skf-refine-architecture

To identify gaps in an architecture document, you can analyze generated skills and API documentation to find undocumented integration paths between libraries and suggest evidence-backed improvements.

Issue detection works by cross-referencing architecture claims against skill API data and Verify Stack feasibility reports to flag contradictions and find missing capabilities backed by API evidence.

The best way to incorporate Verify Stack (VS) feasibility findings is to provide the VS report path alongside the architecture document, strengthening issue detection and gap analysis with feasibility context.

Yes, you can use Python and Pydantic dependencies to automate architecture gap analysis, parsing markdown and JSON inputs to detect issues and output evidence-backed architecture refinement suggestions.

To start architecture refinement, provide the path to your architecture document and optionally a VS feasibility report path; the system will then analyze the data to flag issues and suggest improvements.

You need architecture refinement when your design claims lack API evidence, requiring gap analysis and issue detection to reconcile architecture documents with actual skill API behavior and feasibility constraints.