linkedin-thread-monitor

Monitors LinkedIn comments and flags optimal reply windows for threads.

520|82|Updated Apr 14, 2026

Search Tags:#monitoring #engagement #linkedin #response drafting #thread analysis

Ready to run

npx skills add https://github.com/sergebulaev/linkedin-skills --skill linkedin-thread-monitor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: linkedin-thread-monitor
Source: https://github.com/sergebulaev/linkedin-skills/tree/main/.codex-marketplace/linkedin-skills/skills/linkedin-thread-monitor
Command: npx skills add https://github.com/sergebulaev/linkedin-skills --skill linkedin-thread-monitor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires apify_client, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users track the performance of their LinkedIn comments and identify optimal times to respond to author replies, thus enhancing engagement and follow-up efforts.

Core Features & Use Cases

Thread Performance Monitoring: Flag the 6-24h warm-reply window where thread momentum peaks.
Thread Classification: Classify threads as hot/warm/cool/dormant.
Response Drafting: Route warm threads to follow-up drafting for effective engagement.
Inbound-Quality Signals: Detect high-quality replies for tailored follow-ups.
DM Routing: Route dormant threads to direct messages for personalized engagement.
Use Case: A user wants to monitor and engage with comments that have earned author replies on LinkedIn to maximize visibility and response rates.

Quick Start

Use the linkedin-thread-monitor skill to monitor the comments made on my LinkedIn profile in the last 72 hours.

Frequently Asked Questions about linkedin-thread-monitor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I monitor LinkedIn comments to identify the best time to reply?▼

To monitor LinkedIn comments effectively, you need a tool that flags the 6-24h warm-reply window where thread momentum peaks. This Skill tracks author replies within the last 72 hours and classifies thread temperature to highlight optimal engagement opportunities.

How does thread classification help increase LinkedIn engagement?▼

Thread classification increases LinkedIn engagement by categorizing conversations as hot, warm, cool, or dormant. This routing logic directs warm threads to follow-up drafting and dormant threads to direct messages, ensuring tailored responses that maximize visibility.

Do I need an APIFY_TOKEN to track LinkedIn thread performance?▼

Yes, you need an APIFY_TOKEN to access the Apify client dependency for data fetching. This token is required for the Python scripts to retrieve your LinkedIn comments and analyze thread performance over the last 72 hours.

Can I route dormant LinkedIn threads to direct messages automatically?▼

You can route dormant LinkedIn threads to direct messages for personalized engagement. The Skill identifies dormant conversations and suggests DM routing, alongside routing warm threads to follow-up drafting for effective response strategies.

What is the best way to draft follow-up responses for warm LinkedIn threads?▼

The best way to draft follow-up responses is to use inbound-quality signals that detect high-quality author replies. The Skill flags the 6-24h warm-reply window and routes these warm threads directly to follow-up drafting for tailored engagement.