Which SEO tools are supported by this skill?

The skill provides patterns for Google Analytics 4 (GA4), Google Search Console (GSC), and SE Ranking using their respective MCP servers.

How does this skill handle API rate limits?

It includes pre-built retry patterns with exponential backoff and strategies for handling the specific QPS limits of GA4, GSC, and SE Ranking.

Can I fetch data from GA4 and GSC simultaneously?

Yes, the skill includes a Parallel Data Fetch pattern that issues multiple requests in a single message to improve execution speed by up to 3x.

What happens if one of the data sources is unavailable?

The skill implements a Graceful Degradation strategy, providing fallback mechanisms such as using GSC clicks when GA4 data is missing or using cached ranking data.

Does this skill help with data consistency?

Yes, it provides standardized date range calculations (e.g., 7d, 30d, MTD) and a unified JSON data model to ensure metrics from different sources align correctly.

SEO Data Extraction Patterns

Name: SEO Data Extraction Patterns
Author: involvex

byinvolvex

Automatización de Marketing

Optimizes the retrieval and combination of SEO and analytics data from GA4, GSC, and SE Ranking using resilient patterns.

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This skill provides a standardized framework for Claude to interact with leading SEO and analytics platforms, including Google Analytics 4, Google Search Console, and SE Ranking. It implements advanced implementation patterns such as parallel execution for 3x faster data fetching, exponential backoff for rate limiting, and session-based caching to reduce API costs. By unifying disparate data sources into a single model, it enables the creation of robust SEO pipelines, automated reporting workflows, and graceful degradation strategies when specific data sources are unavailable.

Características Principales

Automated rate limiting and exponential backoff retry logic
Standardized date range calculations for cross-platform consistency
Session-based caching to optimize API usage and speed
Parallel data fetching for high-performance multi-source requests
Unified data model for combining GA4, GSC, and SE Ranking metrics
0 GitHub stars

Casos de Uso

Creating resilient data pipelines with automated error handling and fallbacks
Performing keyword gap and competitive analysis at scale
Building automated SEO performance and traffic dashboards

Acerca de

Características Principales

Automated rate limiting and exponential backoff retry logic
Standardized date range calculations for cross-platform consistency
Session-based caching to optimize API usage and speed
Parallel data fetching for high-performance multi-source requests
Unified data model for combining GA4, GSC, and SE Ranking metrics
0 GitHub stars

Casos de Uso

Creating resilient data pipelines with automated error handling and fallbacks
Performing keyword gap and competitive analysis at scale
Building automated SEO performance and traffic dashboards