speckit-clarify

To clarify ambiguous feature specifications, this Skill scans your spec using a defined taxonomy to detect gaps across functional behavior, UX, data models, and edge cases, then interactively asks up to five high-impact clarification questions and writes the answers back into the file.

Reducing ambiguity in product requirements is achieved by classifying spec gaps across non-functional requirements, integrations, and terminology, then collecting prioritized clarification answers through constrained multiple-choice or short-phrase inputs to incrementally update the specification.

You update documentation with interactive questioning by running this Skill in your feature directory; it asks one targeted question at a time and performs incremental in-place spec file updates after each accepted answer to maintain structure within a dedicated session section.

Spec clarification requires compatibility with the .specify/ directory structure and mandatory YAML frontmatter metadata in the feature spec file, ensuring deterministic prerequisite execution before downstream planning steps can be invoked.

Yes, you can use this for feature-spec workflows needing acceptance criteria, as it detects missing decisions across constraints and terminology, asks up to five clarifying questions using controlled formatting, and prepares the spec for downstream planning steps.

Automated spec clarification is limited to asking only up to five prioritized questions per session, requires mandatory YAML frontmatter, and relies on a deterministic prerequisite checker, meaning highly complex or deeply underspecified documents may need multiple passes.