audit-code-artifact

name	audit-code-artifact
description	Audit scientific implementation, exported-model equivalence, dependency completeness, manifests, packaging, and isolated inference. Use when claims depend on code, trained artifacts, evaluators, or deployable entry points.

Audit Code and Artifact

Inspect existing files and test outputs read-only. Do not execute the scientific pipeline or manufacture missing validation evidence.

Checks

1. Trace critical values across data loading, preprocessing, fitting, export, and inference. Check defaults, branches, feature order, transforms, and manifest declarations against the scientific protocol.
2. Verify that decision-relevant tunable parameters live in machine-readable configuration separate from the main implementation logic. Map each decision-relevant parameter from its stable name, value, unit, and provenance to the code that consumes it. Flag duplicated or unexplained values when they prevent a reviewer from determining which setting actually ran.
3. Require existing numeric evidence that the reference pipeline, exported model or raw weights, and final entry point produce equivalent predictions on fixed samples within a stated tolerance.
4. Require an existing clean-directory or evaluator-like smoke test using only collected artifacts and declared dependencies.
5. Check for undeclared local modules, absolute workspace paths, environment variables, auxiliary files, incompatible versions, and entry-point assumptions.
6. Separate implementation correctness from scientific adequacy: a correctly packaged artifact does not establish that its model or validation is suitable.

For a bounded parallel review, use code-reviewer for concrete implementation defects and repo-scout only when imports or artifact dependencies must first be mapped. Ask for evidence and candidate findings, not a verdict.