sue-doc-code-consistency

Auditing ML experiment documentation for consistency involves checking SKILL.md frontmatter, cross-references, and generated GitHub Pages docs against actual code. This detects stale content, validates workflow topology references, and ensures canonical contract compliance.

Stale doc detection identifies obsolete references and doc-code mismatches in SUE framework documentation. You need it when updating skills to prevent broken workflows, user errors, and misaligned implementation caused by outdated markdown files or invalid cross-references.

Validating skill frontmatter requirements and cross-references involves scanning SKILL.md files to enforce compliance with canonical contracts and check semantic accuracy. It detects broken workflow topology references and ensures documentation completeness across the workspace.

Yes, you can run a standalone documentation audit for doc-code mismatches without a full framework review. It operates in dual mode, allowing targeted checks for stale references and semantic accuracy independently of broader system audits.

Your documentation audit shows broken cross-references in generated GitHub Pages docs due to stale content and unoperationalized shared contracts. Running a consistency check detects these mismatches, validates workflow topology references, and enforces canonical skill contract compliance.