speckit-clarify

Finding ambiguities in feature specs involves scanning across functional, data, UX, non-functional, and integration taxonomy categories to identify underspecified areas. This prevents misaligned implementation and downstream rework before planning begins.

Common requirement gaps in product specifications include missing data models, incomplete UX flows, and undefined non-functional requirements. Scanning feature specs across taxonomy categories identifies these partial details to prevent costly rework.

Clarifying product requirements during pre-planning involves generating up to 5 targeted questions to resolve high-impact spec gaps. Accepted answers integrate directly into the spec file and re-validate associated quality checklists.

You can automatically update a feature spec file by integrating accepted clarification answers directly into the document. The system then updates relevant sections and re-validates the spec quality checklist if one exists.

Spec clarification works with spec-kit structured product feature specs by scanning across 10 core taxonomy categories including functional scope, data model, and UX flow. It dispatches configured pre and post execution extension hooks as required.

Undefined non-functional scope leads to failed acceptance criteria because underspecified feature specifications cause misaligned implementation. Identifying these requirement gaps before engineering begins prevents costly downstream rework.