droughty

name	droughty
description	Droughty schema-introspection toolkit — use when working with Droughty commands, profile/project configuration, LookML generation from warehouse schemas, dbt test generation, DBML diagrams, field documentation, or data quality reports
tags	["droughty","lookml","dbt","schema","bigquery","snowflake","dbml","qa"]

Droughty Skill

This skill activates when working on Droughty-related tasks within a Wire engagement. It provides guidance on the Droughty CLI, configuration, Wire integration conventions, and output structure.

Droughty Overview

Droughty is a schema-driven toolkit that reads the live warehouse and generates semantic layer artifacts. It is the bottom-up counterpart to Wire's top-down, document-driven workflow.

Command	What it does	Requires
droughty dbml	DBML entity-relationship diagram	Warehouse connection
droughty docs	AI field descriptions for all columns	Warehouse + OpenAI API key
droughty qa	LangGraph data quality agent	Warehouse + OpenAI API key
droughty dbt	Pattern-based schema.yml tests	Deployed dbt tables
droughty stage	Staging SQL + sources.yml (BigQuery only)	Source dataset in BigQuery
droughty lookml	Base LookML views, explores, measures	Deployed dbt tables

Pinned Version

Wire pins a specific Droughty version in wire/droughty/pinned_version.txt. All /wire:droughty-setup runs install this version. Current pin: check the file at wire/droughty/pinned_version.txt.

To update the pin, run the refresh script (Wire repo owner only):

bash wire/droughty/refresh_version.sh           # updates pinned_version.txt
bash wire/droughty/refresh_version.sh --commit  # also commits the change

Consultants re-run /wire:droughty-setup --force after pulling the updated repo.

Configuration Files

profile.yaml (~/.droughty/profile.yaml)

Not committed to git — contains warehouse credentials.

BigQuery:

my_engagement:
  type: bigquery
  project: my-gcp-project
  dataset: analytics
  schemas:
    - analytics
    - staging
  openai_api_key: sk-...   # required for docs + qa

Snowflake:

my_engagement:
  type: snowflake
  account: xy12345.us-east-1
  username: analyst
  password: secret
  warehouse: COMPUTE_WH
  database: ANALYTICS
  schema: PUBLIC
  role: ANALYST
  schemas:
    - PUBLIC
    - STAGING
  openai_api_key: sk-...

droughty_project.yaml (git root)

Committed to git — controls output paths.

profile_name: my_engagement

# Wire-aligned output paths
dbml_path: .wire/releases/01-discovery/artifacts/droughty/
field_description_path: .wire/releases/01-discovery/artifacts/droughty/field_descriptions/
dbt_path: ./models/
stage_path: ./models/staging/
lookml_path: ./lookml/views/generated/

LookML File Organisation Convention

Droughty-generated views land in views/generated/. Wire-authored extensions (explores, refinements, derived fields) go in views/extended/. Never edit views/generated/ by hand — Droughty regenerates it on each /wire:droughty-lookml run.

lookml/
├── views/
│   ├── generated/      ← Droughty output — auto-regenerated, do not hand-edit
│   │   ├── orders.view.lkml
│   │   └── customers.view.lkml
│   └── extended/       ← Wire extensions — business logic, explores, refinements
│       ├── orders_extended.view.lkml
│       └── explores.model.lkml

Use LookML refinements in views/extended/ to extend base views:

view: +orders {
  dimension: order_value_band {
    type: string
    sql: CASE WHEN ${order_total} < 100 THEN 'low' ...
  }
}

Wire Command Reference

Command	Purpose
/wire:droughty-setup <release>	Install Droughty, generate profile.yaml + droughty_project.yaml
/wire:droughty-introspect <release>	Schema inventory report — tables, columns, PK/FK coverage
/wire:droughty-dbml <release>	DBML entity-relationship diagram
/wire:droughty-docs <release>	AI field descriptions
/wire:droughty-qa <release>	Data quality agent report
/wire:droughty-stage <release>	Staging SQL + sources.yml (BigQuery only)
/wire:droughty-dbt-tests <release>	Pattern-based schema tests (post-dbt deploy)
/wire:droughty-lookml <release>	Base LookML views (post-dbt deploy)
/wire:droughty-generate <release>	Full Droughty phase in sequence

Workflow Placement

Discovery / existing warehouse audit (Droughty release type):

/wire:new (release_type: droughty)
/wire:droughty-setup
/wire:droughty-introspect
/wire:droughty-dbml
/wire:droughty-docs
/wire:droughty-qa
→ feed artifacts into /wire:problem-definition-generate

Post-dbt phase (within full_platform or dbt_development):

... /wire:dbt-validate ← dbt run ← /wire:dbt-generate ...
/wire:droughty-setup
/wire:droughty-dbt-tests
/wire:droughty-lookml
/wire:droughty-docs
/wire:droughty-qa
/wire:semantic_layer-generate   ← extends Droughty base views

Troubleshooting

droughty: command not found — install with pip install "droughty==[version]". Python 3.9–3.12.3 required.

profile.yaml not found — run /wire:droughty-setup [release].

No tables found — check that schemas: in profile.yaml matches the actual schema names in the warehouse. BigQuery schema names are case-sensitive.

OpenAI API error — check that openai_api_key is set in profile.yaml and the key has billing enabled.

droughty qa timeout — the QA agent runs live warehouse queries; large schemas can take 20+ minutes. Consider limiting schemas in scope.