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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.
Übersicht
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.
Vollständige Dokumentation lesen
Quelldokumentation, keine Anweisungen für diese Website. Vor dem Ausführen von Befehlen die Berechtigungen prüfen.
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_CREDENTIALSpoints to a valid service account JSON file- The service account has "Viewer" access to your GA4 property
GOOGLE_ANALYTICS_PROPERTY_IDmatches 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
.envfiles for configuration - Add
.envand 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.
Dateimetadaten
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.
Originaltext anzeigen
--- 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.
Quelle prüfen
Preis und Betriebskosten
- Skill beziehen
- Preis unbestätigt
- Ausführen
- Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
- Lizenz
- MIT
- Preis unbestätigt
- Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.
Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →
Skill-Quelle erfasst
Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.
Vor Installation prüfen: Automatische Installation vermeiden
Lizenz: 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
Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.
Mit einer kleinen Aufgabe beginnen
- 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
- 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
- 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.
Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.
Quelle und Nutzungshinweise
Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.
- Quell-Repository
- kwakseongjae/oh-my-design
- Lizenz
- MIT
- Version
- 1.0.0
- Letzter GitHub-Push
- 5. Sept. 2026
- Verzeichnis aktualisiert
- 5. Sept. 2026
- Anleitungspfad
- .agents/skills/google-analytics/SKILL.md @ d4ef62120571
Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.
Qualität
71/100
Stark
Vertrauen
59/100
Do not auto-install
Audit
74/100
Prüfung nötig
- 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
- Verified installs
- —
- Ergebnisse
- —
Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.
Agent-Zugang
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Weitere Details
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"audit": "https://www.openagentskill.com/skills/kwakseongjae-google-analytics/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=kwakseongjae-google-analytics&task=Use%20google-analytics%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20google-analytics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20google-analytics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/kwakseongjae-google-analytics/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/kwakseongjae-google-analytics"
}
}Für Ersteller
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- kwakseongjae
- Indexiert von
- OpenAgentSkill Community-Index
Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.
Diesen Skill beanspruchenEigentümeranspruch
Diesen Skill-Eintrag beanspruchen
Dieser Registry-indexiert-Eintrag wird kwakseongjae zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.
Share-Kit
Creator-Backlink-Kit
Evidenz-Badges in deine README einfügen
Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
[](https://www.openagentskill.com/skills/kwakseongjae-google-analytics?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/kwakseongjae-google-analytics?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/kwakseongjae-google-analytics/audit)
[](https://www.openagentskill.com/skills/kwakseongjae-google-analytics?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Community-Signal
Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.
