Materialize helps engineering teams use SQL to transform siloed operational data into a live semantic representation of their business to power apps and agents.
Materialize's skills help AI agents work with its SQL-based streaming engine, covering environment health checks, performance and memory investigation, freshness debugging for materialized views and sinks, and deployment on AWS, Azure, and GCP through Terraform modules and the Terraform provider. Its most-used skills also support declarative SQL project management with mz-deploy, documentation lookup, and generating realistic demo data. Together they cover the full operational lifecycle of Materialize, from provisioning and configuration to tuning and troubleshooting live data pipelines.
Agent Skills
11 skills · 3 stars · NOASSERTION
Mz Dbt Using the dbt-materialize adapter to manage Materialize streaming pipelines with dbt.
Mz Debug Freshness Use when a Materialize object is behind wall-clock time — high lag in mz_wallclock_global_lag, a…
Mz Demo Data Trigger: "demo data", "synthetic data", "live operational data", "generate fake data", "create a…
Mz Deploy Using the mz-deploy CLI to manage a declarative SQL project for Materialize — project layout and…
Mz Docs Materialize documentation for SQL syntax, data ingestion, concepts, and best practices.
Mz Health Check Analyze a Materialize environment via the MCP Developer endpoint, and/or configure an MCP client…
Mz Ontology Design Designing and reviewing the semantic layer of a Materialize SQL code base as a canonical ontology…
Mz Optimize Memory Reduce the memory footprint and cost of Materialize compute clusters: find where arrangement memo…
Mz Sql Navigation Use when reading or editing .sql files in an mz-deploy project (a directory with project.toml) —…
Mz Terraform Provider Using the Materialize Terraform provider to manage Materialize resources declaratively.
Mz Terraform Self Managed Terraform modules for deploying self-managed Materialize on AWS (EKS), Azure (AKS), and GCP (GKE).