evidence-shape-check

You can validate artifact structure and identify missing evidence by running Python-based analysis scripts that check artifacts against their declared structures and report unresolved questions, remaining delta, or not proven items.

Evidence-first QA is a quality assurance approach that applies observation lenses to inspect design depth, ensuring artifacts contain required proof while flagging unresolved questions or missing structural elements during validation.

Yes, you need Python installed because the analysis and reporting logic for structure validation and gap detection is implemented through Python scripts included in the skill components.

The best way to check for unresolved questions and not proven items in a codebase is to apply observation lenses that read code-change evidence and validate it against the artifact's declared structure using automated scripts.

You should use design depth inspection for quality assurance when reviewing design documents or code commits to ensure all required evidence is present and the artifact correctly follows its declared structure.