DBT Building Tools - Genesis Computing

Overview

Tool Name

dbt_building_tools

Purpose

DBT Building Tools accelerate dbt development by scaffolding projects, preparing specialized dbt agents, and running work from mission tasks. This enables fast, repeatable patterns for data modeling at scale.

Key Features & Functions

Project initialization

Create standardized dbt project structures and configs.

Specialized agent creation

Spin up dbt-focused data agents with the right tool access.

Task-driven execution

Run dbt tasks from todos to keep work coordinated and auditable.

Note: Exact argument names may vary by deployment. The tables below describe typical fields and formats.

Input Parameters for Each Function

`dbt_building_tools_init_project`

Parameters

Name	Definition	Format
project_name	Name for the new dbt project.	String
adapter	Target adapter (e.g., snowflake, bigquery, redshift).	String
target_schema	Default schema for models.	String
profiles_source	Location/reference to dbt profiles for credentials.	String/JSON
seed_demo_data	If true, populate sample structures or seeds.	Boolean

`dbt_building_tools_create_bot`

Parameters

Name	Definition	Format
bot_name	Human-friendly name for the dbt agent.	String
available_tools	(Legacy) Comma-separated tool groups to grant (e.g., dbt_action, git_and_file_tools, snowflake_tools). Prefer `default_tool_groups` + `tool_access_mode`/`tool_access_list`; legacy values migrate into durable context defaults.	String
slack_access_open	Whether Slack access is open to all users initially.	Boolean
runner_id	Optional runner binding for execution.	String

`dbt_building_tools_run_todo`

Parameters

Name	Definition	Format
todo_id	The mission todo to execute (drives context).	String
command	dbt operation to perform (e.g., build, run, test, seed).	String
models	Optional selector(s).	String or List
vars	Runtime variables to pass to dbt.	JSON
full_refresh	Whether to perform a full refresh for incremental models.	Boolean

Use Cases

Initialize a new dbt project for a domain team
Create a dbt-specialized agent to own model build/test tasks
Run dbt build/test as part of a mission, tied to todos and dependencies

Workflow/How It Works

Initialize project scaffolding for consistent structure.
Create a dbt agent with appropriate tool access (dbt_action, git_and_file_tools, snowflake_tools).
Wire mission todos to dbt runs; record results and artifacts.
Iterate with PRs and CI-like patterns using Git Action Tools.

Store build artifacts and docs so runs stay auditable and easily reproducible.

Integration Relevance

DBT Action Tools for build/test/compile operations
Git Action Tools for versioning, diffs, and history
Project Manager Tools to orchestrate todo execution
Snowflake Tools for stage access and Snowpark usage when needed

Configuration Details

Standardize adapter configuration and profiles handling.
Store run outputs and docs as artifacts for reproducibility.

Limitations or Notes

Parameter names can differ by implementation. Validate against your environment’s schema before automating.
Long-running builds should be scheduled with adequate warehouse capacity.

Output

Init: Project scaffolding details (paths, profiles references)
Create Data Agent: Bot id and capabilities summary
Run Todo: Execution log, status, and artifacts (tests, docs, manifests)