speckit-clarify

To resolve ambiguities in a feature specification, scan the document across functional scope, data model, and UX categories to identify coverage gaps, then generate prioritized clarification questions for stakeholders. This prevents misalignment and costly downstream rework.

Requirement validation for feature specs is the process of checking a document for missing decision points across integration, non-functional, and edge case categories. You need it during pre-planning to ensure engineering receives a complete, unambiguous blueprint.

Identify missing decision points in an underspecified feature requirement by applying structured ambiguity scanning across 10+ taxonomy categories. This flags partial information affecting architecture, task decomposition, and test design to maximize rework reduction.

Yes, you can automatically integrate accepted stakeholder answers directly into your feature spec file. The system updates relevant sections to resolve ambiguities and validates the final document for consistency, ensuring no contradictory text and complete coverage.

The best way to prevent rework caused by ambiguous software requirements is generating up to five high-impact, answerable clarification questions per session. Targeting gaps that affect architecture and test design delivers a clear blueprint for implementation planning.

Yes, spec clarification works for non-functional and integration requirements by evaluating your feature spec across comprehensive taxonomy categories. It flags missing details like delivery retry rules or accessibility requirements to ensure complete functional scope coverage.