What problem does it solve? Writing feature specifications manually produces inconsistent documents that lack traceability, visual flows, and testable acceptance criteria, and they quickly drift out of sync with the codebase. ## Core Features & Use Cases - Structured Spec Generation: Creates a complete spec.md with prioritized user stories, Gherkin scenarios, Mermaid flowcharts, numbered acceptance criteria (AC), and functional requirements (FR) under .specs/features/NNN-feature-name/. - Scope Analysis and Splitting: Detects when a request covers multiple independent domains, proposes a split, and writes seed.md files preserving context for deferred sub-features. - Behavioral and UI Contract Injection: Detects UI behavioral traits from a taxonomy and injects Gherkin acceptance criteria, plus resolves Penflow UI contracts for interface features. - Use Case: A product engineer runs the command with a feature description like "user notifications" and receives a validated, numbered feature spec with stories, diagrams, and testable criteria, with the roadmap and changelogs updated automatically. ## Quick Start Ask the AI to run /spec-specify with a feature description such as "user can receive real-time notifications" to generate a complete validated feature spec.
