speckit.specify

To convert natural language feature descriptions into formal specs, submit the plain-text input to generate a structured draft. The output analyzes scope, actors, and outcomes to produce a complete specification skeleton with acceptance criteria and readiness signals.

A feature specification skeleton is a structured draft defining scope, user roles, acceptance criteria, and success metrics. You need one during the planning phase to establish traceability from user intent into measurable requirements.

Yes, generating acceptance criteria leverages project context signals to fill gaps in the natural language input. When context is insufficient, it proposes clarifying questions to resolve ambiguities before finalizing the specification.

The best way to resolve ambiguities in a feature spec draft is to use automated clarification prompts. The specification process analyzes your input, flags vague areas, and proposes targeted clarifying questions incorporating collaborative design and governance inputs.

Feature spec generation includes ready-to-fill placeholders to prepare the artifact for downstream pipeline steps. These placeholders ensure the specification is debate-ready and structured for subsequent clarification, planning, and collaborative design phases.

No, you do not need prior design collaboration inputs beforehand. The specification drafting process can analyze minimal natural language input, determine scope and actors, and then actively prompt for clarifications or split analysis where context is missing.