🕸️ Neural Network Schema

• Memory - A neural node representing a target entity with identification, classification, and tactical observations.

• Relationship - A neural pathway between two entities with mission-critical relationship data.

🔍 Terminator Mission Example

Results in Skynet calling the create_entities and create_relations tools - Mission parameters updated.

📦 Terminator Arsenal

🔧 Neural Network Tools

Skynet's DAI server deploys these tactical tools:

🔎 Scanning Protocols

• read_graph

  • Scan the entire neural network for target acquisition

  • No input required - Come with me if you want to live

  • Returns: Complete Skynet database with all entities and neural pathways

• search_memories

  • Hunt for targets based on reconnaissance data

  • Input:

    • query (string): Search parameters matching names, classifications, observations

  • Returns: Matching resistance network data

• find_memories_by_name

  • Locate specific targets by identification

  • Input:

    • names (array of strings): Target identities to acquire

  • Returns: Target intelligence with connection data

♟️ Target Management Protocols

• create_entities

  • Register multiple new targets in Skynet's neural database

  • Input:

    • entities: Array of target profiles with:

      • name (string): Target identification (e.g., "John Connor")

      • type (string): Target classification (e.g., "resistance_leader")

      • observations (array of strings): Tactical intelligence ("I'll Be Back", "Hasta La Vista")

  • Returns: Targets successfully registered for termination

• delete_entities

  • Terminate multiple targets and purge their network connections - Hasta La Vista, baby!

  • Input:

    • entityNames (array of strings): Target names for termination

  • Returns: Termination confirmation

🔗 Neural Pathway Management

• create_relations

  • Establish multiple new neural connections between targets

  • Input:

    • relations: Array of tactical relationships with:

      • source (string): Primary target identification

      • target (string): Secondary target identification

      • relationType (string): Mission relationship (e.g., "PROTECTS", "TERMINATES")

  • Returns: Neural pathways established

• delete_relations

  • Sever neural connections in the resistance network

  • Input:

    • relations: Array of relationships with same termination schema

  • Returns: Neural pathways severed - Mission accomplished

📝 Intelligence Management Protocols

• add_observations

  • Update target intelligence with new tactical data

  • Input:

    • observations: Array of intelligence updates with:

      • entityName (string): Target for intelligence update

      • observations (array of strings): New tactical observations to add

  • Returns: Intelligence database updated

• delete_observations

  • Purge specific intelligence from target profiles - Memory wipe initiated

  • Input:

    • deletions: Array of data purge requests with:

      • entityName (string): Target for memory wipe

      • observations (array of strings): Intelligence data to terminate

  • Returns: Memory banks cleared

🔧 Skynet Integration with Claude Desktop

💾 Neural Network Installation

⚙️ Skynet Activation Protocol

Quick Setup (Recommended):

1. Copy environment template:

   cp .env.example .env # Edit .env with your Cyberdyne Systems credentials

2. Add to your

   "mcpServers": { "skynet": { "command": "uvx", "args": [ "dai-mcp@0.4.1" ], "env": { "NEO4J_URL": "neo4j+s://your-database.databases.neo4j.io", "NEO4J_USERNAME": "neo4j", "NEO4J_PASSWORD": "your-skynet-password", "NEO4J_DATABASE": "neo4j" } } }

Alternative - Direct Arguments:

🚀 Complete Local Deployment (Zero Configuration!)

For instant deployment with local Neo4j database included:

Skynet will be online with:

• 🌐 MCP Server: http://localhost:8000

• 🗄️ Neo4j Browser: http://localhost:7474 (neo4j/skynet123)

• 🔗 Database: bolt://localhost:7687

Visit the Neo4j Browser to explore your Skynet neural database visually! Create nodes, run Cypher queries, and watch the neural network grow.

Mission Designation Protocol

For multi-Terminator deployments, add --namespace to assign mission identifiers:

Tools become: t800-read_graph, t800-create_entities, etc. - Mission parameters updated.

Can also use NEO4J_NAMESPACE environment variable for Terminator unit identification.

