Model Data in MotherDuck

SkillDatabases & data

Design and build database schemas and data models in MotherDuck. Produces a file-based SQL project scaffold with a model manifest. Use for any schema design or data modeling task — creating tables, choosing data types, star schemas, wide denormalized tables, raw/staging/analytics layers, dbt-style t

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Then ask your AI: use the Model Data in MotherDuck skill

What this skill tells your AI

The instructions your AI receives, as published by motherduckdb/agent-skills in skills/motherduck-model-data/SKILL.md and read by ahel’s review.

Core Behavior

For multi-model work, keep transformations in reviewable SQL files using the project's existing dbt, SQLMesh, or local conventions. If none exist, use stage directories and a model_manifest.yml recording dependencies, materialization, and target database. A single-table request needs only the requested SQL or change.

Prerequisites

Use the known source schema and connection. Discover missing types, grain, and join keys before implementing; planning can use supplied schema without a live connection.

Default Posture

  • Design for analytical reads, not transactional writes.
  • Prefer wide denormalized tables and pre-aggregated serving tables over highly normalized OLTP-style schemas.
  • Use fully qualified names and add comments to tables and columns. Preserve stable object names so Guides can reference the intended catalog objects reliably.
  • Use NOT NULL aggressively; do not assume primary keys or foreign keys are enforced.
  • Reuse an existing dbt, SQLMesh, or repo-local modeling convention when one is already present; create the lightweight scaffold only when there is no established project shape.
  • Separate raw, staging, and analytics lifecycle stages when the project is non-trivial.

Workflow

  1. Inspect the current source tables and actual column types before designing new models.
  2. Choose the target lifecycle stage and grain for each modeled table. Map dependencies between models.
  3. Place SQL in the existing project, or use the scaffold reference for a new multi-model project.
  4. Author each model as a standalone SQL file. Use explicit types, nullability, comments, and fully qualified names. Decide between a table, CTAS rebuild, or view based on freshness and cost.
  5. Record dependencies and materializations in the project's framework or lightweight manifest, not both.
  6. For implementation, run the in-scope models and verify grain and row counts; MCP DDL and CTAS require query_rw. For an answer, review, or plan, return the requested explanation or SQL without creating a project or mutating the warehouse unless requested.

References

Read only the reference sections needed for the current task.

  • Read references/MODELING_PLAYBOOK.md for schema patterns, data-type guidance, CTAS/view decisions, complex types, constraints, project scaffold conventions, and common modeling mistakes.

Related Skills

Load related skills only for missing capabilities; reuse established context.

  • motherduck-duckdb-sql for type syntax and function details
  • motherduck-query for executing DDL, rebuilds, and validation queries
  • motherduck-explore for understanding the source schema before remodeling
  • motherduck-load-data for ingestion paths that feed the modeled tables
  • motherduck-manage-guides for durable business definitions and join rules that do not belong in transformation code

Signals

GitHub stars
58
Forks
4
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
motherduck-model-data
Source
github.com/motherduckdb/agent-skills