Which platforms does this skill support?

It supports major advertising (Google, Meta, LinkedIn), analytics (GA4, Mixpanel, Amplitude), email (Mailchimp, HubSpot, SendGrid), and social (Instagram, Twitter) platforms.

How are API credentials managed?

The skill uses a secure configuration system located in your project directory that handles encrypted OAuth2 tokens and API keys for safe platform access.

How does the skill ensure data quality?

The skill includes automated checks for data completeness, cross-platform consistency (e.g., GA vs Ads variance), data freshness, and Z-score based anomaly detection.

Can it handle custom attribution modeling?

Yes, it supports multiple attribution models including last-click, first-click, linear, time-decay, and position-based models to help you understand your customer journey.

Marketing Data Pipeline

Name: Marketing Data Pipeline
Author: jmagly

byjmagly

•

Marketing Automation

Automates the collection, transformation, and reporting of marketing metrics across advertising, analytics, and social platforms.

About

The Marketing Data Pipeline skill for Claude Code streamlines complex marketing operations by orchestrating data workflows from various sources like Google Ads, Meta, and GA4. It manages the entire lifecycle of marketing data—from secure API extraction and normalization to advanced attribution modeling and anomaly detection. This skill is ideal for growth teams and developers building automated reporting systems, ensuring data consistency and providing actionable insights through structured reports, real-time alerts, and multi-channel performance summaries.

Key Features

Scheduled reporting with automated performance comparisons and trend analysis
67 GitHub stars
Automated transformation and normalization of KPIs like ROAS, CPA, and CTR
Advanced attribution modeling including Last-click, Linear, and Position-based
Multi-platform data extraction for Ads, Social, Analytics, and Email
Built-in data quality validation and statistical anomaly detection

Use Cases

Monitoring spend anomalies and conversion drops to prevent budget waste
Generating automated stakeholder reports with custom cross-channel attribution
Syncing daily campaign performance metrics across multiple fragmented ad networks

About

Key Features

Scheduled reporting with automated performance comparisons and trend analysis
67 GitHub stars
Automated transformation and normalization of KPIs like ROAS, CPA, and CTR
Advanced attribution modeling including Last-click, Linear, and Position-based
Multi-platform data extraction for Ads, Social, Analytics, and Email
Built-in data quality validation and statistical anomaly detection

Use Cases

Monitoring spend anomalies and conversion drops to prevent budget waste
Generating automated stakeholder reports with custom cross-channel attribution
Syncing daily campaign performance metrics across multiple fragmented ad networks