dbt schema-verify

When to invoke this skill — every time

Run altimate-dbt schema-verify --model <name> before declaring any of the following tasks complete:

• Creating a new dbt model that has (or will have) a schema.yml entry
• Modifying an existing model whose schema.yml declares columns
• Refactoring a CTE into its own intermediate model
• Renaming columns or changing their order
• Changing materialization config in a way that re-creates the table
• Any task that says "match the schema", "produce these columns", "the output should have columns X, Y, Z", or references a _models.yml
• Any task with AUTO_*_equality or AUTO_*_existence tests on a model

If the task touched N models, run schema-verify on all N of them, not just the last one. A build is not a verify.

How to run it

altimate-dbt schema-verify --model <name>

Note: altimate-dbt build --model <name> already runs schema-verify automatically after a successful build and includes the verdict in its response under a schema_verify field. You will see the diff in the same result that reported the build outcome — read it there before deciding the task is done. If you need to re-check after editing, call schema-verify directly.

Returns a structured JSON result:

{
  "model": "int_asana__project_user_agg",
  "verdict": "mismatch",
  "expected_columns": ["project_id", "users", "number_of_users_involved"],
  "actual_columns": ["project_id", "users"],
  "columns_extra": [],
  "columns_missing": ["number_of_users_involved"],
  "columns_reordered": [],
  "type_mismatches": []
}

How to read the verdict

How to act on a mismatch

For each non-empty list, the fix is mechanical:

Then run altimate-dbt build --model <name> again, then re-run altimate-dbt schema-verify --model <name> until verdict is match.

Iron Rules

1. The verdict is the source of truth, not your inspection. Reading the columns yourself and concluding "looks right to me" does not count. Run the command and read its output.
2. A mismatch is "not done", even if the build is green. dbt build only proves the SQL compiled and ran without errors. It does not prove the column shape is correct. Equality tests grade shape AND values.
3. Do not reinterpret the spec to make the model right. The spec is the contract. If the spec lists supplier_company and your model has supplier_id, the answer is to fix your model, not to argue that supplier_id is more useful.
4. Run schema-verify on every model touched, not just the last one. The most common "almost-pass" is N-1 models passing and the Nth one silently failing on column shape. Walk the list.
5. Skip only on no-spec. Do not skip on the grounds that the model is small, or trivial, or "obvious." The spec is small only because the dbt project author already curated it.

Fallback when altimate-dbt is unavailable

If which altimate-dbt returns nothing, do the same diff by hand:

# 1. Read expected columns from any YAML spec under models/
#    dbt allows any .yml filename; common patterns include schema.yml,
#    _models.yml, models.yml, sources.yml, etc.
cat models/**/*.yml | grep -A 50 "name: <name>"   # or: yq eval '...' models/**/*.yml

# 2. Read actual columns from the materialized table
dbt show --select <name> --limit 0

Compare the two ordered lists. Produce the same four-bucket diff (columns_extra, columns_missing, columns_reordered, type_mismatches) in your head, and apply the same fix logic. The mechanics don't change; only the tool name does.

What this skill does NOT cover

• Value-level correctness — passing schema-verify only proves shape; whether the values in each column are right is a separate check (altimate-dbt test + dbt unit tests). Generate unit tests with the dbt-unit-tests skill when the model has non-trivial transformation logic.
• Row count — schema-verify compares columns, not rows. If a refactor drops rows that should be preserved (common when extracting a CTE into its own model — see dbt-develop's "Refactoring a CTE into its own model" section), schema-verify will pass while equality tests fail. Check row counts separately.
• Custom tests — check_* and other non-AUTO tests check task-specific business rules, not column shape. schema-verify can pass while a custom test fails. Read the custom test SQL to understand what's being asserted.