Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add KerberosClaw/kc_ai_skills --skill grillgit clone --depth 1 https://github.com/KerberosClaw/kc_ai_skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kerberosclaw/kc_ai_skills/grill)<a href="https://agentmods.dev/skills/kerberosclaw/kc_ai_skills/grill"><img src="https://agentmods.dev/badge/skills/kerberosclaw/kc_ai_skills/grill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kerberosclaw/kc_ai_skills/grill"><img src="https://agentmods.dev/badge/skills/kerberosclaw/kc_ai_skills/grill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00121 | $0.01673 |
| Opus 5 | $0.00060 | $0.00837 |
| Sonnet 5 | $0.00024 | $0.00335 |
| Haiku 4.5 | $0.00012 | $0.00167 |
Grade A, and why
grill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 fact | filesystem / 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 fact | repo 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:
- 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。
- 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」。
- 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:
- 提議一個 canonical 詞:「所以我們統一叫『孵化』?」
- User 點頭 → 當場 inline 更新 repo 根目錄
CONTEXT.md,不攢批、不留到收尾:
- **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 → 解除禁動,交棒給實作(或 spec / prd-create 等下游流程)。
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
- 停止條件 = 共同理解確認,不是題數
- 一次一題、附建議答案
- Fact 自己查、decision 才問人 — 本 skill 最鋒利的一條
- 詞彙敲定當場落檔,lazy 建立、只收專案獨有詞、禁實作細節
- 確認前禁動手;user 不在場則假設明文化、繼續走
- 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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 93 lines · 121 tokens per session scan A 3052eab8efeb
grill is a skill published in the GitHub repository KerberosClaw/kc_ai_skills (79 stars, last pushed 2d ago), licensed MIT. It adds 121 tokens to every session and 1,673 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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