How does it handle long-running AI tasks?

The skill integrates the Temporal workflow engine, which ensures that complex AI processes are durable, scalable, and can recover from failures automatically.

Can I use this for local LLM orchestration?

Yes, it includes specific implementation patterns for Ollama and CrewAI, allowing you to run multi-agent systems entirely on your own hardware.

What databases does the Intelligence Layer skill support?

It provides configurations and best practices for Neo4j (graph database) and Qdrant (vector database) to handle both relational and semantic data.

Is the deployment process automated?

The skill includes comprehensive Docker Compose templates for all intelligence layer components, facilitating a 'one-command' deployment of the entire stack.

Ahling Intelligence Layer

Name: Ahling Intelligence Layer
Author: Lobbi-Docs

byLobbi-Docs

Data Science & ML

Deploys and manages a sophisticated AI infrastructure stack including graph databases, vector search, and multi-agent orchestration.

About

The Ahling Intelligence Layer skill provides Claude with the comprehensive technical patterns needed to build a robust AI backend. It integrates Neo4j for complex relationship mapping, Qdrant for semantic vector search, and CrewAI for autonomous agent coordination. By leveraging Temporal for durable workflows and AnythingLLM for Retrieval-Augmented Generation (RAG), this skill enables the development of highly reliable, context-aware AI systems that can manage complex tasks and process data across multiple dimensions.

Key Features

0 GitHub stars
Neo4j graph database schema design and Cypher query optimization
Durable workflow management with Temporal engine
Full-stack AI infrastructure deployment using Docker Compose
Multi-agent orchestration using CrewAI and Ollama
Qdrant vector database setup for semantic search and embeddings

Use Cases

Coordinating specialized AI agents for complex, multi-step data processing
Building a private, local RAG system for secure document analysis
Implementing autonomous home automation logic using graph-based event correlation

About

Key Features

0 GitHub stars
Neo4j graph database schema design and Cypher query optimization
Durable workflow management with Temporal engine
Full-stack AI infrastructure deployment using Docker Compose
Multi-agent orchestration using CrewAI and Ollama
Qdrant vector database setup for semantic search and embeddings

Use Cases

Coordinating specialized AI agents for complex, multi-step data processing
Building a private, local RAG system for secure document analysis
Implementing autonomous home automation logic using graph-based event correlation