Dbt Project Analyzer
by a5c-ai
Analyzes dbt projects for best practices, performance, maintainability, and generates actionable recommendations for improvement.
Skill Details
Repository Files
2 files in this skill directory
name: dbt-project-analyzer description: Analyzes dbt projects for best practices, performance, maintainability, and generates actionable recommendations for improvement. version: 1.0.0 category: Transformation skill-id: SK-DEA-003 allowed-tools: Read, Grep, Glob, Bash, WebFetch
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
Applicable Processes
- dbt Project Setup (
dbt-project-setup.js) - dbt Model Development (
dbt-model-development.js) - Metrics Layer (
metrics-layer.js) - Incremental Model Setup (
incremental-model.js)
References
Version History
- 1.0.0 - Initial release with core analysis capabilities
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