speckit-clarify

To identify missing requirements in a feature spec, you need targeted clarification questioning on ambiguous scope, roles, data models, and edge cases. This exposes underspecified areas early, capturing answers directly back into the spec to prevent downstream rework during planning and implementation.

To clarify ambiguous feature requirements, ask up to five highly targeted questions covering scope, roles, data models, edge cases, integrations, and non-functional quality constraints. Prioritizing these clarification questions helps surface critical missing decisions before implementation begins.

You update a feature spec incrementally without breaking consistency by capturing concise answers directly into the document as they are resolved. This preserves spec alignment and reduces downstream rework by ensuring all scope and data model updates are recorded immediately.

You need spec clarification before planning a feature when scope, roles, data models, edge cases, integrations, or non-functional quality constraints are still incomplete. Early questioning of these ambiguous areas speeds up refinement and prevents implementation rework.

The best way to reduce ambiguity in software feature specifications is applying prioritized, targeted questioning to expose incomplete decisions. Capturing these answers directly into the spec ensures the document stays aligned and minimizes downstream rework during the build phase.