analyze-performance

Analyzes LinkedIn CSV exports to identify engagement trends and underperforming areas.

4|1|Updated Jun 16, 2026

Search Tags:#analytics #engagement #linkedin #pattern-analysis #quarterly-review #content-analytics

Ready to run

npx skills add https://github.com/warpirate/linkedin-maxxing --skill analyze-performance-warpirate

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyze-performance
Source: https://github.com/warpirate/linkedin-maxxing/tree/main/plugins/linkedin-maxxing/skills/analyze-performance
Command: npx skills add https://github.com/warpirate/linkedin-maxxing --skill analyze-performance-warpirate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reading and interpreting LinkedIn analytics data is time-consuming and error-prone; this skill provides a rigorous analysis workflow to extract actionable patterns from analytics CSVs so you can improve content strategy.

Core Features & Use Cases

Reads and parses LinkedIn analytics CSV exports (per-post metrics, impressions, engagement, comments, shares) and groups posts by pillars, format, hooks, length, and closing patterns.
Identifies patterns with statistical signals (median engagement, dwell-time proxies, audience signals) and surfaces actionable recommendations for the user.
Use cases: quarterly content review, diagnosing engagement drops, steering pillar-based content planning and optimization.

Quick Start

Upload your LinkedIn analytics CSV and run the analyze-performance skill to generate a quarterly performance review.

Frequently Asked Questions about analyze-performance

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

FAQPage Schema

How do I analyze LinkedIn analytics CSV data to identify engagement patterns?▼

To analyze LinkedIn analytics CSV data, you upload the export to parse per-post metrics like impressions and engagement, grouping content by pillars and formats to surface actionable patterns and statistical signals for your strategy.

What is the best way to diagnose a drop in LinkedIn engagement using content analytics?▼

Diagnosing a LinkedIn engagement drop uses content analytics to compare statistical signals like median engagement and dwell-time proxies across recent posts, identifying which content pillars or formats caused the performance decline.

Can I use my standard LinkedIn analytics export for a quarterly content review?▼

A standard LinkedIn analytics CSV export provides the per-post impressions, engagement, comments, and shares required to group posts by hooks and length, generating a structured quarterly performance review with actionable insights.

Does this approach work for pillar-based content planning on LinkedIn?▼

Pillar-based content planning is supported by grouping LinkedIn analytics CSV posts by content pillars, then evaluating statistical signals like median engagement to steer and optimize your future content strategy.

What LinkedIn metrics are needed to extract actionable content patterns?▼

Extracting actionable content patterns requires LinkedIn analytics CSV metrics including per-post impressions, engagement, comments, and shares to calculate median engagement and dwell-time proxies for your content review.