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dbt-model-index

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dbt Model Index

dbt模型索引

When to Use

适用场景

  • 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
  • 在针对生产数据编写任何BigQuery SQL之前
  • 当任务未明确指定要查询的模型/表时
  • 当需要将模糊或歧义的数据问题转化为正确的BigQuery表时

How to Set Up This Skill

如何设置该技能

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
本技能是你的dbt模型的精选索引。每个条目描述一个模型（即BigQuery表），包含其内容以及最适合解答的问题类型。
要为你的项目自定义此索引：
  • 将模型组织到逻辑领域板块中（例如：用户、活动、收入、事件）
  • 每个模型需包含：表名、1–2句话描述其粒度和内容，以及“适用场景：”项目列表，涵盖常见查询模式
  • 相关的关键连接键、标准过滤器和分区字段也要注明

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

[领域：例如，用户与身份]

your_model_name

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]
该模型内容的简要描述。每行对应一个[实体]。需说明该模型粒度的独特之处以及最重要的字段。
适用场景：
  • [该模型可解答的问题类型，例如：用户数量、群组规模]
  • [另一个使用场景，例如：筛选特定用户群体]
  • [常见连接模式，例如：作为标准用户维度与其他表连接]

another_model_name

another_model_name

Description of this model and its grain.
Useful for: [Brief use case description]
该模型及其粒度的描述。
适用场景： [简要描述使用场景]

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

[领域：例如，活动与参与度]

your_activity_model

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]
活动信号的描述（例如：什么算作“活跃”）、粒度以及时间维度。
适用场景：
  • [使用场景1，例如：每日/每周活跃用户指标]
  • [使用场景2，例如：留存分析]

your_engagement_model

your_engagement_model

Description.
Useful for:
  • [Use case 1]
  • [Use case 2]
描述。
适用场景：
  • [使用场景1]
  • [使用场景2]

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

[领域：例如，收入与订阅]

your_revenue_model

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]
收入粒度的描述（例如：每行对应每个客户每天，或每行对应一个订阅事件）。
适用场景：
  • [使用场景1，例如：MRR/ARR报告]
  • [使用场景2，例如：流失分析]

your_subscription_model

your_subscription_model

Description.
Useful for:
  • [Use case 1]
  • [Use case 2]
描述。
适用场景：
  • [使用场景1]
  • [使用场景2]

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

[领域：例如，事件与遥测]

your_events_model

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]
事件来源、所应用的增强处理以及可用的关键字段的描述。
适用场景：
  • [使用场景1，例如：原始事件级分析]
  • [使用场景2，例如：构建特定领域的转化漏斗]

Important Notes

重要说明

  • 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
  • 标准过滤器： 记录所有在面向用户的查询中应始终应用的过滤器（例如：排除测试账户、软删除记录、内部用户或标记为欺诈的用户）。示例：
    where not is_internal_user
  • 生产数据： 指定默认的项目/数据集路径。示例：
    your-gcp-project.prod.<model_name>
  • 成本控制： 对于大型分区表，始终按分区字段过滤并限制日期范围，以避免全表扫描
  • 模型粒度： 始终注明每个模型的粒度（每行对应什么？），以避免连接时意外出现数据膨胀
  • 计划/层级类型： 如果你的产品有订阅层级或计划类型，请在此记录有效值，以便查询能正确过滤
  • 敏感数据集： 如果任何模型位于单独的数据集中，请明确指出，以便查询使用正确的全限定表引用