Design rigorous A/B tests with hypotheses, variants, metrics, and sample size calculations.
日本語の概要は準備中です。原文の説明を表示しています。
Write personalized cold outreach emails to hiring managers and founders — specific, human, not a pitch deck
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill when the user wants to:
Cold emails fail because they sound like cover letters. The goal is to sound like a smart person who genuinely noticed something specific about the company — not a candidate broadcasting their credentials.
The email should feel like: A colleague mentioning something interesting they read, not a sales pitch.
Quick intro — [First Name Last Name]
Simple. No clickbait. No role title. Just a name.
Start with what genuinely pulled the candidate in. Must be specific to this company — not a generic compliment.
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]
What makes a good hook:
What makes a bad hook:
If the candidate is in a different country from the company, disclose it early and casually:
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.
If the role lists more years than the candidate has, acknowledge it briefly and redirect:
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.
Current role → relevant past → one or two projects. Keep it contextual, not a list.
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"].
Rules:
Bridge between their background and the specific role. Should feel like an observation, not a claim.
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.
Or more specific:
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.
More about me: [portfolio URL]
One line. No elaboration.
End with low-pressure, confident energy. Not desperate, not corporate.
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]
Avoid:
Before writing the email, gather:
If the user provides a JD but no company website, ask for it or fetch it before writing.
Longer is fine if every sentence earns its place. Cut anything that could apply to any company.
Always wrap the final email in a plain code block for clean copy-paste:
Subject: Quick intro — [Name]
Hi [Name],
[Email body]
[First Name]
[email]
Too confident: ❌ "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" — states a belief, doesn't claim certainty
Too humble: ❌ "I know I may not have all the experience you're looking for, but..." ❌ "I'm just reaching out on the off chance..." ✅ Acknowledge gaps directly and move on — don't apologize
Too long: ❌ Three paragraphs listing every achievement ✅ One paragraph on background, one on the connection
Too generic: ❌ Opening with "I am writing to express my interest in..." ✅ Opening with what specifically pulled them to this company
No research: ❌ "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"
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
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Design rigorous A/B tests with hypotheses, variants, metrics, and sample size calculations.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
日本語の概要は準備中です。原文の説明を表示しています。
Format CVs for academic positions with publications, grants, and teaching
日本語の概要は準備中です。原文の説明を表示しています。
Conduct a comprehensive accessibility audit against WCAG guidelines with severity ratings and remediation steps.
日本語の概要は準備中です。原文の説明を表示しています。
Create accessibility testing plans covering assistive technologies and WCAG criteria.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative testing,' 'ad performance optimization,' 'write me some ads,' 'Facebook ad copy,' 'Google ad headlines,' 'LinkedIn ad text,' or 'I need more ad variations.' Use this whenever someone needs to produce ad copy at scale or iterate on existing ads. For campaign strategy and targeting, see ads. For landing page copy, see copywriting.
日本語の概要は準備中です。原文の説明を表示しています。