Log Analysis with Claude Code: Result JSON Schema

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

Audience: Platform and support teams

The problem

Platform and support teams need log analysis to run repeatedly against application logs, traces, metrics exports, and incident notes. 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 log analysis 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 log analysis, 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": "log analysis completed",
  "body": { "type": "log_analysis", "data": {}, "exceptions": [] },
  "artifacts": []
}

Run this on Argo