Data Quality Exception Review with Claude Code: Result JSON Schema
A production playbook for data quality exception review in cross-industry operations using Claude Code: result json schema, run-scoped inputs, logs, typed results, and artifacts.
Audience: Data operations teams
The problem
Data operations teams need data quality exception review to run repeatedly against failed rows, schemas, validation rules, and samples. In cross-industry operations, the pain is not one good answer; it is repeatability, auditability, exception handling, and evidence that survives handoff.
Implementation path
Define the outer result contract once, let the data quality exception review skill own body.data, and reject terminal output that does not match the expected schema.
Tradeoffs and failure modes
Schema enforcement adds upfront design work, but removes prompt parsing from the product surface. For data quality exception review, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.
Result shape
{
"schema_version": "argo.result.v1",
"summary": "data quality exception review completed",
"body": { "type": "data_quality_exceptions", "data": {}, "exceptions": [] },
"artifacts": []
}
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