Data Quality Exception Review with Codex: SKILL.md Template
A production playbook for data quality exception review in cross-industry operations using Codex: skill.md template, run-scoped inputs, logs, typed results, and artifacts.
Audience: Data operations teams
The problem
Data operations teams need data quality exception review to run repeatedly against failed rows, schemas, validation rules, and samples. In cross-industry operations, the pain is not one good answer; it is repeatability, auditability, exception handling, and evidence that survives handoff.
Implementation path
Put the operating procedure in SKILL.md, keep examples beside the skill, attach failed rows, schemas, validation rules, and samples per run, and let Argo turn the folder into a repeatable Codex execution.
Tradeoffs and failure modes
A skill folder is less flexible than an open chat, but it gives the product a versioned workflow that can be tested and rolled back. For data quality exception review, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.
SKILL.md starter
# SKILL.md
You run data quality exception review using Codex.
Read only /skill/.argo/inputs.
Write artifacts to /skill/output/artifacts.
Return argo.result.v1 with body.type = "data_quality_exceptions".
Run this on Argo