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Grill Me Socratic Questioning

AI Agent Updated 2026.08.30

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Follow https://skillhub.cn/install/skillhub.md to install @user_310a65be/grill-me-ytang.

About this skill

What Problem It Solves

Many failed plans are not caused by weak execution, but by unexamined assumptions. When a user provides a rough plan, an AI may start acting immediately and later miss constraints, boundaries, or important decisions. Grill Me addresses the gap between an idea and implementation by forcing a structured questioning pass before work begins.

How It Works and Where It Fits

It turns the plan into a design tree: each node is a decision, its children are the assumptions supporting that decision, and deeper nodes are downstream choices that depend on those assumptions. The skill first identifies the frontier, the set of questions that can be asked right now without guessing, then lists all frontier questions at once and waits for answers. After the user responds, the tree is updated and newly unlocked questions become the next frontier. The process repeats until no fuzzy assumption remains.

  • Triggered by phrases such as grill, 追问, 拷问, or 压力测试.
  • When facts require environment inspection, it prefers sub-agent lookups instead of asking the user for local state.
  • Questions that depend on unresolved answers are deferred, avoiding premature assumptions.
  • For complex plans, it is useful to cap the discussion around 20 questions.

It fits best when there is already a plan, decision, or architecture sketch that needs stress testing. It is less suitable for trivial edits, simple error lookups, or executing an already clear PRD.

Use Cases

  • Before implementing a refactor, ask the model to pause and question the architecture dependencies, migration risks, and rollback boundaries.
  • During migration planning, have the model list unconfirmed assumptions in a design tree instead of changing configuration directly.
  • Before building a feature, ask it to question user paths, error branches, acceptance criteria, and missing permissions.
  • Before execution, let it use sub-agents to inspect environment facts and defer questions that depend on unanswered items.

Best For

  • Backend engineers preparing a service migration who want dependencies, compatibility, rollback, and environment facts examined before execution.
  • Product managers reviewing draft PRDs who want ambiguous acceptance criteria, edge cases, and missing assumptions clarified.
  • Project owners leading refactors who need plans broken into a design tree and unresolved assumptions questioned layer by layer.
  • Independent developers using AI for complex tasks who want the model to expose gaps before starting implementation.