Prompt Library Hardening with Claude Code: Result JSON Schema
A production playbook for prompt library hardening in cross-industry operations using Claude Code: result json schema, run-scoped inputs, logs, typed results, and artifacts.
Audience: AI platform teams maintaining reusable prompts
The problem
AI platform teams maintaining reusable prompts need prompt library hardening to run repeatedly against prompt folders, examples, red-team cases, and tool 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 prompt library hardening 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 prompt library hardening, 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": "prompt library hardening completed",
"body": { "type": "prompt_library_hardening", "data": {}, "exceptions": [] },
"artifacts": []
}
Run this on Argo