Pull the people who liked or commented on any LinkedIn post and segment them by ICP fit (peer / aspirational / prospect / other). Produces an engager roster, tier breakdown, and outbound action lists (follow back, comment-drop, DM-able with one-line openers). Powered by Apify, no LinkedIn login.
SkillScore86Maintained
Automatic
◆Sales & GTM pack
Last verified 8/19/2026
What it changes
Passed 19 of 22 test cases with the skill, and 7 of 22 without it.
Without skill32%
With skill86%
Checks it passed that the model alone missed
Assigns the person to the Prospect tier.
Labels the non-matching engager as Other.
Assigns the person to the Aspirational tier.
and 13 more in the full report
12 fixed7 passed either way3 still failing0 broke
Pass-rate lift
+55 pp
Tokens
+27%
Agent turns
0%
Response time
-46%
Model gemini-3.6-flashMeasured 8/19/2026v119 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 12
Prompt
When creating action lists from the audience roster, which category of engagers who post actively is placed on the 'Follow back' action list?
What was checked, without and with the skill
Checked
Without skill
With skill
Designates active posters in the Peer tier for the follow back list.
Missed
Passed
case-13 · gemini-3.6-flash · 8/19/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md74 lines2 files
LinkedIn Engager Analytics
Pull every liker and commenter on a LinkedIn post and bucket them by ICP fit. Outputs a roster + action list you can feed into your DM or outreach queue.
Depends on APIFY_TOKEN. Without it, falls back to user-paste of the engager list.
When to use
After publishing a post: "Who actually engaged? Are they ICP?"
Before a campaign: "Pull the last 5 viral posts in my niche, group their commenters by company size"
Reviewing competitor engagement: which prospects show up across multiple authors
Input
One or more LinkedIn post URLs
Optional: ICP definition (target titles, company size, industry)
Optional: max engagers per post (default 100)
Output
Output format (engager roster, tier breakdown, action lists): see references/output-spec.md. Headline: a table of engagers labelled by ICP tier and a per-tier action list.
Steps
Fetch engagers. Call lib.ApifyClient.fetch_post_engagers(post_url=<url>, max_items=100). Returns a list of dicts with type ("commenters" | "likers"), name, subtitle (job title + company), url_profile, content (comment text if commenter), datetime. Cost is roughly $0.005 per engager-record.
Parse subtitle into structured fields. The subtitle typically reads "Director at Acme Corp" or "Founder & CEO at SaaS Inc". Extract: title, company, seniority bucket (IC / Manager / Director / VP / C-suite / Founder).
Score ICP fit. Use the user's supplied ICP rules:
Title match (regex or keyword list)
Company size proxy (look up via the user's CRM if integrated, else mark Unknown)
Industry match (parse company name + subtitle keywords)
Assign tier.
Peer: founder / operator at similar-stage company in same niche
Aspirational: senior leader (Director+) at larger company in adjacent niche
Prospect: title in ICP target list AND company in ICP target list
Other: no match
Produce action lists.
Follow back: peers with active posting (heuristic: appears as author in fetch_user_recent_comments of any team member)
Comment-drop targets: aspirational tier
DM-able: prospect tier, with a one-line DM opener referencing the specific post they engaged with ("Saw you reacted to . Curious. Are you currently ?")
Optional cross-post analysis. If the user supplied multiple post URLs, deduplicate engagers and flag people who engaged with 2+ posts (highest-intent signal).