grill-me

To clarify ambiguous software requirements, this Skill conducts self-service research across your codebase and docs, then interviews you with one targeted question per turn to resolve scope and constraints before generating code.

Human-in-the-loop requirements gathering for under-specified change requests is a process where the AI asks exactly one blocking question per turn to resolve missing details and prevent incorrect implementation or misaligned trade-offs.

You need to resolve request ambiguity before taking action when a user's request is under-specified or conflicts with existing project context, ensuring missing details do not lead to premature execution and incorrect implementation.

A prompt clarifies scope and constraints by prioritizing self-service codebase research, asking sequential blocking questions, and concluding with a structured handoff plan that summarizes requirements instead of implementing changes directly.

Yes, you can use this to gather requirements for software-building scenarios with conflicting project context, as it interviews the human to clarify key preferences and safely hands off the plan without executing premature edits.

A limitation of using an AI to resolve request ambiguity is that it does not implement code simultaneously; it exits with a structured handoff plan and recommends the next skill to ensure safe execution after clarification.