A Warm List From Post Engagement
Outcome: a contactable list of people who engaged with a post about the problem you solve, filtered before any enrichment, for roughly 70–80 credits per 40 people.
- Surface
- MCP server
- Level
- Beginner
- Uses
- linkedin_post_commenters · linkedin_post_engagers · linkedin_profile_enrich · find_work_email
- Credits
- ~70–80 per 40 people
- Prerequisite
- Lesson 02
Pick the post first
The post matters more than the prompt.
| Good | Bad |
|---|---|
| Describes the problem you solve | Announces a product |
| Posted by a competitor or an industry voice | Posted by a celebrity — engagement is generic |
| 30–300 engagements | Thousands, mostly noise |
| Under 30 days old | Months old |
| Substantive comments | ”Great post! 🔥” |
A competitor’s post about the pain point is the strongest target: everyone engaging has the problem and is already aware of a solution in your category.
Commenters before reactors
| Source | Intent | Volume | Personalization material |
|---|---|---|---|
| linkedin_post_commenters | Higher — they wrote something | Lower | Their own words |
| linkedin_post_engagers | Lower — a click | Higher | None |
Start with commenters. Add reactors only when you need volume, and message them differently — there is nothing to quote.
The prompt
Using the SyncGTM MCP server, build a warm list from this post:
https://www.linkedin.com/posts/example
Steps:
1. linkedin_post_commenters. Cap at 100.
2. Show me the list with name, headline and company BEFORE enriching anything.
Stop there. I will tell you who to enrich.The stop is deliberate. Filtering is free and enrichment is not, so you want to see the raw list and cut it yourself before a single paid call runs. A prompt that goes straight through to emails will enrich the competitor’s own employees, three consultants and a job seeker.
Then, after you have cut it:
Enrich only these 25: [names or profile URLs]
Steps:
1. linkedin_profile_enrich
2. find_work_email
3. verify_email
Return a table: name, title, company, email, status, plus their comment text.
Cap at 25.What to cut, for free
Peers and competitors
Check the company against your competitor list. They comment on these posts constantly.
The author’s colleagues
Everyone at the posting company. They are engaging with their own content.
Job seekers and students
Filter on seniority and headline.
Consultants and agencies
Unless they are a channel for you, in which case they are a different play.
A post with 100 commenters typically yields 25–35 real prospects. The cut is most of the work and it costs nothing.
Keep the comment text
The comment is your personalization. Store it alongside the contact.
“Your comment on Priya’s post about attribution — the point about multi-touch models breaking down at low volume is one I keep hearing from teams under 50 reps.”
That works because it quotes the substance. What does not work:
“I noticed you engaged with a post about attribution!”
The second tells them they were harvested from a list. The difference is entirely in whether you quote what they said.
Cost
| Step | Rows | Cost | Total |
|---|---|---|---|
| Pull commenters | — | — | ~2 |
| Free filtering | 100 → 25 | 0 | 0 |
| Profile enrich | 25 | ~1 | 25 |
| Find work email | 25 | ~1 | 25 |
| Verify | 25 | ~0.3 | 8 |
| ~60 |
Adding reactors for volume roughly doubles it, which is why commenters come first.
Do this now
Find a post
Problem-focused, recent, 30–300 engagements.
Pull commenters and stop
Read the raw list.
Cut it yourself
Peers, colleagues, job seekers, consultants.
Enrich only the survivors
With a cap.
Keep the comment text per row
Write from the comment
Quote the substance.
Compare reply rate to your cold baseline
If it is not several times higher, the filtering was too loose.
Check your work
- You saw the raw list before anything was enriched
- Peers, colleagues and job seekers are removed
- Comment text is stored per contact
- Every message quotes something the person wrote
- Cost matched the estimate
Where this breaks
Enriching everyone who touched the post is how a warm list becomes an expensive cold one. The engagement list contains the competitor’s team, the author’s colleagues, and people who react to everything — and enriching all of them costs four times what the real prospects cost while producing a worse list. The free filter is the lesson.
Further automation
Watch a set of pages rather than picking posts by hand. A weekly run over competitor and industry-voice pages, pulling engagers on anything above an engagement threshold, turns this into a standing warm-lead source. AI-Powered GTM lesson 17 runs the whole chain into a live campaign.
Next lesson
06 — The buying committee from a job post, sourcing accounts rather than people.
Reference for this lesson: linkedin_post_commenters, linkedin_post_engagers, find_work_email, Automated Outbound.