transform

Transform

Author and refactor the dbt project: both the SQL transformations (staging to marts, tests, docs) and the semantic layer on top (entities, dimensions, measures, metrics). Both are the same job, writing reviewable diffs to the dbt project, which is the source of truth. This is the building half of the loop. It writes only to the repo, as reviewable diffs, and runs against a dev target only.

How to drive it

uv run "${CLAUDE_SKILL_DIR}/scripts/run.py" <subcommand> [flags]

You author the dbt file content; the engine validates it, computes the diffs, and stores the proposal as a plan. Hand content over with --edits-file <path> (or - to read stdin), a JSON payload:

{"edits": [
  {"path": "models/staging/stg_orders.sql", "kind": "model_sql", "content": "..."},
  {"path": "models/staging/stg_orders.yml", "kind": "schema_yml", "content": "..."}
]}

kind is model_sql, schema_yml, or semantic_yml (optional on semantic define|update|plan, which imply it). Model SQL must be a single read-only SELECT once its jinja is stripped; semantic YAML is validated against MetricFlow's schemas, cross-reference-checked, and (when dbt is available) parsed by dbt itself before the plan is accepted.

Bootstrapping a project

If no dbt project exists in the repo, offer transform init before anything else: transform plan needs a project to edit. Ask the user for the project name and confirm the connector with them, then run:

uv run "${CLAUDE_SKILL_DIR}/scripts/run.py" transform init "<name>" --connector <c>

The engine renders the whole skeleton (dbt_project.yml, models/staging/ and models/marts/, a profiles.yml with a single dev target and no secrets) and records connector, dbt_project_dir, and dbt_target: dev in .dex/config.yml; do not hand-write these files yourself. Init never assumes a connector: it errors rather than defaulting, so always pass the user's confirmed choice (a connector: already committed in .dex/config.yml also counts). Every connector is supported: DuckDB, BigQuery, Snowflake, Databricks, and Postgres. DuckDB needs a warehouse path (--path, or the duckdb.path config). BigQuery needs a GCP project (usually bigquery.project in .dex/config.yml; confirm it with the user) and writes builds to a dedicated dev dataset (bigquery.dev_dataset, default dbt_dev); auth is Application Default Credentials, so if credentials are missing tell the user to run gcloud auth application-default login, never ask for a key. Snowflake writes builds to a dedicated snowflake.dev_database/dev_schema on the pinned warehouse; Databricks writes builds to a dedicated databricks.dev_catalog/dev_schema on the pinned SQL warehouse (if credentials are missing tell the user to run databricks auth login, never ask for a token); Postgres writes builds to a dedicated postgres.dev_schema (default dbt_dev), with the password reaching dbt only through the PGPASSWORD environment variable. All of them discover their connections and refuse with the fix named when none resolves. Init refuses if any dbt project already exists.

dbt SQL models

• transform plan "<intent>" --edits-file <path|-> validates the edits and returns them as diffs with a plan id. Nothing is applied yet. Add --scaffold <table> (repeatable) to generate a staging skeleton (stg_<table>.sql plus per-model YAML with key tests and PII meta) from the .dex/ cache instead of, or on top of, hand-authored edits.
• transform apply [plan-id] writes the plan into the dbt project (the latest unapplied plan when no id is given; any plan kind, semantic included). The result is still a reviewable git diff for the user. If a human edited a file after the plan was made, nothing is written: the divergence comes back as diffs with needs_confirmation, and you should re-plan against current state (or, only when the user says so, re-run with --confirm).
• transform plans lists stored plans (pending and applied, newest first), so you never need to browse .dex/plans/ by hand.
• transform build --target dev runs dbt build against a dev target. The engine surfaces a cost preflight first and runs only with --confirm (plus a --budget on billed connectors). On BigQuery there is no upfront estimate (dbt has no dry-run), so always get an explicit byte budget from the user and pass it as --budget <bytes>; never invent one. Each statement dbt runs is capped server-side by the profile's maximum_bytes_billed, and the envelope reports billed bytes afterward. Production-looking targets are refused outright; --confirm cannot override that. dbt runs with its working directory pinned to the project dir, so relative paths in profiles.yml resolve against the project. When the project declares packages (packages.yml) and dbt_packages/ is missing, the engine runs dbt deps automatically before the build.
• transform deps installs dbt packages explicitly (also the refresh path when dbt_packages/ exists but is stale). No confirmation needed: deps writes only inside the project and never touches the warehouse.

Preparing the dev target

Before the cost gate, and for free, transform build refuses two things and names the fix for each. Neither costs anything to check, so both surface on the unconfirmed call rather than after a budget has been agreed.

Config that has drifted from the profile. transform init renders .dex/config.yml into the project's profiles.yml, and dbt reads only the profile from then on. If a later config edit never reached it (a retargeted dev_database, a different warehouse), the build refuses and names both values and both files. Edit one to match the other. The engine never rewrites profiles.yml, which you may legitimately have hand-edited.

A dev target that does not exist. On Snowflake, dbt creates schemas but never databases, so a missing dev_database is refused with the CREATE DATABASE statement to run; dex will not create it for you, because its only writes are reviewable diffs inside the repo. On DuckDB the dev target is a database file, and dbt would happily create an empty one, then fail every source() relation with a confusing catalog error. The convention there: copy the shared source warehouse to the dev target path (for example cp shared/f1.duckdb <project>/dev.duckdb), or point the dev target at an existing file. Projects without sources just get a warning and an empty database, which is fine for model-only builds.

The semantic layer

• semantic define ... --edits-file <path|-> and semantic update ... author and evolve the dbt semantic models (entities, dimensions, measures, metrics) as plans. define refuses names that already exist (use update); update refuses names that do not (use define). For one logical change that mixes both (evolve existing metrics and add the helpers they depend on), use semantic plan ...: it accepts mixed intent and classifies each name, and the envelope reports the split as defined and updated.
• Plan-time validation is layered so a plan that validates will build: MetricFlow's schemas check the shape; the engine resolves every metric input (ratio and derived metrics reference metrics, not measures; a measure only becomes a metric via create_metric: true, and the error names that fix); and finally the emitted YAML is run through dbt's own parser against a throwaway copy of the project. A plan that fails parse is refused, not stored. If dbt is not installed the parse degrades to a warning; --no-parse skips it explicitly.
• A semantic plan is applied like any other: transform apply [plan-id] writes its YAML into the dbt project (no id applies the latest unapplied plan).
• dbt cannot parse semantic models in a project without a MetricFlow time spine; the engine warns when one is missing and defers the parse gate until one exists. Author it like any other model (a day-grain date model plus YAML with a time_spine: config) in the same or a separate plan.
• viz preview is not yet implemented (it returns not_implemented); the Viz integration arrives later.

Guardrails (enforced in the engine, not here)

• Writes confined to the repo, and within it to the dbt project's model paths. dex never writes to source warehouse data.
• Dev-target only. Prod-target execution is never initiated by dex.
• Cost surfaced before any spend. A build that would spend requires explicit confirmation and a session budget.
• Propose, don't impose. Human edits to dbt (SQL and semantic YAML) are authoritative; on conflict the engine surfaces a diff and asks rather than overwriting.
• PII flags propagate from the cache into emitted dbt (model and column meta), never example values.