Social Media Analyzer

This handles the math-heavy side of social media analytics: engagement rates, ROI calculations, and platform benchmarking across Instagram, Facebook, Twitter, LinkedIn, and TikTok. Feed it post metrics (likes, comments, shares, reach) and optional ad spend, and it returns campaign-level performance data compared against industry benchmarks. The included Python scripts do the number crunching, while the reference docs provide context on what constitutes good performance per platform. Honestly most useful when you're reporting to stakeholders or trying to justify budget decisions, less so for day-to-day content optimization. The engagement value estimates ($0.50 per like, $2 per comment) are a bit arbitrary but give you a framework for ROI conversations.

skills.sh installs: 201
GitHub stars: 1.1K
last updated: Aug 21, 2026

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Install

$ npx -y skills add borghei/claude-skills --skill social-media-analyzer --agent claude-code

Installs into .claude/skills of the current project.

Files

Social Media Analyzer

Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.

Analysis Workflow
Engagement Metrics
ROI Calculation
Platform Benchmarks
Tools
Examples

Clarify First

Before analyzing, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

Platform: Instagram, Facebook, Twitter/X, LinkedIn, or TikTok — selects the correct benchmark set for comparison.
Post/campaign data: likes, comments, shares, saves, reach per post — required; reach must be unique users, not impressions.
Ad spend: total spend for the period — determines whether ROI/CPE/ROAS is calculated.
Analysis goal: full audit, top-performer patterns, ROI, or competitor comparison — focuses the output artifact.

SKILL.md on GitHub