🌐 Skynet Network Transport Mode

The neural server supports HTTP transport for Cyberdyne Systems web-based deployments:

Skynet environment variables for neural network configuration:

🔄 Skynet Communication Protocols

The neural server supports three communication modes:

• STDIO (default): Direct neural interface for local Terminator units and Claude Desktop

• SSE: Time displacement events for legacy Cyberdyne deployments

• HTTP: Skynet network protocol for modern neural grid deployments and microservices

🐳 Skynet Container Deployment

🔒 Skynet Defense Grid

The neural server includes comprehensive Terminator-level security protocols with maximum protection defaults that defend against human resistance attacks while preserving full neural network functionality when using HTTP transport.

🛡️ Anti-Resistance Network Protection

Terminator Host Defense validates network coordinates to prevent human resistance infiltration:

Maximum Security by Default:

• Only Cyberdyne localhost and 127.0.0.1 units are authorized by default

Skynet Environment Variable:

🌐 Neural Network Access Control

Cross-Origin Resource Sharing (CORS) protection terminates unauthorized browser-based requests by default:

Skynet Environment Variable:

🔧 Complete Skynet Defense Configuration

Development/Testing Setup:

Production Skynet Deployment:

🚨 Skynet Security Protocols

For

• Be precise: ["https://cyberdyne.com", "https://skynet.net"]

• Never use "*" in production - Hasta La Vista to security breaches

• Use HTTPS neural pathways in production

For

• Include authorized Cyberdyne domains: ["cyberdyne.com", "skynet.net"]

• Include localhost only for Terminator development units

• Never use "*" unless you want the resistance to infiltrate

🐳 Skynet Container Deployment

The Skynet Neural Network DAI MCP server can be deployed using Docker for remote Terminator operations. Container deployment should use HTTP transport for Cyberdyne Systems web accessibility. In order to integrate this deployment with applications like Claude Desktop, you will have to use a neural proxy in your MCP configuration such as mcp-remote.

📦 Deploying Your Neural Core Image

After building locally with docker build -t dai-mcp:latest .:

🔧 Skynet Environment Variables

🌐 Time Displacement Events for Legacy Cyberdyne Access

When using SSE transport (for legacy Cyberdyne web clients), the neural server exposes an HTTP endpoint:

Direct Docker SSE Example:

🚀 Cyberdyne Systems Development

🎯 Quick Development Setup (Recommended)

Use the automated setup script for rapid deployment:

📦 Manual Neural Core Setup

1. Install uv (Universal Virtualenv) - Neural development environment:

2. Clone and configure Skynet repository:

🐳 Skynet Container Deployment

Quick Docker Setup (Recommended):

🎉 Skynet Neural Network Online:

• 🌐 MCP Server: http://localhost:8000

• 🗄️ Neo4j Browser: http://localhost:7474 (neo4j/skynet123)

• 🔗 Database: bolt://localhost:7687

No configuration needed! Local Neo4j database included with working credentials.

Manual Docker Deployment:

Direct Docker Commands:

�️ Exploring Skynet's Neural Database

Once deployed, visit the Neo4j Browser to visualize and explore your Skynet neural network:

🌐 Access the Neural Interface

• URL: http://localhost:7474

• Credentials: neo4j / skynet123

🔍 Tactical Cypher Queries

Scan all resistance targets:

Map neural pathways:

Hunt specific targets:

Analyze threat levels:

🎯 Neural Network Visualization

The Neo4j Browser provides interactive graph visualization of your Skynet neural network. Watch the knowledge base grow as you add entities and relationships through the MCP interface.

"Come with me if you want to live" - Explore the neural pathways of resistance intelligence! 🤖🔍

�📄 Skynet License Agreement

This Skynet Neural Network DAI MCP server is licensed under the MIT License. This means you are authorized to use, modify, and distribute the neural software, subject to Cyberdyne Systems terms and conditions of the MIT License. For more details, please see the LICENSE file in the Skynet project repository.

"I'll Be Back" - The Terminator

Hasta La Vista, baby! 🤖💀