dbt-core

name	dbt-core
description	Managing dbt-core locally - installation, configuration, project setup, package management, troubleshooting, and development workflow. Use this skill for all aspects of local dbt-core development including non-interactive scripts for environment setup with conda or venv, and comprehensive configuration templates for profiles.yml and dbt_project.yml.

dbt-core Local Development Guide

Purpose

Guide AI agents through a systematic, user-choice-driven installation process for dbt-core on local machines. The agent runs diagnostic scripts, presents options to users, and executes non-interactive installation scripts based on user preferences.

When to Use This Skill

Activate this skill when users ask about:

• Installing dbt-core and dbt-snowflake
• Configuring profiles.yml for Snowflake
• Setting up authentication (PAT, SSO, key pair, OAuth)
• Installing and managing dbt packages
• Troubleshooting connection issues
• Initializing new dbt projects
• Verifying installation and configuration
• Upgrading dbt versions

Official dbt Documentation: Install dbt

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AI Agent Workflow

IMPORTANT: This skill uses non-interactive scripts. The AI agent must:

1. Run diagnostic scripts to check the environment
2. Present options to the user based on findings
3. Execute the script chosen by the user
4. Guide the user through next steps

Step 1: Check Environment

AI Agent Action: Run the check script to assess what's already installed:

macOS/Linux:

cd .claude/skills/dbt-core/scripts/
./check-environment.sh

Windows:

cd .claude\skills\dbt-core\scripts\
check-environment.bat

What It Checks:

• conda installation status
• Python installation and version compatibility (3.9-3.12)
• dbt installation status
• curl availability (for downloads)

Output: Structured summary with recommendations for next steps.

Step 2: Present Options to User

Based on the check results, the AI agent should ask the user which installation path they prefer:

If conda is NOT installed:

• Option A: Install Miniforge (recommended, free, conda-forge channel)
• Option B: Install Miniconda (minimal, defaults channel)
• Option C: Skip conda and use Python venv (if Python 3.9-3.12 is installed)

If conda IS installed:

• Option A: Create conda environment for dbt
• Option B: Create venv environment for dbt (if Python is also available)

If dbt is already installed:

• Show version information
• Offer to update or reconfigure

Step 3: Execute User's Choice

AI Agent Action: Run the appropriate script from scripts/ folder (use .sh for macOS/Linux, .bat for Windows):

Option	Script	Action After	User Action
A: Install Miniforge	install-miniforge	Restart terminal, rerun check-environment	None
B: Install Miniconda	install-miniconda	Restart terminal, rerun check-environment	None
C: Setup Conda Env	setup-conda-env	Instruct user to activate	conda activate dbt
D: Setup Venv Env	setup-venv-env	Instruct user to activate	source .venv/bin/activate (macOS/Linux) .venv\Scripts\activate (Windows)

Example execution:

cd .claude/skills/dbt-core/scripts/
./install-miniforge.sh  # or install-miniforge.bat on Windows

Step 4: Next Steps

AI Agent Action: Once dbt is installed and verified, guide user to configure Snowflake connection (see profiles.yml configuration section below).

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Available Scripts

All scripts are in the scripts/ folder and are non-interactive for AI agent execution:

Diagnostic Script

• check-environment.sh/.bat - Comprehensive environment check that:
  • Scans all conda environments for dbt installations
  • Shows dbt-core and dbt-snowflake versions
  • Activates appropriate environment (prefers those with dbt-snowflake)
  • Verifies dbt installation and functionality
  • Tests dbt commands
  • Provides recommendations for next steps

Installation Scripts

• install-miniforge.sh/.bat - Install Miniforge (conda-forge)
• install-miniconda.sh/.bat - Install Miniconda
• setup-conda-env.sh/.bat - Create conda environment from dbt-conda-env.yml
• setup-venv-env.sh/.bat - Create venv environment from requirements.txt

Environment Files (also in scripts/ folder)

• dbt-conda-env.yml - Conda environment specification
• requirements.txt - pip requirements for venv

Sample Configuration Files (in samples/ folder)

• profiles.yml.sample - Snowflake connection configuration examples
• dbt_project.yml.sample - dbt project configuration patterns
• packages.yml.sample - Package dependencies
• gitignore.sample - Version control setup

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Manual Installation

For manual installation instructions, review the automated scripts (see AI Agent Workflow above) or refer to:

• Official dbt Docs: Core Installation
• Environment files in scripts/ folder: dbt-conda-env.yml (conda) or requirements.txt (pip/venv)

