dbt-forge 0.4.2

Scaffold production-ready dbt projects with opinionated defaults

dbt-forge

dbt-forge is a Python CLI for scaffolding opinionated dbt projects with a consistent layout, starter models, adapter-specific profiles, and optional CI / SQLFluff setup. The project is currently in its 0.4.x alpha phase.

Features

• src/-packaged Python CLI built on Typer and Rich
• Interactive or default-driven project generation
• Adapter templates for BigQuery, Snowflake, PostgreSQL, DuckDB, Databricks, Redshift, Trino, and Spark
• 13 add subcommands for extending an existing dbt project: mart, source, snapshot, seed, exposure, macro, pre-commit, ci, model, test, package, project, source --from-database
• doctor command with 10 project health checks, --fix auto-repair, and --ci mode
• lint command with 6 architectural rules: fan-out, source-to-mart, complexity, duplicate logic, circular deps, YAML-SQL drift
• impact command — downstream impact analysis with blast radius, untested model detection, and PR markdown output
• cost command — query cost estimation from BigQuery, Snowflake, or Databricks usage data with materialization suggestions
• contracts generate command — auto-generate dbt data contracts by introspecting warehouse column types
• changelog generate command — detect breaking and non-breaking model changes between git refs
• status command — Rich terminal dashboard showing model counts by layer, test/doc coverage, sources, and packages
• update command — re-apply dbt-forge templates with unified diffs and per-file accept/skip
• migrate command — convert legacy SQL scripts into a dbt project with ref() and source()
• docs generate command — AI-assisted documentation via Claude, OpenAI, or Ollama
• Custom presets via --preset flag — YAML files with defaults and locked fields for team standardization
• add model auto-detects existing sources from YAML files for staging layer selection
• add test supports schema tests (column-level in .yml) in addition to data and unit tests
• add package generates package-specific vars in dbt_project.yml (e.g., elementary, dbt-project-evaluator)
• Optional pre-commit hooks, SQLFluff config, .editorconfig, and CI templates
• Environment config scaffolding: generate_schema_name macro and adapter-specific .env.example
• CODEOWNERS file generation with mart-based ownership mapping
• Curated package registry with 20 dbt packages and known-good version ranges
• Generated .env support so local dbt commands resolve profiles/ consistently

Install

pip install dbt-forge

Or with uv:

uv tool install dbt-forge

Quickstart

dbt-forge init

For a non-interactive scaffold:

dbt-forge init my_dbt_project --defaults

Add components inside an existing dbt project:

dbt-forge add mart finance
dbt-forge add source salesforce
dbt-forge add snapshot orders
dbt-forge add seed dim_country
dbt-forge add exposure weekly_revenue
dbt-forge add macro cents_to_dollars
dbt-forge add pre-commit
dbt-forge add ci github
dbt-forge add model users
dbt-forge add test stg_orders
dbt-forge add package dbt-utils

Run health checks on an existing project:

dbt-forge doctor
dbt-forge doctor --fix
dbt-forge doctor --ci
dbt-forge doctor --check naming-conventions

Lint project structure and analyze impact:

dbt-forge lint
dbt-forge lint --rule fan-out --ci
dbt-forge impact stg_orders
dbt-forge impact --diff --pr

Estimate query costs and generate contracts:

dbt-forge cost --days 7 --top 20
dbt-forge contracts generate orders
dbt-forge contracts generate --all-public --dry-run

Track model changes:

dbt-forge changelog generate
dbt-forge changelog generate --from v1.0 --format json

View project stats and update templates:

dbt-forge status
dbt-forge update --dry-run
dbt-forge update

Manage presets:

dbt-forge preset validate company-standard.yml
dbt-forge init my_project --preset company-standard.yml

Supported Python

• Python 3.11
• Python 3.12
• Python 3.13

Local Development

cd cli
uv sync --all-groups
uv run pre-commit install --hook-type commit-msg
uv run ruff check .
uv run pytest -m "not integration"   # unit tests only (~483 tests)
uv run pytest -m integration -v      # integration tests with DuckDB (~32 tests)
uv run pytest                        # all tests
uv build
uvx twine check dist/*

Commit Messages

This repo uses Conventional Commits. Every commit message must start with a type such as:

• feat: add databricks profile scaffolding
• fix: handle missing target directory
• chore: refresh release workflow

The local commit-msg hook enforces this after you run:

uv run pre-commit install --hook-type commit-msg

Release Process

The package publishes from GitHub Actions on v* tags. The website in this repo is supporting docs/marketing and is not a separately versioned release artifact.

From the repository root:

python3 scripts/release_assistant.py prepare 0.4.2
python3 scripts/release_assistant.py verify 0.4.2
python3 scripts/release_assistant.py publish 0.4.2 --confirm

prepare stages the version and changelog updates. verify is the release gate and requires a clean main branch aligned with origin/main. publish dispatches the TestPyPI preflight, waits for manual confirmation, then tags and creates the GitHub Release.

For the detailed checklist and publish prerequisites, see RELEASING.md.

Publishing Setup

• Public repo: maroil/dbt-forge
• Configure GitHub environments named pypi and testpypi
• Register the GitHub repo as a Trusted Publisher in PyPI and TestPyPI

License

MIT. See LICENSE.