speckit-specify

To create a feature specification from natural language, provide a plain-text idea to generate a structured spec outline with user scenarios, functional requirements, and acceptance criteria. This reduces ambiguity and rework by extracting goals, constraints, and success criteria directly from the description.

The best way to structure product requirements for early-stage discovery is to apply a standardized template with sections for assumptions, risks, and measurable success criteria. This ensures a complete plan, fast handoffs, and clear alignment before functional design begins.

Yes, you can generate acceptance criteria and testing requirements from plain text ideas. The process guides you through clarifications and assumptions to ensure a complete plan, automatically translating natural language feature descriptions into measurable success criteria.

Structured specification generation works for fast handoffs in product planning by producing a ready-to-plan document. It extracts goals and constraints from natural language, giving researchers and writers a repeatable spec generation process that reduces alignment friction.

A complete feature spec to reduce ambiguity and rework should include user scenarios, functional requirements, acceptance criteria, assumptions, risks, and measurable success criteria. Extracting these elements from natural language ensures a clear plan is established for alignment.