speckit-clarify

To resolve feature specification ambiguity, you can systematically scan spec files across functional scope, data model, UX, and edge case categories to identify coverage gaps. Generating prioritized clarification questions prevents misaligned implementation and downstream rework before development begins.

Validating acceptance criteria involves scanning feature specs across taxonomy categories like non-functional requirements and edge cases to flag partial coverage. This requirement validation process ensures spec completeness by identifying missing decision points prior to the planning phase.

Spec-kit structured feature development workflows support automated spec clarification by scanning active spec files for ambiguities. The process integrates accepted clarification answers directly back into spec files and updates associated quality checklists to reflect new information.

The best way to generate high-impact clarification questions is to scan feature specifications for missing decision points and produce prioritized multiple choice or short answer queries. Targeting questions that materially impact architecture ensures clarifications prevent unnecessary rework.

Updating quality checklists after resolving feature spec ambiguities involves integrating user answers back into the spec file and automatically re-validating associated checklists. This ensures spec completeness and reflects newly clarified requirements prior to starting implementation.

You should scan feature specs for ambiguity and missing decision points prior to starting the planning phase. Identifying coverage gaps in functional scope, data model, and non-functional requirements early prevents misaligned implementation and downstream rework during development.