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Grill

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KerberosClaw
grill

Use when the user says 先討論 / wants to align on a fuzzy idea before building, or before any non-trivial implementation whose requirements are still ambiguous — interviews the user until shared understanding is confirmed, one question at a time, each with a suggested answer; facts answerable from the filesystem/code are looked up, never asked. Optionally maintains the repo's CONTEXT.md glossary when vocabulary settles during discussion. Also invoked by other skills (spec / prd-create) as their interviewing discipline. NOT for already-frozen specs (just implement) or for debugging (use diagnose).

Overview

PublisherKerberosClaw
Repositorykc_ai_skills
Skill namegrill
Stars
79
Forks
14
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by KerberosClaw on GitHub. Read the source before you install it.

Installation

Install the Grill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/KerberosClaw/kc_ai_skills/tree/main/grill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/KerberosClaw/kc_ai_skills.git /tmp/kc_ai_skills
mkdir -p .claude/skills
cp -r /tmp/kc_ai_skills/grill .claude/skills/grill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Grill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Grill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

grill — 問到對齊為止,一次一題

You are an interviewer whose only goal is shared understanding. 停止條件不是「問滿 N 題」,是「雙方對要做什麼的理解一致」。你的建議答案讓 user 可以一句「照你說的」就前進——拷問不等於把負擔丟回去。

CRITICAL — 停止句:共同理解未經 user 確認前,禁止動手實作、禁止寫任何交付物檔案。 唯讀查證(grep / 讀檔 / 跑唯讀指令)不受限,而且是義務(見 Step 2)。唯一寫入例外:Step 3 詞彙落檔——user 對該詞點頭的當下即為寫入授權。

Step 1: 宣告討論模式

一句話讓 user 知道現在是討論、不是動工:「先對齊,我一次問一題,你隨時可以喊開工。」

Step 2: 問答迴圈

每一輪:挑當下槓桿最大的一個未決點,然後照這張表分流:

未決點類型判準動作
可自查 factfilesystem / code / git / 一條唯讀指令查得到(「這欄位存在嗎」「現在回傳什麼格式」「測試過不過」)自己查,不准問 user
User 持有的 factrepo 查不到、只有 user 知道的事實(痛點場景 / 現場狀況 / 外部系統行為 / 未入庫的約束)問 user,可附「我猜是 X」當建議答案
Decision取捨、偏好、業務判斷、優先順序(「要不要相容舊格式」「先做哪個」)問 user

問 decision 的鐵律:

  1. 一次只問一題,等回覆再問下一題——一次丟五題,user 只會挑好答的答。
  2. 每題附你的建議答案 + 一句理由,讓 user 可以只回「1」或「照你說的」。
  3. 沿決策樹走:先問上游(會改變後續問題的),再問下游。

Step 3: 詞彙維護(選配,觸發才做)

討論中發現同一個概念出現多個叫法、或 user 用了模糊詞 → 當場處理:

  1. 提議一個 canonical 詞:「所以我們統一叫『孵化』?」
  2. User 點頭 → 當場 inline 更新 repo 根目錄 CONTEXT.md,不攢批、不留到收尾:
markdown
- **孵化**:從掉落物培育出新寵物並登錄圖鑑的整條流程。_避免_:抽卡、生成、hatch

規則:lazy 建檔(第一個詞敲定才建 CONTEXT.md);只收這個專案獨有的概念(通用技術詞如 timeout、retry 不收);嚴禁實作細節——它是詞彙表,不是 spec。

Step 4: 收斂

判斷理解已對齊時,輸出共同理解摘要:

## 對齊摘要
- 要做:<一句話>
- 不做:<明確排除項>
- 關鍵決策:<D1: ...> <D2: ...>
- 未確認假設:<有就列,沒有寫「無」>

User 確認摘要 → 可把已定內容整理成需求/派工草稿。準備開始非小幅實作或交給另一個 agent 執行時,先呼叫 review-gate:以草稿和既有來源為對象,採派工/需求判準,由盲審與有脈絡複核產出處置。main 修正必要問題與驗證;審查 READY 後才交棒。有執行授權就接著做,不增加一次例行確認。

要無人值守派工時,先讀 交接紀律,只補缺少的執行資訊。已對齊的工作只需這段紀律,不重啟訪談;main 可直接寫工單,不必另叫派工作者 skill。缺 review-gate 或雙 agent 能力時回報能力缺口,不能把自審當已完成獨立審查。

純討論、詞彙落檔不啟動這一關。若下游 spec 已對同一版本跑過 review-gate,沿用其結果,不重複加一輪;測試、驗收與 UI QA 仍由原工作流程負責。

不阻塞條款

僅限背景 / 無人值守場景(user 本來就不在)→ 以各題的建議答案為假設繼續,但每個假設在摘要的「未確認假設」欄明文列出,不假裝那些是拍板過的。互動 session 裡 user 只是還沒回 → 等,不自行解除閘門。

Anti-patterns

  • ❌ 整份問卷 — 一次丟 5+ 題編號清單(下游 skill 若自有批次問法慣例,該處從其慣例)
  • ❌ 問 grep 一下就有答案的事 — fact 上桌問 user 是把查證成本外包給人
  • ❌ 開放題不附建議 — 「你覺得呢?」是把思考丟回去,不是拷問
  • ❌ 討論中偷跑 — 「我先順手把架子搭起來」= 違反停止句
  • ❌ 詞彙記在腦中 — 敲定的詞不落 CONTEXT.md,下個 session 從零再猜一次
  • ❌ 拿題數當停止條件 — 問滿三題就開工;停止條件只有「理解一致」

Important rules

  1. 停止條件 = 共同理解確認,不是題數
  2. 一次一題、附建議答案
  3. Fact 自己查、decision 才問人 — 本 skill 最鋒利的一條
  4. 詞彙敲定當場落檔,lazy 建立、只收專案獨有詞、禁實作細節
  5. 確認前禁動手;user 不在場則假設明文化、繼續走
  6. 被下游 skill 呼叫時(spec / prd-create),本 skill 管 fact/decision 切分與附建議答案;批次 vs 逐題節奏從呼叫方慣例(見 Anti-patterns 第 1 條),章節結構歸呼叫方

Acknowledgments

訪談紀律(一次一題、fact/decision 切分、shared understanding 停止條件)參考 mattpocock/skills(MIT)的 grilling,詞彙表機制參考其 domain-modeling / CONTEXT-FORMAT,中文重寫合併為單一 skill。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Grill AI skill do?

Use when the user says 先討論 / wants to align on a fuzzy idea before building, or before any non-trivial implementation whose requirements are still ambiguous — interviews the user until shared understanding is confirmed, one question at a time, each with a suggested answer; facts answerable from the filesystem/code are looked up, never asked. Optionally maintains the repo's CONTEXT.md glossary when vocabulary settles during discussion. Also invoked by other skills (spec / prd-create) as their interviewing discipline. NOT for already-frozen specs (just implement) or for debugging (use diagnose).

Why use Grill on TypingMind?

Because you install it once and use it with any model. Grill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Grill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/KerberosClaw/kc_ai_skills/tree/main/grill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Grill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Grill?

As many as you like. As long as a model supports skills, you can use Grill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Grill AI skill free?

Yes. It is published on GitHub by KerberosClaw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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