cold-email-writer

Cold Email Writer

使用场景 (When to Use This Skill)

当用户想要执行以下操作时使用此 Skill：

• 给招聘经理（Hiring Manager）、创始人或招聘人员撰写 Cold Email（主动套瓷/自荐冷邮件）
• 在没有内部推荐的情况下，针对心仪岗位进行主动沟通
• 向感兴趣的目标公司做自我介绍
• 提及关键词："cold email"、"reach out"、"intro email"、"outreach"、"contact hiring manager"、"冷邮件"、"套瓷信"

核心原则 (Core Principle)

大部分 Cold Email 失败的原因在于写得太像求职信（Cover Letter）了。我们的目标是让自己听起来像是一个聪明、专业且真正关注到了对方公司某项具体业务的人，而不是一个到处海投简历的候选人。

邮件给人的感觉应该是： 像同行之间分享自己看到的有趣细节，而不是一份商业推销 PPT。

模板结构 (The Template Structure)

邮件主题 (Subject)

Quick intro — [First Name Last Name]

简单直接。拒绝标题党。不要写岗位名称。只写姓名即可。

开篇 — 钩子/切入点 (2-3 句话)

直奔主题，写出真正吸引候选人的具体点。必须针对这家公司定制，绝不能用泛泛而谈的客套话。

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]

好钩子（Hook）的特征：

• 来自公司公开材料的具体数据或指标（如：“市场中仅 5% 实现了线上化”）
• 展现你懂行业的产品决策（如：“你们是在和核保人一起从零构建产品，而不只是做分发层”）
• 来自 JD 的技术洞察（如：“你们重点改善的是大模型应用的数据基础设施，而不仅仅是模型本身”）

坏钩子（Hook）的特征：

• 泛泛而谈：“我非常喜欢你们正在做的事”
• 模糊不清：“你们的使命深深打动了我”
• 阿谀奉承：“你们是这个赛道里最让人兴奋的公司之一”

所在地说明（若为跨国/远程）

如果候选人与公司不在同一个国家/地区，尽早且自然地提及：

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.

工作年限差距说明（如适用）

如果岗位要求的年限高于候选人的实际年限，简要承认并巧妙引导到实际产出上：

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 — 自我介绍与背景 (3-5 句话)

当前职位 → 相关经历 → 1-2 个代表项目。保持语境自然，避免简历式的履历堆砌。

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"].

硬性规则：

• 把工作经历作为背景支撑，而不是吹嘘战绩
• 别罗列虚浮的数据战报（如“我把 X 提升了 Y%”）——让项目本身说话
• 如果现公司是支持欧洲/国际团队的远程办公模式，一定要明确点出
• 项目最多提 1-2 个——只放最相关的

契合点连接 (1-2 句话)

将个人背景与具体岗位连接起来。听起来要像客观观察，而不是主观空话。

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.

或者更具体：

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.

作品集链接 (Portfolio Link)

More about me: [portfolio URL]

单行展示，无需展开说明。

结尾 (Closing)

以从容、自信、低压力的语气收尾。不要显得委曲求全，也不要一股官腔。

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]

避坑指南（切忌使用）：

• "I look forward to hearing from you"（静候您的回复）
• "Please find attached my resume"（请查收附件履历）
• "I would love the opportunity to..."（希望能有幸获得...的机会）
• "I am excited to potentially join..."（非常兴奋能有机会加入...）

撰写前调研 (Research Before Writing)

写信前，先收集以下信息：

1. 公司切入点 — 浏览其官网、JD 或 About 页面，找到一个值得提及的具体细节。
2. 接收人姓名 — 查找 LinkedIn、团队介绍页或 AngelList。“Hi [姓名]” 永远比 “Hi Hiring Manager” 好得多。
3. 相关项目 — 匹配候选人与该领域最相关的代表作。
4. 技术栈重合度 — 只有在真正契合时才提及。

如果用户只提供了 JD 但没提供官网，撰写前先询问或获取官网信息。

篇幅建议 (Length Guidelines)

• 理想字数： 200–300 词
• 上限： 400 词
• 下限： 150 词（不要为了精简而砍掉实质内容）

只要每句话都有价值，稍长一点也没关系。删掉任何放到哪家公司都能套用的废话。

输出格式 (Output Format)

生成的邮件最终成果务必包裹在纯代码块中，方便用户一键复制：

Subject: Quick intro — [Name]

Hi [Name],

[Email body]

[First Name]
[email]

常见误区与避坑 (Common Mistakes to Avoid)

过于自信/说大话：
❌ "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" — 表达信念，而非狂妄断言

过于卑微/缺乏自信：
❌ "I know I may not have all the experience you're looking for, but..."
❌ "I'm just reaching out on the off chance..."
✅ 直接承认差距并快速过渡 — 不必为此道歉

篇幅过长：
❌ 用三大段罗列个人所有成就
✅ 一段讲背景，一段讲契合点

泛泛而谈：
❌ 开头就是 "I am writing to express my interest in..."
✅ 开头直接写吸引自己的具体细节

不做功课：
❌ "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"

示例邮件 (Example Email)

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