dbt-project-analyzer

Testing & Quality

Analyzes dbt projects for best practices, performance, maintainability, and generates actionable recommendations for improvement.

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Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/data-engineering-analytics/skills/dbt-project-analyzer/SKILL.md

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dbt Project Analyzer

Analyzes dbt projects for best practices, performance, and maintainability following dbt Labs recommended patterns.

Overview

This skill examines dbt project structure, model dependencies, test coverage, documentation completeness, and adherence to naming conventions. It provides actionable recommendations for improving project health and maintainability.

Capabilities

Model dependency graph analysis - Visualize and analyze model relationships, detect circular dependencies
Incremental model optimization - Evaluate incremental strategies and suggest improvements
Materialization strategy recommendations - Recommend optimal materializations based on usage patterns
Test coverage analysis - Measure and report on test coverage across models
Documentation completeness check - Identify undocumented models, columns, and sources
Naming convention validation - Enforce consistent naming patterns (staging, marts, intermediate)
Ref/source usage validation - Detect hardcoded references and missing source definitions
Macro efficiency analysis - Evaluate macro usage and suggest optimizations
Slim CI optimization - Configure efficient CI builds with state comparison
Model contract validation - Verify model contracts for type safety

Input Schema

{
  "projectPath": {
    "type": "string",
    "description": "Path to the dbt project root directory",
    "required": true
  },
  "manifestJson": {
    "type": "object",
    "description": "Parsed manifest.json from target/ directory (optional, will be loaded if not provided)"
  },
  "catalogJson": {
    "type": "object",
    "description": "Parsed catalog.json from target/ directory (optional)"
  },
  "runResults": {
    "type": "object",
    "description": "Parsed run_results.json for performance analysis (optional)"
  },
  "analysisDepth": {
    "type": "string",
    "enum": ["quick", "standard", "deep"],
    "default": "standard",
    "description": "Depth of analysis to perform"
  },
  "focusAreas": {
    "type": "array",
    "items": {
      "type": "string",
      "enum": ["performance", "testing", "documentation", "naming", "incremental", "dependencies"]
    },
    "description": "Specific areas to focus analysis on (all if not specified)"
  }
}

Output Schema

{
  "healthScore": {
    "type": "number",
    "description": "Overall project health score (0-100)"
  },
  "issues": {
    "type": "array",
    "items": {
      "severity": "error|warning|info",
      "category": "string",
      "model": "string",
      "message": "string",
      "recommendation": "string",
      "line": "number"
    }
  },
  "metrics": {
    "testCoverage": {
      "type": "number",
      "description": "Percentage of models with tests"
    },
    "docCoverage": {
      "type": "number",
      "description": "Percentage of models/columns documented"
    },
    "incrementalRatio": {
      "type": "number",
      "description": "Percentage of eligible models using incremental"
    },
    "avgModelDepth": {
      "type": "number",
      "description": "Average depth in DAG"
    },
    "totalModels": {
      "type": "number"
    },
    "totalTests": {
      "type": "number"
    }
  },
  "recommendations": {
    "type": "array",
    "items": {
      "priority": "high|medium|low",
      "category": "string",
      "description": "string",
      "effort": "string",
      "impact": "string"
    }
  },
  "dependencyGraph": {
    "type": "object",
    "description": "Simplified dependency graph for visualization"
  }
}

Usage Examples

Basic Project Analysis

# Invoke skill for standard analysis
/skill dbt-project-analyzer --projectPath ./my-dbt-project

Deep Analysis with Focus Areas

{
  "projectPath": "./analytics",
  "analysisDepth": "deep",
  "focusAreas": ["performance", "testing", "incremental"]
}

CI Integration Analysis

{
  "projectPath": "./dbt_project",
  "manifestJson": "./target/manifest.json",
  "runResults": "./target/run_results.json",
  "focusAreas": ["performance"]
}

Analysis Rules

Naming Conventions

Layer	Pattern	Example
Staging	`stg_<source>__<entity>`	`stg_stripe__payments`
Intermediate	`int_<entity>_<verb>`	`int_payments_pivoted`
Marts	`fct_<entity>` or `dim_<entity>`	`fct_orders`, `dim_customers`

Test Coverage Requirements

Severity	Condition
Error	No unique/not_null test on primary key
Warning	< 50% columns have tests
Info	Missing relationship tests

Materialization Guidelines

Model Type	Recommended	Reason
Staging	View or Ephemeral	Source transformations, low compute
Intermediate	Ephemeral	Reduce warehouse clutter
Marts	Table or Incremental	End-user queries, performance
Large tables (>1M rows)	Incremental	Reduce build time

Integration Points

MCP Server Integration

This skill integrates with the official dbt MCP server for enhanced capabilities:

dbt-labs/dbt-mcp - Project metadata discovery, model information, semantic layer querying
dbt Remote MCP Server - Cloud-hosted dbt MCP with secure endpoint access