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motherduck-model-data

面向 MotherDuck 的分析建模 Skill:先探查源表结构与实际列类型,按生命周期阶段和粒度设计 raw/staging/analytics 模型,产出带显式类型、非空约束、注释和全限定名的独立 SQL 文件,并在既有 dbt/SQLMesh 或轻量清单中记录依赖与物化方式;也可只返回计划或解释而不改动仓库。

motherduckdb
热度 0.00
最近更新 2026-10-19 00:08:43
分类:数据依赖:MCP 服务 / 读写文件应用场景:数据处理 / 工作流 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add motherduckdb/agent-skills/motherduck-model-data
Metadata
name
motherduck-model-data
description
Design or implement MotherDuck analytical schemas and transformation models, including grain, types, and materialization.
license
MIT

Model Data in MotherDuck

Core Behavior

For multi-model work, keep transformations in reviewable SQL files using the project's existing dbt, SQLMesh, or local conventions. If none exist, use stage directories and a model_manifest.yml recording dependencies, materialization, and target database. A single-table request needs only the requested SQL or change.

Prerequisites

Use the known source schema and connection. Discover missing types, grain, and join keys before implementing; planning can use supplied schema without a live connection.

Default Posture

  • Design for analytical reads, not transactional writes.
  • Prefer wide denormalized tables and pre-aggregated serving tables over highly normalized OLTP-style schemas.
  • Use fully qualified names and add comments to tables and columns. Preserve stable object names so Guides can reference the intended catalog objects reliably.
  • Use NOT NULL aggressively; do not assume primary keys or foreign keys are enforced.
  • Reuse an existing dbt, SQLMesh, or repo-local modeling convention when one is already present; create the lightweight scaffold only when there is no established project shape.
  • Separate raw, staging, and analytics lifecycle stages when the project is non-trivial.

Workflow

  1. Inspect the current source tables and actual column types before designing new models.
  2. Choose the target lifecycle stage and grain for each modeled table. Map dependencies between models.
  3. Place SQL in the existing project, or use the scaffold reference for a new multi-model project.
  4. Author each model as a standalone SQL file. Use explicit types, nullability, comments, and fully qualified names. Decide between a table, CTAS rebuild, or view based on freshness and cost.
  5. Record dependencies and materializations in the project's framework or lightweight manifest, not both.
  6. For implementation, run the in-scope models and verify grain and row counts; MCP DDL and CTAS require query_rw. For an answer, review, or plan, return the requested explanation or SQL without creating a project or mutating the warehouse unless requested.

References

Read only the reference sections needed for the current task.

  • Read references/MODELING_PLAYBOOK.md for schema patterns, data-type guidance, CTAS/view decisions, complex types, constraints, project scaffold conventions, and common modeling mistakes.

Related Skills

Load related skills only for missing capabilities; reuse established context.

  • motherduck-duckdb-sql for type syntax and function details
  • motherduck-query for executing DDL, rebuilds, and validation queries
  • motherduck-explore for understanding the source schema before remodeling
  • motherduck-load-data for ingestion paths that feed the modeled tables
  • motherduck-manage-guides for durable business definitions and join rules that do not belong in transformation code