speckit-clarify

To identify spec gaps, run a taxonomy-based ambiguity scan on your feature specification to mark categories as Clear, Partial, or Missing, which guides your planning and reduces downstream rework.

The best way to track clarifications is to generate targeted questions, record each Q/A as a bullet under a dated session header in a Clarifications section, and then update the spec with the accepted answers.

You can encode clarification answers by applying the accepted responses directly into the Functional Requirements, Data Model, User Stories, and Non-functional Criteria sections of your spec, then saving the changes.

Yes, you can generate feature clarification questions in either multiple-choice or short-answer formats, prioritizing high-impact gaps to ensure comprehensive coverage of underspecified areas.

You can generate up to five highly targeted clarification questions for a single feature specification to address the most critical underspecified areas without overwhelming the review process.

You need to run a spec clarification workflow when your active feature spec in the .specify/ directory has potential ambiguity, ensuring all taxonomy categories are fully defined before development begins.