speckit-clarify

Identify ambiguities in a feature specification by running a structured clarification session that scans functional scope, data models, and edge cases to generate targeted questions. This process detects underspecified areas early, reducing downstream rework and misaligned acceptance tests.

Track clarification questions and answers by creating a dedicated Clarifications section with daily session headings in the spec. Bullet points are inserted after each answer, and changes are written back to the document, ensuring full traceability and auditability for future reference.

Yes, you can use a structured clarification workflow to improve requirements for active feature specs. It automatically validates incremental updates and scans categories like interaction flows, non-functional criteria, and external dependencies to ensure all requirements are fully specified.

Generate up to five prioritized, answerable clarifying questions to resolve underspecified requirements without overwhelming stakeholders. This concise set targets critical gaps in terminology, integration points, and functional scope to efficiently align expectations.

Feature specification clarification works specifically within an existing .specify project structure. The tool scans the active feature spec, applies in-memory spec tracking, and automatically writes validated incremental updates directly back into the project files.

Categories scanned during an ambiguity detection process include functional scope, data model, interaction flows, non-functional criteria, integration points, edge cases, and terminology. Scanning these areas ensures comprehensive coverage of potential underspecified requirements.