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paid-media

Normalize cross-platform paid media data into a unified schema and detect anomalies.

1|Updated Mar 19, 2026
Search Tags:#analytics#optimization#cross-platform#reporting#attribution#anomaly-detection#paid-media
One-click install
npx skills add https://github.com/weisberg/agile_agentic_analytics --skill paid-media-weisberg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: paid-media
Source: https://github.com/weisberg/agile_agentic_analytics/tree/main/plugins/marketing-analytics/skills/paid-media
Command: npx skills add https://github.com/weisberg/agile_agentic_analytics --skill paid-media-weisberg

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Cross-platform paid media data lives in silos and is hard to compare, slowing insights and optimization.

Core Features & Use Cases

  • Unified cross-platform data normalization from Google, Meta, LinkedIn, TikTok, and DV360 into a single schema.
  • Multi-method anomaly detection and automated insights for spend, CPA, CTR, and conversions.
  • Actionable outputs for budgeting, reporting, and creative rotation across campaigns.
  • Use Case: Analysts can surface top spend drivers and alert on budget overruns.

Quick Start

Load your latest campaign exports from workspace/raw and run the analytics workflow to generate a unified dashboard and alerts.

Frequently Asked Questions about paid-media

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

FAQPage Schema
How do I normalize cross-platform paid media data from Google, Meta, and LinkedIn into a single schema?▼

You can normalize cross-platform paid media data by running the deterministic Python scripts that ingest raw exports from Google, Meta, LinkedIn, TikTok, and DV360, mapping them into a single unified schema for consistent analysis.

What is the best way to detect spend and CPA anomalies across multiple ad platforms?▼

The best way to detect paid media anomalies is by applying multi-method analysis using rolling z-score, isolation forest, and STL decomposition to surface actionable insights for spend, CPA, CTR, and conversions.

How do I calculate ROAS and forecast spend for cross-platform advertising campaigns?▼

To calculate ROAS and forecast spend for cross-platform advertising, the workflow derives these metrics from normalized data, enabling you to surface actionable insights for budgeting, optimization, and fatigue management.

Can I use pandas for cross-platform paid media reporting and anomaly detection?▼

Yes, you can use pandas for cross-platform paid media reporting and anomaly detection because it is the required dependency for the script-based workflow that aggregates data and applies detection algorithms.

Does this workflow support budget overrun alerts and creative fatigue management?▼

Yes, the workflow supports budget overrun alerts and fatigue management by applying anomaly detection to normalized data, allowing analysts to surface top spend drivers and alert on budget overruns across campaigns.

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