Linkedin Engager Analytics
Skill ActivePull 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).
To help users identify valuable engagement on their LinkedIn posts and generate targeted outreach lists based on Ideal Customer Profile fit.
Features
- Pull likers and commenters from LinkedIn posts
- Segment engagers by ICP fit (peer, aspirational, prospect)
- Produce engager roster, tier breakdown, and action lists
- Utilize Apify for data fetching (optional)
- Fallback to manual data pasting if API token is missing
Use Cases
- Identify valuable engagement on recently published LinkedIn posts.
- Segment LinkedIn commenters by ICP fit before a sales campaign.
- Generate targeted outreach lists from LinkedIn post engagers.
Non-Goals
- Tracking author replies to your comments.
- Performing automated outreach or posting without user approval.
Documentation
- info:Configuration & parameter referenceThe SKILL.md mentions an optional ICP definition and max engagers, but these parameters lack explicit documentation regarding their format or default values.
Maintenance
- warning:Dependency ManagementThe skill depends on Apify and Publora, but there are no explicit measures mentioned for updating or managing these dependencies for vulnerabilities.
Compliance
- info:GDPRThe skill operates on public LinkedIn post data, but personal data from profiles might be submitted to the LLM for ICP scoring without explicit sanitization beyond title/company extraction.
Execution
- warning:Pinned dependenciesWhile Python scripts are included, there is no explicit mention of pinned dependencies or lockfiles for them, and shebangs are not consistently present.
Practical Utility
- info:Usage examplesWhile the SKILL.md outlines the output spec and the workflow, concrete, ready-to-run examples demonstrating input, invocation, and output are missing.
Portability
- warning:Stack assumptionsWhile Python is implied, the SKILL.md does not explicitly declare the runtime surface (e.g., Python version) or side effects for bundled scripts.
Installation
First, add the marketplace
/plugin marketplace add sergebulaev/linkedin-skills/plugin install linkedin-skills@linkedin-skillsQuality Score
Trust Signals
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