linkedin-engager-analytics Checked

Name: linkedin-engager-analytics
Author: sergebulaev

Pull the people who liked or commented on any LinkedIn post and segment them by ICP fit (peer / a…

📊 Data & Analytics skills By sergebulaev Version v1.0.0 Updated 2026-09-15

8.9Overall rating

Installation

🤖 Install via AI

Copy the prompt below and send it to your AI assistant (e.g. Claude Code) — it will follow the instructions and install automatically.

Install the "linkedin-engager-analytics" skill by following the instructions at https://skill123.me/install/linkedin-engager-analytics.

⌨️ Command line install

Run in your terminal — downloads and installs to ~/.claude/skills/.

curl -fsSL https://skill123.me/install/linkedin-engager-analytics.sh | bash

📦 Download ZIP

Download the zip and extract it into your skills directory (e.g. ~/.claude/skills/), then restart your session.

⬇ Download v1.0.0 · 4 KB

About this skill

Overview

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).

Source

Repo: https://github.com/sergebulaev/linkedin-skills
Path: skills/linkedin-engager-analytics

Score breakdown

Trigger

9.5

What/When plus explicit Not-For routing to thread-monitor; strong trigger phrases.

Structure

9.3

Lean 97-line SKILL.md with a single canonical output-spec reference.

Workflow

8.4

Numbered pipeline, no-token paste fallback, cross-post dedupe, cost accounting; output validated against spec example.

Content

9.0

Concrete parsing heuristics and tier definitions; mostly crisp.

Engineering

9.0

Correct frontmatter; relies on bundle-root lib/ which exists in repo context.

Security

8.5

Public data only, no credentials, untrusted-content rules inline; small deduction for bulk third-party profiling into outreach lists.

Fetches public likers/commenters via declared no-cookie Apify actors (no LinkedIn login), parses titles into ICP tiers, outputs roster plus outreach lists locally. Ethical hard rules limit scraping others audiences and DM cadence; data stays local, only Apify sees requests. Minor privacy friction from bulk profiling of third parties.