--- name: dbt-project-analyzer-d51c3088a7cb 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 graph: domains: [domain:data-engineering] specializations: [specialization:data-engineering-analytics] skillAreas: [skill-area:dbt-modeling, skill-area:data-warehouse-modeling] roles: [role:analytics-engineer, role:data-engineer] workflows: [workflow:data-pipeline-deployment] --- # 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 ```json { "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 ```json { "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 ```bash # Invoke skill for standard analysis /skill dbt-project-analyzer --projectPath ./my-dbt-project ``` ### Deep Analysis with Focus Areas ```json { "projectPath": "./analytics", "analysisDepth": "deep", "focusAreas": ["performance", "testing", "incremental"] } ``` ### CI Integration Analysis ```json { "projectPath": "./dbt_project", "manifestJson": "./target/manifest.json", "runResults": "./target/run_results.json", "focusAreas": ["performance"] } ``` ## Analysis Rules ### Naming Conventions | Layer | Pattern | Example | |-------|---------|---------| | Staging | `stg___` | `stg_stripe__payments` | | Intermediate | `int__` | `int_payments_pivoted` | | Marts | `fct_` or `dim_` | `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 - [dbt Best Practices](https://docs.getdbt.com/best-practices) - [dbt Style Guide](https://github.com/dbt-labs/corp/blob/main/dbt_style_guide.md) - [dbt Project Maturity](https://docs.getdbt.com/guides/best-practices/how-we-structure/1-guide-overview) - [dbt MCP Server](https://github.com/dbt-labs/dbt-mcp) ## Version History - **1.0.0** - Initial release with core analysis capabilities --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Development & Engineering, Operations, Product Management Roles: Business Process Analyst, Data Analyst, Data Scientist, Growth Product Manager, ML / AI Engineer Specialties: API, Database Tools: Dbt Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 48fcdc10ce20005369dd58fbfbd1bf52b5f2c2a08f7b13d664737f03119020e3