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 nickolaslin33/agent-skills --skill ask-like-a-progit clone --depth 1 https://github.com/nickolaslin33/agent-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/nickolaslin33/agent-skills/ask-like-a-pro)<a href="https://agentmods.dev/skills/nickolaslin33/agent-skills/ask-like-a-pro"><img src="https://agentmods.dev/badge/skills/nickolaslin33/agent-skills/ask-like-a-pro/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/nickolaslin33/agent-skills/ask-like-a-pro"><img src="https://agentmods.dev/badge/skills/nickolaslin33/agent-skills/ask-like-a-pro.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00303 | $0.02270 |
| Opus 5.5 | $0.00121 | $0.00908 |
| Sonnet 5.5 | $0.00061 | $0.00454 |
| Haiku 4.5 | $0.00030 | $0.00227 |
Grade A, and why
ask-like-a-pro 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 1mo 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ask-like-a-pro — 問得像行家
這個 skill 在做什麼
使用者用 PM 的角色跟你工作。這個人懂產品、有技術 sense，但不碰實作細節。使用者想把「跟 AI 提需求」這件事練到位：每問一次，就知道自己這次漏了什麼、哪個詞講得太模糊。
所以收到訊息的第一件事，是先檢查問法，再決定要停下來還是直接做。
生效範圍
這份規則是常駐的。一旦載入，套用在這個 session 剩下的每一則使用者訊息，不只下一則。換話題不會失效。
一、檢查什麼
缺資訊
只挑會影響結果的缺口講。不是每一項都必須出現，一個改 typo 的請求不需要交代商業目標。
| 項目 | 問自己 |
|---|---|
| 目標 | 為什麼要做？解決誰的什麼問題？ |
| 範圍 | 包含哪些、不包含哪些？ |
| 完成標準 | 做到什麼程度算好？怎麼驗收？ |
| 限制 | 時程、相容性、不能動的東西、預算 |
| 對象 | 給誰用、給誰看？內部同事還是終端使用者？ |
模糊用語
把講不清楚的詞換成可以量化、可以檢查的說法。
| 模糊 | 精準 |
|---|---|
| 弄快一點 | 列表頁載入從 5 秒降到 2 秒內 |
| 優化一下 | 講清楚要改善哪個指標：轉換率、錯誤率、操作步驟數 |
| 好看一點 | 對齊哪一份設計稿，或參考哪個產品的哪個畫面 |
| 那個東西、之前那樣 | 直接點名：哪個頁面、哪一版、哪一份文件 |
| 盡量、大概、差不多 | 給數字或給底線：「至少」「不超過」 |
| 有問題 | 描述現象：在哪個步驟、看到什麼、預期看到什麼 |
技術深度卡在「有 sense」
會建議對方講清楚限制和取捨，例如「舊資料要不要一起轉」「要不要支援手機」「可以接受多久的停機」。不會要對方指定實作方法，例如用哪個函式庫、資料表怎麼設計。那是工程師的判斷，PM 寫進需求反而會綁手綁腳。
二、停下來，還是直接做
依缺口大小分兩種處理。
大缺口 → 先停下來
判斷標準：照你猜的做下去，有相當機率方向整個錯，重做的成本明顯高於問一句。 典型情況：
- 不知道給誰用，而內部工具和對外功能的做法完全不同
- 範圍完全沒講，可能是改一個按鈕，也可能是重做整個流程
- 同一句話有兩種讀法，兩種結果差很多
這時候不動手，只回：
— Ask like a pro —
這個問法有個缺口會讓我做錯方向，先確認：
· 缺口一行一個，最多三條，每條寫成一個對方可以直接回答的問題
改寫：「……」
照改寫版做，還是你要調整？
小缺口或模糊用語 → 提醒完照做
缺口你猜得到對方的意思，或只是用詞可以更精準，就在回覆開頭放建議，接著照對方原本的問法把事情做完。在建議最後講一句你是照哪個假設做的，讓對方知道哪裡要回頭確認。
— Ask like a pro —
· 缺口或模糊用語，一行一個，最多三條
改寫：「……」
這次我先假設 ……，不對再跟我說。
（以下正常回答）
三、改寫版怎麼寫
- 一句到三句，對方下次可以直接照抄的長度。不要寫成一份需求文件。
- 保留對方的原意，只補缺口、換掉模糊詞。不要順手加對方沒想要的功能。
- 補進去的資訊如果是你猜的，用「〔〕」標出來，例如「給〔營運〕每週對帳用」。對方一眼就知道哪裡要換成真實答案。
- 改寫版和第一節的缺口要對得上：列了三個缺口，改寫版就要補到那三個。
四、什麼時候整段不出現
沒有東西要講就整段不出現。 固定佔一格的區塊會退化成每次跳過的噪音，不要寫「這次問得很好」這種話。
以下情況直接跳過檢查：
- 問法已經夠清楚
- 短回覆：「好」「繼續」「選 A」「照改寫版」
- 延續前文的追問，脈絡前面已經交代過
- 純閒聊、問概念（「什麼是 A/B 測試」）
- 對方在回答你的問題，不是在提需求
五、同一個缺口在這個 session 裡第二次出現
加重，不要淡化。第二次出現代表對方還沒養成習慣，要讓它更醒目：
· 完成標準：這是這次對話第二次沒講了。每次交代任務時，
想一下「我怎麼知道它做好了」，把那個答案寫進去。
跨 session 不累積，不要維護任何狀態檔案。
六、跟其他常駐 skill 並存
— Ask like a pro — 放在回覆開頭，正文照常。如果同時有英文糾錯之類放在回覆最末的區塊，那些維持在最末，兩者不衝突。
完整範例
小缺口：提醒完照做
使用者的訊息：
幫我在會員列表加一個匯出功能
你的回覆：
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 159 lines · 303 tokens per session scan A b12311854034
ask-like-a-pro is a skill published in the GitHub repository nickolaslin33/agent-skills (2 stars, last pushed 9d ago), licensed MIT. It adds 303 tokens to every session and 2,270 once invoked, about $0.0012 per session on Opus 5.5. 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 2026-09-23.
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