How does this skill handle Google API rate limits?

The skill implements an exponential backoff retry pattern that automatically waits and retries requests when it encounters 429 'Too Many Requests' errors.

Can I combine GA4 and GSC data into one report?

Yes, the skill provides a unified data model specifically designed to merge engagement metrics from GA4 with search performance data from Google Search Console.

What happens if one of the APIs is unavailable?

It uses a graceful degradation strategy, allowing the analysis to continue using partial data from available sources while providing clear troubleshooting steps for the failed source.

Does it support caching to save on API usage?

Yes, it includes a session-based caching pattern that stores API responses locally for a configurable duration (TTL) to prevent redundant network calls.

SEO Data Extraction Patterns

Name: SEO Data Extraction Patterns
Author: MadAppGang

byMadAppGang

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211

Marketing Automation

Streamlines the extraction and normalization of analytics data from Google Analytics 4 and Google Search Console.

About

This skill provides a comprehensive framework for building robust SEO data pipelines within Claude Code. It implements sophisticated patterns for interacting with GA4 and GSC APIs, including parallel request execution to reduce latency, session-based caching to minimize API costs, and exponential backoff for handling rate limits. By standardizing date ranges and providing a unified data model, it enables developers to merge disparate traffic and search performance metrics into actionable insights while ensuring graceful degradation when specific data sources are unavailable.

Key Features

Automated rate limiting and exponential backoff retry logic
Session-based caching system for reduced API overhead
Unified data modeling for merging traffic and search metrics
211 GitHub stars
Parallel data fetching patterns for GA4 and GSC optimization
Standardized date range calculations for consistent reporting

Use Cases

Building automated SEO performance and traffic dashboards
Creating resilient data collection workflows for marketing reporting
Merging GSC keyword data with GA4 engagement metrics for content analysis

About

Key Features

Automated rate limiting and exponential backoff retry logic
Session-based caching system for reduced API overhead
Unified data modeling for merging traffic and search metrics
211 GitHub stars
Parallel data fetching patterns for GA4 and GSC optimization
Standardized date range calculations for consistent reporting

Use Cases

Building automated SEO performance and traffic dashboards
Creating resilient data collection workflows for marketing reporting
Merging GSC keyword data with GA4 engagement metrics for content analysis