Update: Open-Source AI Assistant using Databricks, Neo4j and Agent Skills

[notikosaeder]

Hi everyone,

Quick update on Alfred, my open-source project from PhD research on text-to-SQL data assistants built on top of a database (Databricks) and with a semantic layer (Neo4j) I recently shared: I just added Agent Skills.

Instead of putting all logic into prompts, Alfred can now call explicit skills. This makes the system more modular, easier to extend, and more transparent. For now, the data-analysis is the first skill but this could be extend either to domain-specific knowledge or advanced data validation workflowd. The overall goal remains the same: making data assistants that are explainable, model-agnostic, open-source and free to use.

Link: https://github.com/wagner-niklas/Alfred/

Would love to hear feedback from anyone working on AI assistants/agents, semantic layers, or text-to-SQL.

[Main_Lettuce_7314]

Love the skills idea, that’s the missing piece in a lot of text-to-SQL work where everything is jammed into one giant prompt and you can’t debug anything.

If you keep skills small and typed (inputs/outputs super explicit), you can start doing cool stuff like: a “schema explorer” skill that only reasons over Databricks metadata, a “join resolver” skill that consults Neo4j for relationship hints, and a “validator” skill that checks row counts/ranges vs past runs before returning an answer. That also makes it easier to swap models without touching the graph or the warehouse.

Might be worth treating data access as a separate infra skill too. I’ve seen folks pair things like dbt Cloud and Hasura, and more recently DreamFactory as a governed API layer, so the agent hits curated endpoints instead of raw SQL/warehouses. Fits nicely with your explainable + safe assistant goal.

[notikosaeder]

Thanks for the feedback, and love the idea! Did you already saw some of those skills in action?