Human Review Queue Preparation with Claude Code: Result JSON Schema
A production playbook for human review queue preparation in cross-industry operations using Claude Code: result json schema, run-scoped inputs, logs, typed results, and artifacts.
Audience: Operations teams combining automation with review
The problem
Operations teams combining automation with review need human review queue preparation to run repeatedly against agent output, confidence notes, artifacts, and escalation policy. 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 human review queue preparation 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 human review queue preparation, 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": "human review queue preparation completed",
"body": { "type": "human_review_queue", "data": {}, "exceptions": [] },
"artifacts": []
}
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