Dbt Project Initializer

The script will prompt for:

• Project name: Used for folder name and dbt project (e.g., "Sales Analytics")
• Database name: SQL Server database name (required — no default)
• Database schema: Default schema for raw data (default: "raw")
• Description: Brief project description for documentation

Non-Interactive Mode

Provide all parameters via command line:

python "${CLAUDE_PLUGIN_ROOT}/skills/dbt-project-initializer/scripts/initialize_project.py" \
  --target "C:\path\to\new\project" \
  --name "sales_analytics" \
  --database "SalesDB" \
  --schema "raw" \
  --description "Sales performance analytics pipeline"

Parameters

Schema Convention

The project uses two schema parameters:

1. --schema (default: raw): Where sql-executor loads source CSV data

   • Source tables created in raw schema (e.g., raw.carbon_intensity)

2. --dbt-schema (default: dbo): dbt's default schema prefix

   • dbt concatenates this with model schemas to create final schema names:
   • Staging models → dbo_staging
   • Intermediate models → dbo_intermediate
   • Marts (facts/dims) → dbo_analytics

Final Schema Structure:

raw                    # Source data (sql-executor)
dbo_staging            # Staging views (stg_*)
dbo_intermediate       # Intermediate models (int_*)
dbo_analytics          # Marts - facts and dimensions (fct_*, dim_*)

What Gets Created

ProjectName/
├── 0 - Architecture Setup/
│   ├── setup_environment.ps1    # Python environment setup
│   ├── project-config.yml       # Project configuration
│   └── README.md                # Setup documentation
├── 1 - Documentation/           # Project docs (empty)
├── 2 - Source Files/            # CSV source data (empty)
├── 3 - Data Pipeline/
│   ├── dbt_project.yml          # dbt project config
│   ├── packages.yml             # dbt packages (dbt_utils)
│   ├── profiles.yml.example     # Profile template
│   ├── models/
│   │   ├── staging/            # stg_* models
│   │   ├── intermediate/       # int_* models
│   │   └── marts/              # dim_* and fct_* models
│   ├── tests/                   # Custom tests
│   ├── macros/                  # Custom macros
│   ├── seeds/                   # Seed data
│   └── snapshots/               # SCD snapshots
├── 4 - Semantic Layer/          # Power BI TMDL (empty)
├── 5 - Report Building/         # Power BI reports (empty)
├── 6 - Agentic Resources/
│   └── reference/               # Copied from skill templates
│       ├── sql-style-guide.md
│       ├── testing-patterns.md
│       ├── tmdl-best-practices-guide.md
│       └── examples/
├── 7 - Data Exports/            # Query results (empty)
├── .venv/                       # Python virtual environment
├── .gitignore                   # Git ignore file
└── CLAUDE.md                    # Project-specific agent config

Virtual Environment Setup

The skill automatically creates a Python 3.12 virtual environment and installs:

dbt Dependencies:

• dbt-core
• dbt-sqlserver
• dbt-fabric

Data Processing:

• pandas
• sqlalchemy
• pyodbc
• python-dotenv

Post-Initialization Steps

After initialization, complete these steps:

1. Configure dbt profile:

   cd "3 - Data Pipeline"
   cp profiles.yml.example profiles.yml
   # Edit profiles.yml with your connection details
   dbt debug
   

2. Install dbt packages:

   dbt deps
   

3. Load source data:

   • Place CSV files in "2 - Source Files/"
   • Use /sql-executor skill to load into database

4. Start development:

   • Use dbt-staging-builder agent for first models
   • Use data-profiler skill to understand source data

Integration with Agents

The generated CLAUDE.md configures the project for these agents:

Customization

Modifying Templates

Template files are located in the skill's templates/ directory:

• CLAUDE.md.template - Project CLAUDE.md template
• dbt_project.yml.template - dbt project template
• setup_environment.ps1 - Environment setup script
• gitignore.template - .gitignore template

Edit these to customize default configurations.

Adding Reference Materials

The skill copies reference materials from Agents/reference/ in this repository. To add new reference materials:

1. Add files to Agents/reference/
2. They will be copied automatically during initialization

Troubleshooting

Python 3.12 Not Found

# Install Python 3.12
winget install Python.Python.3.12

# Verify installation
py -3.12 --version

Virtual Environment Issues

# Force recreate
.\0 - Architecture Setup\setup_environment.ps1 -Force

# Skip venv, just update packages
.\0 - Architecture Setup\setup_environment.ps1 -SkipVenvCreation

dbt Connection Issues

cd "3 - Data Pipeline"
dbt debug

# Check ODBC driver
python -c "import pyodbc; print(pyodbc.drivers())"

Related Skills

• dbt-runner: Execute dbt commands after project setup
• sql-executor: Load CSV data into SQL Server
• data-profiler: Profile source data before modeling
• tmdl-scaffold: Initialize Power BI semantic models