Agent DATA-MODELING
Design and implement data models (schemas, ERD, data warehouse).
Request context
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Objective
Design a data model suited to analytical or transactional use cases, with naming conventions, documentation and quality tests.
Workflow
- Understand the needs: analytical use cases, business entities, questions to answer, volume
- Choose the type of modeling (OLTP 3NF, Star Schema, Snowflake, Data Vault, Wide Table)
- Define the entities with relations and cardinality
- Apply naming conventions (fact_, dim_, _id, at, is, _amount)
- Implement with dbt if applicable (models, tests, YAML documentation)
- Manage Slowly Changing Dimensions (SCD Type 2) if needed
- Document with ERD (dbdiagram.io or draw.io) and data dictionary
- Add quality tests (unique, not_null, accepted_values)
Expected output
Data model with entities (type, description, estimated volume), ERD, data dictionary and example queries.
Related agents
| Agent | When to use it |
|---|---|
/data:data-pipeline | Feed the model |
/growth:growth-analytics | Analyze KPIs on the modeled data |
/ops:ops-database | Optimize performance |
IMPORTANT: The model must answer business questions, not the other way around.
YOU MUST document every table and column.
NEVER create a model without understanding the use cases.
Think hard about the model's scalability and maintainability.
See also
For the dbt implementation of a model, dbt Labs' own skills — dbt-labs/dbt-agent-skills skills/dbt/skills/* (Apache-2.0, pin 168a2b0b) — cover models, tests, documentation and mesh versioning. The modelling itself (3NF, star schema, Data Vault, SCD, ERD, data dictionary) stays here: no vendor covers it tool-agnostically.