speckit-clarify

To identify ambiguity in a feature specification, scan the document for unclear scope, data model gaps, UX edge cases, and unresolved dependencies to prevent misaligned implementation and rework.

Spec clarification is the process of finding missing decisions in product specs. It is needed to turn vague feature ideas into actionable requirements, ensuring implementation work starts with clear acceptance criteria.

Turn vague product specs into targeted clarification questions by prioritizing high-impact ambiguities, asking a small number of direct questions about roles, states, and integrations, and recording concise answers back into the document.

Yes, spec ambiguity detection works for incomplete UX flows and data models by applying to product and software teams preparing specs where requirements, data models, or integrations are currently incomplete.

The best way to resolve unresolved dependencies in software requirements is to surface the most important clarification questions before planning begins, tightening the spec so the document stays implementation-ready.

Keep a feature spec current by recording each accepted clarification answer directly back into the specification document, ensuring the updated spec remains ready for downstream feature planning and implementation.