🎯Skills6
The open context layer for AI agents over business data — provides semantic modeling so agents understand what your data means, enabling accurate data querying without manual configuration.
Guides AI agents through generating a Wren MDL (Modeling Definition Language) project by exploring a database schema, normalizing types, and scaffolding YAML model files for use with the WrenAI semantic context layer.
Agent workflow guide for the Wren Engine CLI that enables end-to-end data analysis — from gathering schema context and recalling past queries to writing SQL through the MDL semantic layer and executing results.
Walks AI agents through Wren Engine onboarding — environment checks, project scaffolding, connection configuration, and first query — with step-by-step procedural guidance.
Part of WrenAI, an open context layer that models business data semantics so AI agents can accurately query 20+ data sources including PostgreSQL, BigQuery, Snowflake, and Spark through a Rust-powered semantic engine built on Apache DataFusion.
WrenAI is an open-source context layer that provides AI agents with business semantics, examples, memory, and governance over data, enabling accurate SQL generation and data querying across any agent framework.