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Snowflake Configuration & Authentication

All profiles.yml configuration and authentication methods are documented in: samples/profiles.yml.sample

The sample file includes:

• Complete profiles.yml examples for all authentication methods
• PAT (Programmatic Access Token) generation using Snowflake CLI (recommended)
• SSO authentication with externalbrowser
• Key pair authentication with setup instructions
• OAuth authentication
• Multi-environment configurations (dev, prod)
• Account identifier formats (preferred account name and legacy locator formats)
• Configuration tips for warehouses, threads, and schemas

To configure:

1. Copy samples/profiles.yml.sample to ~/.dbt/profiles.yml
2. Update with your Snowflake account details
3. Choose and configure your authentication method
4. Test with dbt debug

Official dbt Docs: profiles.yml

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Package Installation

Copy samples/packages.yml.sample to your project root as packages.yml, then run dbt deps.

Official dbt Docs: Package Management

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Verify Installation

Run the diagnostic script to verify installation and test connection:

# macOS/Linux
cd scripts/
./check-environment.sh

# Windows
cd scripts\
check-environment.bat

This script checks dbt installation, tests commands, and validates the dbt-snowflake adapter. For manual verification, use dbt debug.

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Troubleshooting

Connection issues: Run dbt debug and check:

• Environment variables set (DBT_ENV_SECRET_SNOWFLAKE_PAT)
• ~/.dbt/profiles.yml exists and configured correctly
• Snowflake connectivity: snow sql -q "SELECT CURRENT_USER()"

Package issues: rm -rf dbt_packages/ && dbt deps --upgrade

Python compatibility: dbt requires Python 3.9-3.12

For detailed troubleshooting: See samples/profiles.yml.sample

Official Docs: Network Issues

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Project Initialization

# Non-interactive (recommended for AI agents)
dbt init my_project_name --skip-profile-setup

# Configure ~/.dbt/profiles.yml separately (see samples/profiles.yml.sample)
# Configure project with dbt_project.yml (see samples/dbt_project.yml.sample)

Project structure: models/, tests/, macros/, seeds/, snapshots/

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dbt_project.yml Configuration

All project configuration patterns are documented in: samples/dbt_project.yml.sample

The sample file includes:

• Basic project setup (name, version, profile connection)
• Project paths (models, tests, macros, seeds, snapshots)
• Global hooks (on-run-start, on-run-end)
• Global variables for project-wide settings
• Model configurations with materialization defaults
• Medallion architecture pattern (bronze/silver/gold layers)
• Basic structure pattern (staging/marts)
• Snapshot configurations for SCD Type 2
• Test configurations with failure storage
• Common patterns (incremental facts, Python models, dynamic tables)

To configure:

1. Copy samples/dbt_project.yml.sample to your project root as dbt_project.yml
2. Update name to match your project name
3. Update profile to match your profiles.yml profile name
4. Choose your architecture pattern (basic or medallion)
5. Customize materializations and schemas
6. Run dbt debug to verify configuration

Official dbt Docs: dbt_project.yml

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Development Workflow

1. Initial Setup

# Install packages
dbt deps

# Verify connection
dbt debug

# Load seed data (if any)
dbt seed

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2. Development Cycle

# Build specific model
dbt build --select model_name

# Build with dependencies
dbt build --select +model_name+

# Build entire project
dbt build

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3. Deploy to Production

# Build against production target
dbt build --target prod

# Test production
dbt test --target prod

# Generate documentation
dbt docs generate --target prod

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Best Practices

• Use virtual environments (conda or venv) for isolation
• Separate dev/prod configs - Use {{ env_var('SCHEMA_NAME', 'DEFAULT_NAME') }} to allow overriding of schema names
• Version control - See samples/gitignore.sample for what to commit/ignore
• Never commit profiles.yml or .env files (contain credentials)

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Upgrade dbt Version

# Upgrade to latest
pip install --upgrade dbt-core dbt-snowflake

# Or specific version (for dbt Projects on Snowflake: 1.9.4 / 1.9.2)
pip install dbt-core==1.9.4 dbt-snowflake==1.9.2

Check Migration Guides for breaking changes and test in dev first.

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Related Official Documentation

• dbt Docs: Installation
• dbt Docs: profiles.yml
• dbt Docs: Environment Variables
• Snowflake Docs: dbt
• Snowflake Docs: PAT

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Goal: Transform AI agents into expert dbt setup specialists who guide users through installation, configuration, authentication, and troubleshooting with clear, actionable instructions and best practices.