Training Data Audit with Claude Code: Artifact Delivery
A production playbook for training data audit in cross-industry operations using Claude Code: artifact delivery, run-scoped inputs, logs, typed results, and artifacts.
Audience: AI governance teams
The problem
AI governance teams need training data audit to run repeatedly against datasets, consent records, filters, and model usage 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
Require Claude Code to write customer-visible files under /skill/output/artifacts, validate filenames and sizes, then return signed artifact metadata in argo.result.v1.
Tradeoffs and failure modes
Artifact policy constrains file output, but customers receive files that are durable, typed, and safe to download. For training data audit, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.
Artifact manifest
artifacts:
- training-data-audit-summary.md
- training-data-audit-evidence.csv
- training-data-audit-review.json
signed_urls: true
retention: org_policy
Run this on Argo