requirements-intake

To prevent scope drift in AI-assisted development, you need to capture stack-aware requirements, constraints, and success criteria before planning starts. Formalizing a validated requirements-spec ensures complexity and out-of-scope boundaries are explicitly defined prior to code execution.

The best way to formalize vague requests is to process them through a requirements intake that evaluates goal source candidates. This generates a durable, machine-validated requirements artifact and assigns a scope safety decision to protect your backlog.

Capturing stack-aware constraints involves reading project state and prior memory to avoid rebuilding context. This intake process validates that explicit complexity costs and success criteria are complete before handing off to implementation.

Yes, you can route AI development tasks by applying a scope safety gate that separates include, defer, reject, and spike additions. Based on the resulting complexity and verification needs, the system recommends the safest next workflow step.

You need a requirements spec before writing plans or executing code whenever complexity, constraints, and out-of-scope boundaries are not yet formalized. This applies to new features, bug fixes, and refactors to ensure a machine-validated intake artifact is complete.

Requirements intake works for new features, bug fixes, and refactors where constraints are not formalized. It produces a validated requirements-spec with a confidence score and a next-skill recommendation for brainstorming, prd, writing-plans, or executing-plans.