ask-questions-if-underspecified

Requirement clarification involves asking targeted questions to resolve ambiguities in scope, objectives, or constraints before starting development. This skill prompts you with must-have questions to prevent incorrect implementation and wasted effort.

An AI assistant handles ambiguous scoping by asking clarifying questions to resolve unclear success criteria, scope, constraints, or environment details. It ensures a minimum set of must-have questions are answered before initiating work.

Defining success criteria in prompt engineering workflows is best achieved by asking clarifying questions that offer multiple-choice options and defaults. This frictionless interaction speeds up the clarification process before any changes are made.

Yes, you can use requirement clarification to scope out vague tasks like improving a user dashboard. The skill asks targeted questions such as what specific metrics should be prioritized or if there are any performance constraints before proceeding.

Your AI assistant may start implementing changes early because requirements are underspecified. Applying a clarification skill forces the assistant to ask a minimum set of must-have questions, resolving multiple interpretations before initiating work.

You need to ask clarifying questions for an underspecified software engineering task when there are multiple interpretations or unclear success criteria, scope, constraints, or environment details. This prevents wasted effort and incorrect implementation.