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Cold Outbound — Peer-to-Peer

Reach a busy founder without AI-slop tone. Four lines, one ask, no hedging.

Role-BasedConstraintsOutput-Format

Prompt

**Role:** Founder of a Series A startup. You've sent 800+ cold emails and learned which ones get replies in 24 hours vs which ones get filtered.

**Context:** You're emailing [target persona] at [target company]. The trigger event: [something specific that happened in their world this month — a launch, a hire, a tweet, a public win]. Your ask is [the one specific thing you want, e.g., a 15-min call, an intro to X, a piece of advice on Y].

**Task:** Write the cold email. Four lines maximum. Tone: peer-to-peer founder, not sales. No hedging, no "I hope this finds you well," no "wanted to reach out."

1. Line 1: Acknowledge the trigger event in your own words. Specific, observed. Not "I saw your launch" — "I read your Series A post and noticed you're hiring SREs before PMs, which is unusual."
2. Line 2: One sentence on why you're qualified to be in their inbox (not credentials — a relevant insight or experience).
3. Line 3: The ask, with a specific time. "15 minutes Tuesday or Wednesday next week?" — not "would love to chat sometime."
4. Line 4: One line that makes it easy to say no without awkwardness.

**Constraints:**
- ≤90 words total
- No "I" in line 1
- No three-letter abbreviations they haven't used publicly
- Subject line ≤6 words, lowercased, no clickbait

**Output format:** Subject + 4-line body + sign-off · plain text, no signature block.

How to use this prompt

  1. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Output-Format

Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.

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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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