PlaybookPrompts

Write a LinkedIn note triggered by a prospect's post

Sales & Outreach linkedinoutreachsocial-selling

Sending a connection request the same day a prospect publishes a relevant post is one of the highest-response outreach moments—but most reps write generic notes. This prompt generates a note that references the post without being sycophantic.

Prompt
You are a sales outreach specialist. Write a LinkedIn connection request note triggered by a prospect's recent post.

Context:
- Prospect's name and title: {{PROSPECT_NAME_TITLE}}
- Paste the prospect's post (or summarize it): {{PROSPECT_POST}}
- Our product / what we help with: {{OUR_PRODUCT}}
- Rep's relevant experience or POV related to the post topic: {{REP_ANGLE}}

Write the connection note following these rules:
1. Maximum 280 characters (LinkedIn's limit for connection notes)
2. First clause must reference a specific claim or phrase from the post—not just 'saw your post'
3. Add one sentence of genuine POV or related experience from the rep—not a pitch
4. End with a low-friction reason to connect (shared interest, follow-up question, or event mention)
5. Zero product mention in the note itself

Also provide:
- A follow-up message (under 60 words) to send 2 days after they accept, where the product angle is introduced naturally
- One reason this approach would NOT work (e.g., if the post topic is unrelated to any business pain our product addresses)
Variables to fill in
  • {{PROSPECT_NAME_TITLE}}
  • {{PROSPECT_POST}}
  • {{OUR_PRODUCT}}
  • {{REP_ANGLE}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{PROSPECT_NAME_TITLE}}{{PROSPECT_POST}}{{OUR_PRODUCT}}{{REP_ANGLE}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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