rill-model

Define and deploy Rill data models with SQL, partitioning, and output configurations.

Updated Feb 19, 2026

Search Tags:#sql #etl #data-modeling #incremental #rill #partitions

Ready to run

npx skills add https://github.com/ContrastingSounds/myai --skill rill-model-contrastingsounds

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rill-model
Source: https://github.com/ContrastingSounds/myai/tree/main/rill/.claude/skills/rill-model
Command: npx skills add https://github.com/ContrastingSounds/myai --skill rill-model-contrastingsounds

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides comprehensive guidance for creating, organizing, and operating data models in Rill, enabling structured ETL pipelines and reproducible transformations.

Core Features & Use Cases

Defines how to model ETL logic, outputs derived data to OLAP connectors, and references other models for complex workflows.
Covers modeling categories like Source, Derived, Incremental, and Partitioned models, with best practices for development and deployment.
Use case: Implement a Rill model to transform raw events into a cleaned, partitioned table ready for analytics dashboards.

Quick Start

Create a new Rill model that reads from a source connector and writes to the default OLAP connector.

Frequently Asked Questions about rill-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build incremental ETL data models with SQL partitioning?▼

To build incremental ETL data models with SQL partitioning, define the required type, sql, and materialize properties, then configure optional partitions and incremental sections for robust production-grade pipelines.

What are Rill data models used for in ETL and transformation tasks?▼

Rill data models are used for ETL and transformation tasks to define SQL logic that outputs derived tabular data to OLAP connectors, supporting structured pipelines and reproducible analytics workflows.

When should I use partitioned or incremental models for data transformations?▼

Use partitioned or incremental models for data transformations when building near-root DAG models that reference other models and connectors, requiring specific refresh configurations to support robust production-grade pipelines.

What's the best way to structure near-root DAG models that reference other models?▼

The best way to structure near-root DAG models is to define type: model with required sql and materialize properties, referencing source connectors and other models to produce tabular data for analytics dashboards.

Do I need to specify materialize properties when deploying Rill data models?▼

Yes, you need to specify materialize as a required property when deploying Rill data models, alongside type: model and sql, to properly output derived tabular data to OLAP connectors.