Model Data in MotherDuck
SkillDatabases & dataDesign 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 NULLaggressively; 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, andanalyticslifecycle stages when the project is non-trivial.
Workflow
- Inspect the current source tables and actual column types before designing new models.
- Choose the target lifecycle stage and grain for each modeled table. Map dependencies between models.
- Place SQL in the existing project, or use the scaffold reference for a new multi-model project.
- 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.
- Record dependencies and materializations in the project's framework or lightweight manifest, not both.
- 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.mdfor 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-sqlfor type syntax and function detailsmotherduck-queryfor executing DDL, rebuilds, and validation queriesmotherduck-explorefor understanding the source schema before remodelingmotherduck-load-datafor ingestion paths that feed the modeled tablesmotherduck-manage-guidesfor 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