data-modeling plugins

by Honeydew AI

Skills and tools powered by the Honeydew MCP that help coding agents query data and build semantic models

by Matt Corbett

Analytics-engineering team — agents (analytics-engineer, semantic-layer-engineer, data-quality-testing-engineer) for the TRANSFORMATION layer in the modern data stack: dbt modeling (staging -> intermediate -> marts, the medallion/Kimball split, incremental models, materializations), a governed semantic/metrics layer (one definition of revenue/active-user, metrics-as-code) so every tool agrees, and data quality (dbt tests, freshness, model contracts, anomaly checks) that gates the warehouse. Warehouse-neutral (Snowflake/BigQuery/Redshift/Databricks). skills, a decision-tree knowledge bank (materialization + model-layer trees + a dated 2026 map), best-practices, templates, commands, an advisory hook. Distinct from data-platform (ingestion/warehouse/BI) and database-engineering (OLTP). Seams: ELT/warehouse provisioning -> data-platform, OLTP -> database-engineering, BI -> tableau/data-platform, enterprise lakehouse -> microsoft-fabric. Requires ravenclaude-core@>=0.7.0.

by Matt Corbett

Tableau analytics team — specialist agents for Tableau Desktop/Cloud/Server, Tableau Prep, and the developer surface: VizQL and chart-type selection, calculations (LOD expressions, table calcs, FIXED/INCLUDE/EXCLUDE), data modeling (relationships vs joins vs blends, extracts vs live), workbook/extract performance, Server/Cloud governance and permissions, content/ALM promotion, embedding (Embedding API v3, Connected Apps/JWT), and the next-gen surface (Tableau Pulse, Tableau Next, CRM Analytics, Einstein/Agent). Citation-grounded knowledge bank with Mermaid decision trees + a best-practices library. Seams: Salesforce platform data → salesforce; warehouse/semantic modeling → data-platform/microsoft-fabric; Power BI comparison → power-platform; methodology/security-review → ravenclaude-core. Requires ravenclaude-core@>=0.7.0.

by Mark Gillett

A domain-agnostic methodology for building, extending, reviewing, and refactoring ontologies, domain models, taxonomies, and knowledge-graph schemas. Synthesises Stanford Ontology 101, OntoClean/UFO, Gruber's design criteria, Domain-Driven Design, OBO Foundry governance, and agent-facing platform practice into one process with explicit conflict resolution.