speckit-clarify

To identify ambiguities in a feature specification, use a structured clarification workflow that scans for missing details across functional scope, data models, UX flows, and acceptance criteria, then records targeted decisions directly back into the spec.

Capturing specification clarifications with traceability requires updating a dedicated Clarifications section within your spec, ensuring all decisions regarding runtime ownership and user interactions are formally persisted for governance.

The best way to surface underspecified areas in a planning workflow is to apply an interactive clarification session that asks up to five highly targeted questions, encoding the answers to improve plan readiness and reduce rework.

Yes, this spec clarification process supports integration with speckit-like tooling to drive downstream planning and validation, encoding answers directly back into your repository feature specs.

You can use highly targeted questions to resolve gaps in acceptance criteria and data models, as the interactive session specifically surfaces missing details across these dimensions and updates the spec accordingly.

Feature specs often have missing details after initial planning due to underspecified areas across runtime ownership and non-functional criteria, which require a structured ambiguity detection process to resolve before implementation.