sourcing-outreach（采购外展）

Sourcing Outreach

When to Use

Activate when the user asks to write cold outreach to potential candidates (LinkedIn InMails, cold emails), craft referral request messages, build a multi-touch follow-up sequence, or improve response rates on existing recruiting outreach. Also activate when the user is sourcing for a specific role and needs help personalizing messages at scale.

Context Required

• From startup-context: Company name, one-line mission, stage/funding, notable investors or customers, recent milestones, and team size.
• From user: Role being hired for, the candidate's name and background (LinkedIn profile, blog posts, talks, open-source work), what specifically drew the user to this candidate, and the communication channel (LinkedIn, email, Twitter DM).

Workflow

1. Research the candidate — Review the candidate details provided by the user. Identify 1-2 specific, genuine connection points: a project they shipped, a talk they gave, an open-source contribution, a blog post, or a career pattern that signals fit.
2. Choose the outreach template — Select from: cold LinkedIn InMail, cold email, warm intro request, referral ask, or follow-up. Each has different length and tone constraints.
3. Draft the message — Write a short, personalized message using the PRC framework (see below). Keep LinkedIn InMails under 300 characters for the preview. Keep cold emails under 150 words.
4. Add a clear, low-friction CTA — The ask should be small: a 15-minute call, a reply with interest level, or permission to send more details. Never ask for a resume or formal application in cold outreach.
5. Build the follow-up sequence — Draft 2-3 follow-ups spaced 4-7 days apart. Each follow-up adds new information (a company milestone, a team blog post, a relevant data point) rather than just "bumping" the thread.
6. Review for tone — Ensure the message sounds human, not templated. Check that personalization is specific enough that it could only apply to this candidate.

Output Format

• A primary outreach message (ready to send)
• 2-3 follow-up messages with suggested send timing
• Notes on personalization elements used and why they were chosen

Frameworks & Best Practices

The PRC Framework

Every outreach message should contain three elements in roughly this order:

• Personalization (P): A specific, genuine observation about the candidate's work that shows you did your homework. Not "I saw your impressive profile" — something like "Your talk on event sourcing at StrangeLoop changed how I think about our own data pipeline."
• Relevance (R): Why this role connects to their career trajectory. Bridge from what they've done to what they'd do at your company.
• Call-to-action (C): A single, low-commitment ask. "Would you be open to a 15-minute call this week?" is better than "Apply at our careers page."

Channel-Specific Guidelines

LinkedIn InMail:

• Subject line matters more than body — keep it intriguing and specific (e.g., "Your Kafka work + our real-time pipeline" not "Exciting opportunity").
• InMail preview shows ~300 characters. Front-load the personalization.
• Do not connect-and-pitch simultaneously. Either send an InMail or send a connection request with a note — not both at once.

Cold Email:

• Subject: Short, specific, no clickbait. "Quick question about [their project]" or "[Mutual connection] suggested I reach out."
• Keep body under 150 words. Three short paragraphs max.
• Plain text outperforms HTML templates. No logos, no signatures with 10 links.
• Send from a real person's email (founder@, not recruiting@).

Warm Intro / Referral Request:

• Make it easy for the connector: provide a forwardable blurb they can send with zero editing.
• Include context on why you think the candidate is a fit so the connector can vouch meaningfully.
• Always give the connector an out: "No pressure at all if this doesn't feel right."

Personalization Research Checklist

Before writing, look for:

• Recent talks, podcast appearances, or conference presentations
• Blog posts or technical writing
• Open-source contributions (GitHub, GitLab)
• Career trajectory patterns (e.g., "you've gone deep on infrastructure at two companies in a row")
• Mutual connections or shared communities
• Company or product they built that you genuinely admire

Follow-Up Sequence Design

• Follow-up 1 (Day 4-5): Add a new piece of information — a recent company milestone, a blog post from the team, or a specific project they'd work on. Keep it to 2-3 sentences.
• Follow-up 2 (Day 9-11): Try a different angle. If the first message was about the role, this one could be about the team or a technical challenge. Ask a question rather than making a pitch.
• Follow-up 3 (Day 16-20): A graceful close. "I don't want to crowd your inbox — just wanted to leave the door open. If timing is ever better, I'd love to chat." This gets surprisingly high response rates.
• Never send more than 3 follow-ups to a candidate who hasn't responded.

Anti-Patterns to Avoid

• Spray and pray: Sending identical messages to 200 people. Response rates plummet below 5%. Batch personalization into tiers instead.
• The humble brag opener: "We just raised $50M from Sequoia" as your first sentence. Lead with the candidate, not yourself.
• Fake personalization: "I was really impressed by your background" is worse than no personalization because it signals a template.
• Overselling: Don't describe the role as "once in a lifetime." Be honest and specific about what makes it compelling.
• Asking for too much: "Please send your resume and three references" in a cold message guarantees no reply.

Related Skills

• job-description — The JD provides the source material for what makes the role compelling in outreach.
• interview-kit — Mention the candidate-friendly interview process in outreach to reduce friction.

Examples

Prompt: "Write a cold LinkedIn InMail for a senior ML engineer. She gave a talk at NeurIPS on efficient fine-tuning and currently works at Stripe."

Good output snippet:

Subject: Your NeurIPS talk on efficient fine-tuning

Hi [Name],

Your NeurIPS presentation on LoRA variants for production models stuck
with me — especially the bit about keeping inference costs flat while
scaling model complexity. We're solving a similar problem at [Company]:
building real-time ML models for [use case] and need someone who thinks
about efficiency as a first-class constraint.

We're 18 people, Series A, and this would be our second ML hire. You'd
own the model architecture end to end.

Would you be open to a 15-minute call to see if there's a fit?

— [Your name]

Prompt: "Write a referral request email I can send to a mutual connection."

Good output snippet:

Subject: Quick ask — know anyone strong in product design?

Hi [Connector],

We're hiring our first product designer at [Company] and I thought of
you since you work with strong designers regularly. Here's what we're
looking for: [1-2 sentences on the role].

If anyone comes to mind, I'd be grateful for an intro. Here's a
forwardable blurb:

---
"[Your name] is the CEO of [Company] ([one-liner]). They're hiring a
founding product designer to own the end-to-end user experience. The
team is 14 people, Series A, remote-first. Here's the JD: [link]"
---

No pressure at all — and thanks either way.

[Your name]