Library/Prospecting & Pipeline Creation/LinkedIn & Social Outreach/Write LinkedIn Comment to Warm Up a Prospect Before Outreach
TestedProspecting & Pipeline CreationQuick Win (under 10 min)
About this prompt
This prompt helps SDRs, BDRs, and AEs write a LinkedIn comment on a prospect's post that is substantive enough to get noticed without being sycophantic or salesy. It positions the rep as a knowledgeable peer and ends with a question that invites a response. Use it when you want to create a warm touchpoint before sending a cold DM to a high-value prospect.Commenting 'Great post!' on a prospect's LinkedIn content is worse than saying nothing — it's noise that actively signals you have nothing to add. This prompt generates a comment on a post by [PROSPECT NAME] about [POST TOPIC] that contributes a real insight or experience, positions you as a peer in [INDUSTRY/SPACE], and closes with a question that makes it easy for the prospect to reply. The comment is capped at 100 words to keep it readable in a feed context. Use this as a warm-up step before direct outreach — after a prospect has seen your name and engaged with your comment, a follow-up DM or email converts at a meaningfully higher rate than cold outreach to a stranger. For best results, engage on the post within the first few hours of it being published, when the algorithm is still surfacing it and the prospect is actively monitoring engagement.The prompt
I want to comment on a LinkedIn post by [PROSPECT NAME] about [POST TOPIC] as a way to start a relationship before cold outreaching. Write a comment that:
- Adds a genuine insight or experience to the
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Variables to replace
PROSPECT NAME|POST TOPIC|INDUSTRY/SPACE
How to run it
- Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot — it's tuned to run in any of them.
- Replace the variables — swap each bracketed token for your deal's specifics. The variables list above explains every field.
- Make it yours — run it, keep what's strong, and tighten your inputs on the next pass. The prompt improves with your context.
What good output looks like
Make it yours
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