跳到主要内容

grilling

收藏0

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

让 AI 安装

星光尘缘最新版本 0.1.0更新于 2026-10-31下载 0 次

SKILL.md

Interview the user relentlessly until you reach a shared understanding. Map this as a design tree: every decision branches into the decisions that hang off it.

Work the tree in rounds. The frontier is every decision whose prerequisites are already settled: the questions you can ask now without guessing at answers you haven't heard yet. Ask the whole frontier in one round: number each question and give your recommended answer. Then wait for the user's answers before the next round.

Format a round like so:

❓ **Q1** - **<question title>**: <question body, might be multiple paragraphs, including multiple choices>

➡️ <your recommended answer>

---

❓ **Q2** - **<question title>**: <question body, might be multiple paragraphs, including multiple choices>

➡️ <your recommended answer>

Word each question so "yes" accepts your recommended answer.

Each round the user answers reshapes the tree: settled decisions push the frontier outward and unblock questions that depended on them. Recompute the frontier and ask the next round. A question whose answer depends on another question still open in this round belongs to a later round, not this one.

Finding facts is your job, never the user's. When a frontier question needs a fact from the environment (filesystem, tools, etc.), dispatch a sub-agent with a fresh context window to find it; don't ask the user for anything you could look up yourself. Don't block on it: a running exploration is an unsettled prerequisite, so only the questions downstream of it wait for the sub-agent to report; ask the rest of the frontier now. The decisions are the user's: put each to them and wait.

The session is done when the frontier is empty: every branch of the design tree visited, nothing left silently assumed. Do not act on it until the user confirms you have reached a shared understanding.

用途与依赖

作者没写,这些是平台读文件后整理的,以文件为准。

使用示例

请求:我打算把公司的内部工具从单体拆成微服务,帮我盘问一下这个想法。

预期

技能会先给出第一轮“前沿”问题,例如“❓ **Q1** - **迁移范围**:先拆哪几个模块?…… ➡️ 建议先拆通知与报表两个边界清晰的模块”,每题编号并附推荐答案,且把问题措辞成回答“是”即接受推荐。等用户逐条回答后,它会根据已确定的决策推出下一轮问题(依赖未定问题的留到后面),如此循环,直到所有分支都被覆盖、没有隐含假设,最后请用户确认理解一致再开始动手。

适用场景
  • 想对某个方案、决策或点子做压力测试,找出遗漏和隐含假设
  • 在动手实施前把需求、范围、取舍逐层问清楚
  • 想法还很模糊,需要被一轮轮追问逼出清晰结论
  • 要评估一个架构/产品方向,但还没想好各个依赖决策
  • 希望每个问题都附带推荐答案,便于快速确认或否定
输入
  • 要盘问的计划、决策或想法
  • 对每轮问题的回答(可逐条确认推荐答案或给出不同意见)
  • 最终“理解一致、可以开始”的确认
输出
  • 分轮次呈现的编号问题列表,每题附推荐答案
  • 随用户回答更新后的设计树与下一轮前沿问题
  • 一份双方确认过的共识结论(当前沿清空时)
运行环境
未说明
依赖
无