Agent skill · data analytics · davila7
google-analytics
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
Why this skill is useful
Adds utility scripts for fetching and analyzing Google Analytics data that the AI wouldn't generate on its own.
What it needs
Requires google-analytics-data, pandas, python-dotenv installed locally. Requires google-analytics account access. About 2k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
Google Analytics Analysis Analyze website performance using Google Analytics data to provide actionable insights and improvement recommendations. Quick Start 1. Setup Authentication This Skill requires Google Analytics API credentials. Set up environment variables: Or create a .env file in your project root: Never commit credentials to version control. The service account JSON file should be stored securely outside your repository. 2. Install Required Packages 3. Analyze Your Project Once configured, I can: Review current traffic and user behavior metrics Identify top-performing and underperforming pages Analyze traffic sources and conversion funnels Compare performance across time periods Suggest data-driven improvements How to Use Ask me questions like: "Review our Google Analytics performance for the last 30 days" "What are our top traffic sources?" "Which pages have the highest bounce rates?" "Analyze user engagement and suggest improvements" "Compare this month's performance to last month" Analysis Workflow When you ask me to analyze Google Analytics data, I will: 1. Connect to the API using the helper script 2. Fetch relevant metrics based on your question 3. Analyze the data looking for: Traffic trends and patterns User behavior insights Performance bottlenecks Conversion opportunities 4. Provide recommendations with: Specific improvement suggestions Priority level (high/medium/low) Expected impact Implementation guidance Common Metrics For detailed metric definitions and dimensions, see REFERENCE.md. Traffic Metrics Sessions, Users, New Users Page views, Screens per Session Average Session Duration Engagement Metrics Bounce Rate, Engagement Rate Event Count, Conversions Scroll Depth, Click-through Rate Acquisition Metrics Traffic Source/Medium Campaign Performance Channel Grouping Conversion Metrics Goal Completions E-commerce Transactions Conversion Rate by Source Analysis Examples For complete analysis patterns and use cases, see EXAMPLES.md. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/google-analytics