google-analytics: Data & Analytics for AI Agents | OpenAgentSkill

Overview

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.

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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:

export GOOGLE_ANALYTICS_PROPERTY_ID="your-property-id"
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"

Or create a .env file in your project root:

GOOGLE_ANALYTICS_PROPERTY_ID=123456789
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-key.json

Never commit credentials to version control. The service account JSON file should be stored securely outside your repository.

2. Install Required Packages

# Option 1: Install from requirements file (recommended)
pip install -r cli-tool/components/skills/analytics/google-analytics/requirements.txt

# Option 2: Install individually
pip install google-analytics-data python-dotenv pandas

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:

Connect to the API using the helper script
Fetch relevant metrics based on your question
Analyze the data looking for:
- Traffic trends and patterns
- User behavior insights
- Performance bottlenecks
- Conversion opportunities
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.

Scripts

The Skill includes utility scripts for API interaction:

Fetch Current Performance

python scripts/ga_client.py --days 30 --metrics sessions,users,bounceRate

Analyze and Generate Report

python scripts/analyze.py --period last-30-days --compare previous-period

The scripts handle API authentication, data fetching, and basic analysis. I'll interpret the results and provide actionable recommendations.

Troubleshooting

Authentication Error: Verify that:

GOOGLE_APPLICATION_CREDENTIALS points to a valid service account JSON file
The service account has "Viewer" access to your GA4 property
GOOGLE_ANALYTICS_PROPERTY_ID matches your GA4 property ID (not the measurement ID)

No Data Returned: Check that:

The property ID is correct (find it in GA4 Admin > Property Settings)
The date range contains data
The service account has been granted access in GA4

Import Errors: Install required packages:

pip install google-analytics-data python-dotenv pandas

Security Notes

Never hardcode API credentials or property IDs in code
Store service account JSON files outside version control
Use environment variables or .env files for configuration
Add .env and credential files to .gitignore
Rotate service account keys periodically
Use least-privilege access (Viewer role only)

Data Privacy

This Skill accesses aggregated analytics data only. It does not:

Access personally identifiable information (PII)
Store analytics data persistently
Share data with external services
Modify your Google Analytics configuration

All data is processed locally and used only to generate recommendations during the conversation.

View source ↗

File metadata

name: google-analytics
description: 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.

View original text

---
name: google-analytics
description: 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.
---

# 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:

```bash
export GOOGLE_ANALYTICS_PROPERTY_ID="your-property-id"
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"
```

Or create a `.env` file in your project root:

```env
GOOGLE_ANALYTICS_PROPERTY_ID=123456789
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-key.json
```

**Never commit credentials to version control.** The service account JSON file should be stored securely outside your repository.

### 2. Install Required Packages

```bash
# Option 1: Install from requirements file (recommended)
pip install -r cli-tool/components/skills/analytics/google-analytics/requirements.txt

# Option 2: Install individually
pip install google-analytics-data python-dotenv pandas
```

### 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](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](EXAMPLES.md).

## Scripts

The Skill includes utility scripts for API interaction:

### Fetch Current Performance
```bash
python scripts/ga_client.py --days 30 --metrics sessions,users,bounceRate
```

### Analyze and Generate Report
```bash
python scripts/analyze.py --period last-30-days --compare previous-period
```

The scripts handle API authentication, data fetching, and basic analysis. I'll interpret the results and provide actionable recommendations.

## Troubleshooting

**Authentication Error**: Verify that:
- `GOOGLE_APPLICATION_CREDENTIALS` points to a valid service account JSON file
- The service account has "Viewer" access to your GA4 property
- `GOOGLE_ANALYTICS_PROPERTY_ID` matches your GA4 property ID (not the measurement ID)

**No Data Returned**: Check that:
- The property ID is correct (find it in GA4 Admin > Property Settings)
- The date range contains data
- The service account has been granted access in GA4

**Import Errors**: Install required packages:
```bash
pip install google-analytics-data python-dotenv pandas
```

## Security Notes

- **Never hardcode** API credentials or property IDs in code
- Store service account JSON files **outside** version control
- Use environment variables or `.env` files for configuration
- Add `.env` and credential files to `.gitignore`
- Rotate service account keys periodically
- Use least-privilege access (Viewer role only)

## Data Privacy

This Skill accesses aggregated analytics data only. It does not:
- Access personally identifiable information (PII)
- Store analytics data persistently
- Share data with external services
- Modify your Google Analytics configuration

All data is processed locally and used only to generate recommendations during the conversation.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
The SKILL.md references a requirements file path 'cli-tool/components/skills/analytics/google-analytics/requirements.txt' which does not match the actual repository structure ('.agents/skills/google-analytics/requirements.txt'). This could cause installation errors.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 484 stars, 43 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: kwakseongjae/oh-my-design
License: MIT
Version: 1.0.0
Last GitHub push: Sep 5, 2026
Registry updated: Sep 5, 2026
Instruction path: .agents/skills/google-analytics/SKILL.md @ d4ef62120571

Version reported in registry metadata; check source releases before relying on it.

Quality

74/100

Strong

Trust

60/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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Source: kwakseongjae/oh-my-design
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