speckit-specify

To generate a feature specification, you provide a natural-language feature description and the skill outputs a structured spec.md file with user scenarios, functional requirements, and measurable success criteria. It validates the specification against a completeness checklist to ensure it is plan-ready.

A spec-kit workflow converts feature ideas into actionable specifications by creating a spec.md file under a resolved specs directory and writing a feature.json file for downstream discovery. It includes a quality validation loop that iteratively improves requirements when issues are found.

Missing critical details are handled through a clarification process that asks up to three targeted questions before finalizing. The feature specification is then updated with the user's answers to ensure complete requirements and entity assumptions.

Yes, the feature specification generation is specifically configured for a Smart-LMS-with-AI-Proctoring project. It applies to creating or updating requirements, user scenarios, success criteria, and entity assumptions tailored to this specific project context.

The specification generation process is limited to creating or updating requirements for a single feature per invocation. It also restricts the quality validation loop to three iterations and asks at most three clarification questions before finalizing the spec.md file.