speckit-clarify

Spec ambiguity detection involves scanning a feature specification against a structured taxonomy covering functional, data, UX, non-functional, integration, and edge-case categories to mark each area as Clear, Partial, or Missing.

Generating clarification questions for underspecified requirements uses an Impact×Uncertainty heuristic to produce up to five high-impact, short-answer or multiple-choice questions targeting the most critical ambiguities.

Spec clarification works directly with spec-kit project structures by applying the workflow to directories containing a .specify folder to scan spec files and prioritize ambiguities for interactive resolution.

To integrate clarification answers back into a spec file, ask questions sequentially, validate responses, and incrementally update the Markdown structure with atomic writes and validation checks before writing.

The best way to reduce rework from ambiguous acceptance criteria is running a targeted clarification workflow before planning to remove ambiguity from data models, non-functional targets, and acceptance criteria.

You should ask up to five clarification questions for a feature specification, focusing on high-impact areas identified through an Impact×Uncertainty heuristic to resolve ambiguities efficiently.