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Skill 详情

dbt-transformation-patterns

Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.

来源平台:GitHub
来源标识:sickn33/antigravity-awesome-skills
源文件:原始说明
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快速判断

Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.

最后校验2026-05-27
来源平台GitHub
安全提示
下载副本ZIP 可用

适合任务

  • 把重复任务整理成可复用的 AI 操作流程。
  • 让 AI 在特定场景下按统一规范执行。
  • 为团队或个人工作流提供可复制的任务说明。

输入与输出

输入:任务目标、上下文材料、文件路径、约束条件或需要处理的内容。

输出:按 Skill 说明生成的文档、代码、检查结果、计划、建议或操作步骤。

示例任务

  • 使用 dbt-transformation-patterns 帮我处理当前任务,并说明执行前需要确认的输入。
  • 根据 dbt-transformation-patterns 的说明,给我一个安全的使用步骤清单。

安装方式

  1. 下载本站提供的 Skill ZIP 并解压。
  2. 把解压后的 Skill 目录放入当前 AI 工具支持的 skills 目录。
  3. 如需在线查看原始内容,可打开 GitHub 的 SKILL.md

在线原始地址:dbt-transformation-patterns/SKILL.md

风险边界

使用前请检查权限、外部依赖和要处理的数据类型。不要把密码、密钥、身份信息或敏感客户资料交给未经确认的 Skill。

SKILL.md 文档介绍

dbt Transformation Patterns

Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.

Use this skill when

  • Building data transformation pipelines with dbt
  • Organizing models into staging, intermediate, and marts layers
  • Implementing data quality tests and documentation
  • Creating incremental models for large datasets
  • Setting up dbt project structure and conventions

Do not use this skill when

  • The project is not using dbt or a warehouse-backed workflow
  • You only need ad-hoc SQL queries
  • There is no access to source data or schemas

Instructions

  • Define model layers, naming, and ownership.
  • Implement tests, documentation, and freshness checks.
  • Choose materializations and incremental strategies.
  • Optimize runs with selectors and CI workflows.
  • If detailed patterns are required, open resources/implementation-playbook.md.

Resources

  • resources/implementation-playbook.md for detailed dbt patterns and examples.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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