speckit-clarify

To clarify ambiguities in feature specifications, run a structured scan across functional scope, data models, UX flows, and non-functional constraints to identify gaps. This process generates prioritized clarifying questions and logs answers directly into the spec file to resolve underspecified requirements.

The best way to identify missing requirements is performing a structured coverage scan across domain models, interaction flows, edge cases, and integration dependencies. This surfaces underspecified areas and generates a capped queue of clarifying questions to minimize downstream rework.

You resolve underspecified scenarios for data models and user interactions by generating targeted clarifications. A structured ambiguity assessment scans these sections, produces up to five prioritized questions, and applies the session-based answers directly into the specification document.

Yes, you can automatically update functional, data, UX, and non-functional spec sections with clarification answers. The system integrates responses from the clarification session directly into the corresponding specification sections to ensure the document reflects resolved ambiguities.

You should generate a maximum of five prioritized clarifying questions for a feature spec. Capping the queue at five targeted questions minimizes downstream rework while ensuring structured ambiguity assessment covers all critical planning decisions.

A spec ambiguity scan covers Functional Scope and Behavior, Domain and Data Model, Interaction and UX Flow, Non-Functional Attributes, Integration and External Dependency, Edge Cases, Constraints and Tradeoffs, Terminology, and Completion Signals to ensure comprehensive requirement clarification.