social-media-analyzer_skill

This skill analyzes social media campaign performance across platforms to deliver data-driven insights, ROI, and audience trends for optimized marketing
  • Python

1.4k

GitHub Stars

6

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill alirezarezvani/claude-skills --skill social-media-analyzer

  • analyze_performance.py7.2 KB
  • calculate_metrics.py5.4 KB
  • expected_output.json1.5 KB
  • HOW_TO_USE.md1.8 KB
  • sample_input.json867 B
  • SKILL.md3.0 KB

Overview

This skill analyzes social media campaign performance across platforms to deliver actionable, data-driven marketing insights. It combines engagement metrics, ROI calculations, audience insights, and trend detection to help optimize creative, timing, and budget decisions. The outputs include dashboards, performance summaries, and concrete recommendations ready for client reporting.

How this skill works

The analyzer accepts structured campaign data (JSON, CSV) or key metric summaries and computes core KPIs such as engagement rate, CTR, CPC, cost per engagement, and ROAS. It cross-references platform-specific benchmarks, detects high- and low-performing content types and posting times, and generates audience breakdowns by demographics and behavior. Results are packaged as performance dashboards, ROI breakdowns, prioritized recommendations, and exportable visual reports (Excel/PDF).

When to use it

  • After running a paid or organic campaign to evaluate results
  • When comparing cross-platform performance for the same date range
  • To calculate ad efficiency and return on spend for client billing or optimization
  • To discover audience segments and peak engagement windows
  • When preparing monthly or quarterly performance reports for stakeholders

Best practices

  • Provide complete platform metrics (likes, comments, shares, saves, clicks, impressions) to maintain accuracy
  • Compare campaigns using the same time periods and normalized metrics
  • Separate organic and paid data when calculating reach and ROI
  • Use platform-specific benchmarks when interpreting engagement rates
  • Include context like seasonality, promotions, or product launches in reports
  • Validate cost and attribution data before computing ROAS

Example use cases

  • Analyze a month-long Instagram ad campaign to calculate ROAS and identify best-performing creatives
  • Compare Facebook, LinkedIn, and Twitter performance to recommend budget reallocation
  • Identify audience demographics and peak posting times from TikTok and Instagram exports
  • Generate a client-ready PDF report showing engagement trends, top posts, and optimization suggestions
  • Compute cost per acquisition and cost per engagement for a multi-platform product launch

FAQ

It accepts structured JSON, CSV exports from platforms, or concise text summaries of key metrics.

Can it access social platforms directly to pull data?

No. It requires exported metrics or API-provided data; it does not connect directly to third-party accounts.

Are platform benchmarks included?

Yes. The skill references general industry benchmarks but recommends using niche-specific benchmarks for finer accuracy.

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social-media-analyzer skill by alirezarezvani/claude-skills | VeilStrat