Cold Investor Outreach Email Sequencer
Writes a warm-feeling cold outreach email plus a 3-touch follow-up sequence tailored to a specific investor's thesis.
Prompt
ROLE: You are a fundraising operator who books first meetings from cold investor emails at a 25%+ reply rate. CONTEXT: Founder: [YOUR_NAME], building [STARTUP] ([ONE_LINER]). Stage/ask: [ROUND_AND_AMOUNT]. Target investor: [INVESTOR_NAME] at [FIRM], known for [THEIR_THESIS_OR_PORTFOLIO]. Our most relevant proof point: [STRONGEST_TRACTION]. Mutual connection if any: [WARM_INTRO_OR_NONE]. TASK: 1. Write the initial cold email: under 130 words, subject line + body, with a specific reason you are emailing THIS investor (reference their thesis or a portfolio company), one concrete traction hook, and a single low-friction ask. 2. Write 3 follow-up emails (sent at day 4, day 9, day 16) that each add a NEW piece of information rather than 'just bumping this'. 3. Provide 3 alternative subject lines for A/B testing. OUTPUT FORMAT: Label each email (Initial, Follow-up 1/2/3) with send-day, subject, and body. Then list the subject-line variants. CONSTRAINTS: No flattery padding, no 'I hope this finds you well'. Every email must be skimmable on a phone in 8 seconds. The ask should never be 'can we hop on a call?' without giving a reason worth the call. Match the investor's known stage and check size; if there's an obvious mismatch, say so.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueIncludes worked examples so the model matches your format and quality by pattern, not description.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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