dbt-model-index

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Provide a lookup index of dbt models (BigQuery tables) to guide query writing against a data warehouse. Use when you need to query, analyze, or look up data in a dbt-powered data warehouse, or when resolving a vague data question into the right BigQuery tables to query.

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npx skill4agent add warpdotdev/oz-skills dbt-model-index

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Translated version includes tags in frontmatter
dbt-modelsbigquerydata-warehousequery-guidancedata-indexing

SKILL.md Content

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dbt Model Index

When to Use

  • Before writing any BigQuery SQL against production data
  • When the task has not already explicitly stated which models/tables to query
  • When resolving a vague or ambiguous data question into the right BigQuery tables

How to Set Up This Skill

This skill is a curated index of your dbt models. Each entry describes a model (a BigQuery table), what it contains, and what types of questions it is best suited to answer.
To customize this index for your project:
  • Organize models into logical domain sections (e.g., Users, Activity, Revenue, Events)
  • For each model, include: the table name, a 1–2 sentence description of its grain and content, and "Useful for:" bullets covering common query patterns
  • Note key join keys, standard filters, and partition fields where relevant

[Domain: e.g., Users & Identity]

your_model_name

Brief description of what this model contains. One row per [entity]. Include what makes this model's grain unique and the most important fields.
Useful for:
  • [Type of question this model answers, e.g., user counts, cohort sizes]
  • [Another use case, e.g., filtering to a specific user segment]
  • [Common join pattern, e.g., joining to other tables as the canonical user dimension]

another_model_name

Description of this model and its grain.
Useful for: [Brief use case description]

[Domain: e.g., Activity & Engagement]

your_activity_model

Description of the activity signal (e.g., what counts as "active"), the grain, and the time dimension.
Useful for:
  • [Use case 1, e.g., daily/weekly active user metrics]
  • [Use case 2, e.g., retention analysis]

your_engagement_model

Description.
Useful for:
  • [Use case 1]
  • [Use case 2]

[Domain: e.g., Revenue & Subscriptions]

your_revenue_model

Description of the revenue grain (e.g., one row per customer per day, or one row per subscription event).
Useful for:
  • [Use case 1, e.g., MRR/ARR reporting]
  • [Use case 2, e.g., churn analysis]

your_subscription_model

Description.
Useful for:
  • [Use case 1]
  • [Use case 2]

[Domain: e.g., Events & Telemetry]

your_events_model

Description of the event source, enrichment applied, and key fields available.
Useful for:
  • [Use case 1, e.g., raw event-level analysis]
  • [Use case 2, e.g., building domain-specific funnels]

Important Notes

  • Standard filters: Document any filters that should always be applied in user-facing queries (e.g., excluding test accounts, soft-deleted records, internal users, or flagged/fraudulent users). Example: 
    where not is_internal_user
  • Production data: Specify your default project/dataset path. Example: 
    your-gcp-project.prod.<model_name>
  • Cost control: For large partitioned tables, always filter on the partition field and constrain the date range to avoid full-table scans
  • Model grain: Always note the grain (one row per what?) for each model to avoid accidental fan-outs in joins
  • Plan/tier types: If your product has subscription tiers or plan types, document the valid values here so queries filter correctly
  • Sensitive datasets: If any models live in a separate dataset, call that out explicitly so queries use the right fully-qualified table reference