warpdotdev/oz-skills

dbt-model-index

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.

First seen May 6, 2026

Installation

$ npx skills add warpdotdev/oz-skills --skill dbt-model-index

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Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 823
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,659 B
  • docs SUMMARY.md 293 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 125 installs

SKILL.md

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]

yourmodelname

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]

anothermodelname

Description of this model and its grain.

Useful for: [Brief use case description]


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

youractivitymodel

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]

yourengagementmodel

Description.

Useful for:

  • [Use case 1]
  • [Use case 2]

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

yourrevenuemodel

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]

yoursubscriptionmodel

Description.

Useful for:

  • [Use case 1]
  • [Use case 2]

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

youreventsmodel

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 isinternaluser
  • 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