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cold-email-writer

cold-email-writer

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撰寫給用人主管與創辦人的個人化開發信（Cold Email）—— 內容具體、具人情味，而非一份簡報提案

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更新於 2026/6/19
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名稱
cold-email-writer
描述

撰寫給用人主管與創辦人的個人化開發信（Cold Email）—— 內容具體、具人情味，而非一份簡報提案

Cold Email Writer

何時使用此 Skill

當使用者想要執行以下操作時使用此 Skill：

  • 撰寫開發信（Cold Email）給用人主管、創辦人或招募人員
  • 針對沒有內部推薦的職缺主動發信聯繫
  • 向理想目標公司進行自我介紹
  • 提及關鍵字："cold email"、"reach out"、"intro email"、"outreach"、"contact hiring manager"

核心原則

開發信之所以失敗，是因為它們讀起來太像傳統求職信（Cover Letter）。核心目標是讓你聽起來像個聰明人，而且真的注意到這家公司的某個具體細節——而不是一個只顧著宣揚個人資歷的求職者。

這封信給人的感覺應該是： 同事在分享他讀到的有趣事物，而不是一場商業推銷。

範本結構

主旨

Quick intro — [First Name Last Name]

簡單明瞭。不標題黨。不附帶職務名稱。只有姓名。

開頭 —— 鉤子（2-3 句話）

從真正吸引求職者的點切入。必須針對這家公司量身打造——絕不能是泛泛的客套誇獎。

I just came across [Company] and ended up spending more time on it than I expected.
[One specific thing that caught their attention — a stat, a product decision, a market insight from the JD or website]

什麼是好的鉤子：

  • 引用公司公開資料中的具體數字或數據（例如「該市場目前只有 5% 完成線上化」）
  • 展現你理解該領域的產品決策（例如「你們是與核保人員一起從頭構建系統，而不僅僅做通路分銷」）
  • 來自職缺描述（JD）的技術洞察（例如「改善 LLM 使用的資料基礎設施，而不僅僅停留在模型層」）

什麼是不好的鉤子：

  • 套話：「我很喜歡你們正在打造的產品」
  • 模糊：「你們的使命引發了我的共鳴」
  • 逢迎奉承：「你們是這個領域中最令人興奮的公司之一」

所在地說明（若屬跨國求職）

如果求職者與公司不在同一國家，請提早且自然地說明：

Before you read further, I should mention I'm based in [country]. But if we both
feel there's a strong fit, I'd be open to figuring things out.

經驗落差（若適用）

如果職缺要求的年資高於求職者實際年資，簡短承認並轉移焦點：

The role mentions [X] years of experience — I'm at [Y], but the work I've
shipped is production-facing and I'd rather you judge that directly.

正文 —— 個人背景（3-5 句話）

目前職位 → 相關經歷 → 一到兩個專案。保持情境脈絡化敘述，切忌列成清單。

A bit about me: I currently work at [Company], [one line on what the company does],
where I [what they actually do there — not a job title]. Before that I worked
with teams in [region], mostly around [relevant domain].

On the side I've been building [Project] ([link if applicable]) — [one line: what
it is and one proof point like "got to paying clients" or "20K downloads"].

規則：

  • 將工作作為背景情境提及，而非用來證明自己有多厲害
  • 不要條列式寫成戰績報告（如「我將 X 提高了 Y%」）——讓專案成果自己說話
  • 如果目前的公司是遠端團隊且包含歐洲/國際成員，請明確指出
  • 專案提及最多一到兩個即可——只選最相關的

連結點（1-2 句話）

搭起個人背景與該職缺之間的橋樑。聽起來應該像是一個客觀觀察，而非空洞的主張。

I think you're looking for [what the role actually wants] — that's the kind of
work I've been doing, and honestly I feel like I'd be a strong fit.

或者更具體：

My stack maps naturally to yours — [specific tech overlap]. But more than the
stack, it's the [domain/problem] that I'm genuinely interested in.

作品集連結

More about me: [portfolio URL]

簡短一行，無需多做說明。

結尾

以自信且毫無壓迫感的語氣結尾。既不卑躬屈膝，也不像官僚套話。

I'm currently at [Company] and open to what's next. I think my experience lends
nicely to what you're building — so I think we'd both get something out of a
conversation.

[First Name]
[email]

應避免的句式：

  • "I look forward to hearing from you"
  • "Please find attached my resume"
  • "I would love the opportunity to..."
  • "I am excited to potentially join..."

撰寫前的研究準備

撰寫郵件之前，請先收集以下資訊：

  1. 公司切入點 —— 瀏覽官網、職缺描述（JD）或「關於我們」頁面。找出一個值得提及的具體細節。
  2. 收件人姓名 —— 透過 LinkedIn、團隊頁面或 AngelList 查找。「Hi [Name]」永遠勝過「Hi Hiring Manager」。
  3. 相關專案 —— 將求職者最相關的工作成果與該職缺的領域進行匹配。
  4. 技術堆疊重疊點 —— 僅在確實匹配時才提及。

如果使用者只提供職缺描述（JD）而未提供公司官網，在撰寫前請先詢問或主動獲取官網資訊。

長度規範

  • 理想長度： 200–300 字
  • 上限： 400 字
  • 下限： 150 字（切勿為了追求簡短而刪除實質內容）

如果每句話都有其存在的價值，稍微長一點也是可以的。刪除任何放諸四海皆準、套用到任何公司都通用的句式。

輸出格式

始終將最終撰寫好的郵件包裹在純程式碼區塊中，以便乾淨複製貼上：

Subject: Quick intro — [Name]

Hi [Name],

[Email body]

[First Name]
[email]

應避免的常見錯誤

過度自信：
❌ "I would be an exceptional addition to your team"
❌ "My background uniquely positions me for this role"
✅ "I feel like I'd be a strong fit" —— 表達個人看法，而非武斷誇大

過度謙卑：
❌ "I know I may not have all the experience you're looking for, but..."
❌ "I'm just reaching out on the off chance..."
✅ 直接承認經驗落差並迅速帶過 —— 無需道歉

篇幅過長：
❌ 用三大段文字列舉每一項歷史成就
✅ 一段講背景，一段講與職缺的連結

缺乏針對性（套話）：
❌ 開頭寫 "I am writing to express my interest in..."
✅ 開頭寫切中該公司具體特點的細節

未做研究：
❌ "I love your product and mission"
✅ "The 5% stat — only 5% of a $25B market online — doesn't feel like a feature gap, it feels like an entire industry that hasn't digitised"

範例郵件

Subject: Quick intro — Sarah Chen

Hi Marcus,

I just came across Meridian and spent more time on your site than I planned.
What pulled me in was the framing around your data infrastructure — you're not
just building an LLM layer on top of existing records, you're rethinking how
clinical data flows through the system in the first place. That's a harder
problem and a more interesting one.

I'm based in India, flagging that upfront. Happy to figure out the rest if
there's a real fit.

I currently work at Lune, a London-based climate intelligence company, where I
build event-driven data pipelines and agentic AI workflows for emissions
compliance — regulated, data-intensive systems where reliability isn't optional.
I also built Memos, a RAG system from scratch: hybrid retrieval, reranking,
ChromaDB, FastAPI. And InframetAI, an SDK for LLM cost and latency
observability that came out of seeing what breaks when the data layer isn't
designed around how models actually work.

I think you're looking for engineers who've actually built AI systems end to
end, not just wrapped APIs. That's the work I've been doing.

More about me: yourportfolio.com/about

I'm currently at Lune and open to what's next. I think my experience maps
closely to what you're building — so I think we'd both get something out of
a conversation.

Sarah
sarah@email.com