data-analyst

Analyze SEO crawl datasets to detect patterns and identify issues.

56|6|Updated Jan 19, 2026

Search Tags:#performance #reporting #data analysis #seo #trend detection #crawl data

Ready to run

npx skills add https://github.com/Vinix24/vnx-orchestration --skill data-analyst-vinix24

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/Vinix24/vnx-orchestration/tree/main/skills/data-analyst
Command: npx skills add https://github.com/Vinix24/vnx-orchestration --skill data-analyst-vinix24

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, psycopg2, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of analyzing SEO crawl data, enabling data-driven decision-making and trend detection that improve website visibility and performance.

Core Features & Use Cases

Data Processing and Statistical Analysis: Process large SEO datasets to identify key performance metrics and anomalies.
Trend and Pattern Recognition: Detect SEO issues, performance degradations, and successful optimization strategies.
Use Case: Marketing teams can use this Skill to analyze past crawl data, uncover site performance bottlenecks, and generate reports to inform SEO strategies.

Quick Start

Use the data-analyst skill to analyze the latest crawl results stored in your database to generate a detailed SEO performance report.

Frequently Asked Questions about data-analyst

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

FAQPage Schema

How do I analyze SEO crawl data to identify performance issues and trends?▼

Analyzing SEO crawl data requires processing datasets with statistical and trend analysis to detect patterns, identify issues, and produce actionable insights for improving website SEO visibility and performance.

Can I use pandas and numpy to process large SEO datasets for anomalies?▼

Yes, you can use pandas and numpy to process large SEO datasets, identify key performance metrics, and detect anomalies within your crawl results to inform data-driven marketing decisions.

What's the best way to generate SEO performance reports from database crawl results?▼

Generating SEO performance reports from database crawl results involves connecting via psycopg2, processing the data to uncover site performance bottlenecks, and producing detailed reports to inform SEO strategies.

Do I need matplotlib and seaborn to visualize SEO trend detection from crawl data?▼

Yes, matplotlib and seaborn are required dependencies to visualize trend detection and pattern recognition from SEO crawl data, helping you identify performance degradations and successful optimization strategies.

Does this data analysis approach work with PostgreSQL databases for website SEO reporting?▼

Yes, this data analysis approach supports PostgreSQL database connectivity through psycopg2, allowing you to extract crawl data directly to analyze site performance bottlenecks and generate SEO reporting insights.