speckit.clarify

To identify vague requirements in a feature spec, scan the document using a taxonomy that classifies areas as Clear, Partial, or Missing. This systematic ambiguity detection targets underspecified sections to reduce downstream rework and brittle tests.

The best way to clarify feature specifications is generating up to five targeted, answerable questions about ambiguities. This interactive questioning loop asks questions one at a time with recommended answers and reasoning to elicit precise, testable constraints before implementation.

You integrate clarification answers by applying atomic updates to the spec file under a dated Clarifications section. This spec integration ensures traceability by validating structure and consistency after each accepted answer is written into the active document.

Yes, you need to run repository prerequisite checks before clarifying feature specs. The validation process executes these checks and validates updates after writing accepted answers to ensure the active feature spec maintains structural consistency.

You should use automated clarification questions during pre-planning workflows when active feature specs contain implicit assumptions. Applying this process before technical planning ensures acceptance criteria, data shapes, security constraints, and failure modes are explicit and testable.