Dbt Model Planner — Data skill for Claude Code
Claude Code skill: Plan and build dbt models from schema requirements through structured planning with human review, schema validation, and multi-dialect SQL generation.
How to install Dbt Model Planner
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open wan-huiyan/dbt-model-planner and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Dbt Model Planner does
Claude Code skill: Plan and build dbt models from schema requirements through structured planning with human review, schema validation, and multi-dialect SQL generation.
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README
dbt Model Planner
[](https://github.com/wan-huiyan/dbt-model-planner/releases) [](https://claude.com/claude-code) [](LICENSE) [](https://github.com/wan-huiyan/dbt-model-planner/commits)
Jumping straight from a dashboard brief to dbt SQL skips the hard part: mapping every target field to a source table, validating it exists in production, and agreeing on the logic before writing a line of code. This skill guides you through that planning process with human review checkpoints before anything gets built.
What It Does
Takes a target schema (CSV spec, dashboard brief, or requirements doc) and guides you through 7 phases to build a validated dbt model:
- Understand Requirements — parse the spec, determine model grain
- Context Gathering — auto-read project config, mine conventions, explore source models, check with stakeholders
- Planning Document — structured CSV/Excel mapping every target field to source tables with transformation logic
- Human Review Loop — iterate with the team until all fields are confirmed
- Schema Validation — DESCRIBE production tables (3-part check: existence + population + freshness), write investigation queries
- Build the Model — CTE-structured SQL with recommended conventions and a dialect reference table
- Verify and QA — standalone verification query + self-contained QA queries
Key Features
- Complexity gate — lightweight path for simple models (<8 fields), full 7-phase path for complex ones
- Multi-dialect support — dialect reference table for Spark/Databricks, Snowflake, BigQuery, and Postgres/Redshift
- Human review checkpoints — blank "Review Feedback" column in
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