/authoring-data-quality-checks

name: authoring-data-quality-checks
description: >
  Adds and runs data quality checks (dbt-test style assertions) on a project's warehouse tables and
  saved-query views, and HogQL catalog metrics: not-null, uniqueness, accepted values, referential
  integrity, row-count bounds, freshness, and custom HogQL. Metrics support custom SQL checks only.
  Use when asked to test a model, validate a view, check for nulls or
  duplicates, add data quality checks, find out why a number looks wrong, or judge whether a warehouse
  table is trustworthy before using it in an analysis. To describe what data *means* (metrics,
  certifications, joins), see setting-up-data-catalog instead. Trigger terms: data quality, data test,
  dbt test, not null check, uniqueness check, freshness check, referential integrity, row count check,
  validate model, is this table trustworthy.

Authoring data quality checks

A check is one assertion about one warehouse table, view, or HogQL catalog metric. It compiles to a count-only HogQL query and **passes when it finds zero failing rows**, like `dbt test`. Failing rows are never stored; only counts and the compiled query are, so to see the offending rows you re-run the stored query yourself.

`row_count` is the exception. It passes when the observed count is within its configured min/max bounds, so its `failed_row_count` comes back null and its stored query returns that single count, not offending rows. Read the observed count to judge it rather than looking for matched rows.

Reads go through SQL (`system.information_schema.data_quality_*`); writes and runs go through the data-quality MCP tools.

Before you write anything: look

Two queries save you from the two most common mistakes — duplicating a check, and checking a column that doesn't exist.

-- What is already covered?
SELECT name, subject_name, column_name, check_type, config, severity, last_status
FROM system.information_schema.data_quality_checks
WHERE subject_name = 'orders'

-- What columns are there, and what do they mean?
SELECT column_name, data_type, description
FROM system.information_schema.columns
WHERE table_name = 'orders'

Re-creating a byte-identical check is a harmless no-op — checks are keyed by a fingerprint of the subject, type, column, and config, so an identical create upserts. A _near_-duplicate is not harmless: it doubles the noise for whoever reads the results. If an existing check's assertion is close but wrong, edit the existing check. Updates preserve its identity and history; the subject stays fixed by the URL. An edit that duplicates another check's assertion is rejected.

Choosing checks

Aim for a handful that would actually catch a real regression, not blanket coverage. A model with twenty checks nobody reads is worse than three that fail meaningfully.

Reach for these first, in roughly this order:

• **`not_null` on the columns downstream joins and filters depend on.** The single highest-value

check. A null join key silently drops rows.

• **`unique` on whatever the model claims is its grain.** If `orders` is one row per order, say so.
• **`relationships` on foreign keys.** Catches the join that quietly stopped matching after an

upstream change.

• **`accepted_values` on status and category columns** whose downstream logic branches on them.
• **`freshness` on the timestamp column of anything that syncs.** Catches a dead pipeline, which no

row-level check will.

• **`row_count` bounds** when you know the plausible range. Good for catching a truncated sync.
• **`custom_sql`** only when nothing above expresses the invariant — e.g. cross-column arithmetic

(`select 1 from orders where total != subtotal + tax`). Every row it returns counts as a failure.

Call `posthog:data-quality-check-types` for each type's exact config schema rather than guessing.

One tool set covers every kind of subject. Call `posthog:data-quality-subjects` for the tables, views and metrics you can read, then pass the `subject_type` and `id` it gives you as `subject_type` and `subject_uuid` in `posthog:data-quality-check-create`. Only a subject marked `editable` can carry a check; the others can still be the target of a relationships check. After that a check is addressed by its own id: `-update`, `-delete`, `-run` and `-results` take no subject.

Checks on PostHog tables

`events`, `persons` and `groups` take checks like any other subject: pass `subject_type: "posthog_table"` with the id `posthog:data-quality-subjects` gives you. Every check type works on them, and `relationships` can point at one as its target too.

These tables are large, so consider `lookback_hours` before you author a check on one. It bounds the rows the check reads by the table's own time column: `timestamp` on `events`, and `created_at` on `persons` and `groups`, which is when each was first seen rather than when it last changed. Without it the check reads the whole table, which is allowed and sometimes what you want -- an unbounded `unique` on `events.uuid` says something a windowed one cannot. A check that runs out of ClickHouse's execution budget reports `errored` with the message; adding a window is usually the fix.

On a `relationships` check, `lookback_hours` bounds the rows it checks and `to_lookback_hours` bounds the rows it looks for a match among. Set the second one carefully: a narrow target window makes rows fail for being old rather than for being wrong.

`custom_sql` takes no `lookback_hours`. Put the time filter in the query yourself, or it reads the whole table.

Nothing triggers these tables the way a sync triggers a source table, so their checks run on a schedule, daily by default. Change it with `posthog:data-quality-check-schedule`.

Checks on catalog metrics

Only metrics with a saved `HogQLQuery` definition support checks. Markdown, Trends, Funnels, event series, and metrics without definitions do not. A metric can return any number of rows and columns.

Create a `custom_sql` check