docs: add column_value_anomalies test documentation

[devin-ai-integration]

Summary

Adds documentation for a new column_value_anomalies test — a row-level anomaly detection test that operates directly on raw column values rather than on aggregate metrics (min, max, average) per time bucket.

This is a docs-first PR ahead of the implementation in dbt-data-reliability (#982). The new page:

• Explains how the test works (z-score on individual row values against historical baseline)
• Differentiates it from the existing column_anomalies test with a comparison table
• Documents supported configuration parameters: timestamp_column, where_expression, anomaly_sensitivity, anomaly_direction, detection_period, training_period, seasonality, detection_delay
• Provides YAML examples for models
• Is added to the "Anomaly Detection Tests" navigation group in docs.json

Key design decisions:

• time_bucket is excluded — since this test operates on raw individual values (no per-bucket aggregation), time bucketing is not applicable. The test uses training_period and detection_period directly.
• seasonality is included — allows separate baselines per day-of-week (e.g., weekend vs weekday value distributions), which is useful even without time-bucket aggregation.

Screenshots

Mintlify preview: https://elementary-devin-1775975833-column-value-anomalies-docs.mintlify.app/data-tests/anomaly-detection-tests/column-value-anomalies

Note: Screenshots need to be re-captured to reflect the current page (with seasonality, without time_bucket). Please use the Mintlify preview link above for the current rendered page.

Review & Testing Checklist for Human

• Preview the rendered page — use the Mintlify preview link to confirm the comparison table, <Note> block, and config block render correctly. Verify the config block shows seasonality and does NOT show time_bucket.
• Verify config params match the implementation — cross-reference the documented params against the implementation in dbt-data-reliability#982 to ensure they are aligned.
• Confirm timestamp_column should be required — this doc says it's required, unlike other anomaly tests where it's "highly recommended." Confirm this is desired.
• Review which config params are excluded — the config block omits ignore_small_changes, anomaly_exclude_metrics, and dimensions that other anomaly tests support. Verify whether any of these should be supported for this test type.

Notes

• The corresponding implementation PR is dbt-data-reliability#982.
• The test is scoped to numeric columns only as stated in the <Note> block.
• Mintlify deployment and link-rot CI checks passed. The two failing checks (code-quality, create_pylon_issue) are pre-existing issues unrelated to this PR.

Link to Devin session: https://app.devin.ai/sessions/97ebe636d57244fb82a7452e1521604e
Requested by: @arbiv

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Project	Status	Preview	Updated (UTC)
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[devin-ai-integration]

✅ Devin Review: No Issues Found

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[arbiv]

update the pr description and screenshots to the latest version (with seasonality, without buckets)

[devin-ai-integration]

Done — PR description updated and here are fresh screenshots of the current page (with seasonality, without time_bucket):

Page top — title, description, how it works:

Page middle — note, when-to-use table, test configuration:

Page bottom — config block showing seasonality, footer/navigation:

Live preview: https://elementary-devin-1775975833-column-value-anomalies-docs.mintlify.app/data-tests/anomaly-detection-tests/column-value-anomalies