Schema Mapping with Claude Code: Result JSON Schema

A production playbook for schema mapping in cross-industry operations using Claude Code: result json schema, run-scoped inputs, logs, typed results, and artifacts.

Audience: Data integration teams

The problem

Data integration teams need schema mapping to run repeatedly against source schemas, target schemas, samples, and transform rules. 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 schema mapping 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 schema mapping, 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": "schema mapping completed",
  "body": { "type": "schema_mapping", "data": {}, "exceptions": [] },
  "artifacts": []
}

Run this on Argo