speckit-clarify

To identify ambiguities in a feature specification, analyze functional requirements, user roles, data models, edge cases, and integration points to surface underspecified areas and generate targeted clarification questions.

Write effective clarification questions by focusing on concise, verifiable queries that target underspecified areas like non-functional constraints and data models, ensuring answers can be directly applied to the next implementation phase.

The best way to reduce rework from underspecified requirements is to surface up to five targeted clarification questions before implementation, encoding accepted answers directly back into the spec for traceability.

Yes, you can update a specification iteratively by applying atomic writes to a Clarifications section after each accepted answer, ensuring minimal drift and maintaining traceability throughout the process.

Review functional requirements, user roles, data models, non-functional constraints, edge cases, and integration points to find missing requirements and ensure comprehensive feature specification coverage.

Yes, speckit-clarify supports encoding clarification answers directly into the spec by applying iterative, atomic writes to a dedicated Clarifications section after each answer is accepted.