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Linkedin engager analytics

Skill sergebulaev/linkedin-skills/skills/linkedin-engager-analytics

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).From its SKILL.md

Install
npx -y skills add sergebulaev/linkedin-skills --skill linkedin-engager-analytics

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • reads credentialsReads from 1 credential source: `APIFY_TOKEN`.

SKILL.md

4.5 KB, 997 tokens by cl100k_base, as published. Nobody here has run it

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

  1. 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.
  2. 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).
  3. 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)
  4. 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
  5. 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 <post angle>. Curious. Are you currently <ICP problem>?")
  6. 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

ActionApify callCost (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 file
  • references/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 report
  • linkedin-reply-handler — draft DM follow-ups

What ships with it: 1 file

1.4 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most analytics metrics skills give in 997 tokens

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Said here and by no other author read

  • Fetch likers and commenters for each post URL via Apify
  • Use user-pasted engager list when APIFY_TOKEN is missing
  • Parse subtitles into title, company, and seniority bucket
  • Score ICP fit using the user's supplied rules
  • Look up company size via CRM, else mark Unknown
  • Assign each engager peer, aspirational, prospect, or other tier

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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