| name | linkedin-engager-analytics |
| description | 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. Triggers on "who liked my post", "who engaged", "engagers report", "audience analytics". Not for tracking author replies to your comments (use linkedin-thread-monitor). |
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).
Inbound-quality signals
High-quality = follow up: founder/operator title, company in ICP, active posting history, >10 mutual 2nd-degree connections, prior thoughtful comments on user's posts.
Low-quality = skip: generic praise, template language ("I'd love to hop on a quick call"), sales/agency profile with no operator history, same comment copy-pasted across many creators.
Hard rules
Global voice rules: see root SKILL.md §Voice rules. Additional skill-specific rules:
- Don't run engager analytics on posts you didn't write or aren't tracking with permission. The data is technically public but high-volume scraping of someone else's audience reads as creepy.
- Don't DM a prospect on the same day they engaged with your post. Wait 24-72h to avoid the "thirsty" pattern.
- One DM opener per engager, not three. If the first didn't land in 5 business days, drop it.
Cost accounting
| Action | Apify call | Cost (free tier) |
|---|
| Engager analytics on one post (50 engagers) | fetch_post_engagers(max_items=50) | $0.25 |
| Engager analytics on one post (200 engagers) | fetch_post_engagers(max_items=200) | $1.00 |
A weekly engager-analytics run on 1-2 posts stays well under the $5 free monthly credit.
Files
SKILL.md — this filereferences/output-spec.md — engager roster shape, tier breakdown, action lists, sample run
Related skills
linkedin-thread-monitor — track author replies to YOUR comments (different surface)linkedin-comment-drafter — draft outreach comments to engagers from this reportlinkedin-reply-handler — draft DM follow-ups
