speckit-clarify

Ambiguity detection in a feature specification scans taxonomy areas like functional requirements, data models, interactions, and edge cases. This structured analysis pinpoints underspecified sections needing targeted clarifications to reduce downstream rework.

The best way to clarify underspecified requirements is through an interactive questioning loop that proposes targeted clarifications. This approach records decisions directly into the spec, preserving existing structure while systematically reducing ambiguity across documented features.

Yes, you can update your feature spec automatically by adding a dedicated Clarifications section and per-session records. As you approve answers during the interactive workflow, incremental spec updates preserve existing structure while documenting resolved ambiguities.

An interactive specification clarification session limits questioning to five targeted questions per cycle. This constraint ensures a controlled workflow focusing on the most critical underspecified areas before recording decisions and updating the feature spec.

Yes, structured ambiguity analysis explicitly handles non-functional criteria and edge cases alongside functional requirements and data models. It scans these taxonomy categories to detect underspecified areas and propose targeted clarifications for comprehensive specification review.

Use a targeted clarification workflow for software engineering specs when formal structures show underspecified areas. It is essential for reducing ambiguity and downstream rework by systematically addressing core requirements, data models, and edge cases before implementation begins.