reconciliation

To compare generated DDL artifacts against existing schemas, use this Skill to systematically analyze MD-DDL physical artifacts against baseline documentation or live deployed schemas, producing a structured gap report that highlights discrepancies in entities, attributes, and constraints.

Gap analysis between your data model and deployed schema is performed by comparing generated MD-DDL physical artifacts against existing states. The process identifies differences in entities, attributes, data types, and constraints, producing a structured gap report to guide resolution.

Schema reconciliation distinguishes intentional changes from regeneration noise by correlating physical gaps back to logical causes defined in a lifecycle change manifest. This root cause analysis maps physical differences to intentional schema evolution documented in the manifest.

Yes, you can use this Skill to find drift after updating your canonical data model. It compares newly generated database schema artifacts against previously deployed versions in the baselines directory to ensure accurate reflection of changes and identify any drift.

Data reconciliation requires specific generation skills to produce MD-DDL physical artifacts and reference documents for comparison, such as baseline documentation or live deployed schemas found in the baselines directory for the target domain.

Automated artifact comparison should not be used when you lack a lifecycle change manifest. Without a manifest to correlate physical gaps back to logical causes, the reconciliation process cannot distinguish intentional schema evolution from regeneration noise.