spec-analyzer

To extract testable features from a natural language specification, provide the markdown or text document to the analyzer. It outputs a structured, prioritized feature backlog with explicit dependencies and acceptance criteria.

Converting product requirements into a structured feature backlog involves parsing narrative specs to isolate individual deliverables. The tool enforces one feature per deliverable and outputs a JSON-like format suitable for downstream planning.

Defining acceptance criteria for dense product specs requires breaking down narrative documents into isolated, testable features. The analyzer bridges requirements and actionable work items by generating structured feature recipes with clear dependencies.

Yes, you can use markdown specifications to generate a prioritized feature list. The analyzer operates directly on markdown, text, or structured spec documents to extract features and produce structured recipes for sprint estimation.

Yes, the spec analyzer enforces one feature per deliverable for sprint planning. It transforms ambiguous specifications into structured feature definitions, ensuring each backlog item has explicit dependencies and clear acceptance criteria.

Narrative product documents result in ambiguous engineering work items because they lack structured, testable feature definitions. Transforming these dense specs into a structured feature backlog with explicit dependencies bridges the gap between requirements and actionable work.