google-analytics

name	google-analytics
description	Collect and analyze Google Analytics 4 (GA4) website data in a local SQLite database. Stores pageviews, active users, reading dwell time, traffic sources, and outbound clicks so you can run SQL queries or view reports on site performance. Activate when analyzing website traffic, measuring reader engagement and dwell time, evaluating the impact of site updates or milestones, or querying Google Analytics with SQL.
license	Apache-2.0
metadata	{"category":"analytics","tags":"ga4, analytics, traffic, metrics, optimization","author":"Daniela Petruzalek ([e-mail])","version":"0.2.0","catalog":"https://skills.danicat.dev"}

Google Analytics 4 SQLite Ingestion & SQL Analytics

The google-analytics skill ingests Google Analytics 4 (GA4) traffic, reading depth, acquisition channels, event streams, and outbound clicks into a local SQLite analytics database (google_analytics.db or $XDG_DATA_HOME/google-analytics/analytics.db) without data loss, preserving raw JSON payloads on all records, and providing a fast SQL interface for website analytics.

Available scripts

• scripts/google_analytics.py: Automated sync, reporting, and annotation CLI for Google Analytics 4. Executed via uv run scripts/google_analytics.py (requires Google Cloud ADC or OAuth credentials).
• scripts/test_google_analytics.py: Unit and regression test suite validating schema, query extraction, and CLI flags.

---

⚡ Quick Start & Primary Actions

All operations are driven via the bundled Python CLI script:

# 1. Authorize OAuth 2.0 (with analytics.edit & readonly scopes)
uv run scripts/google_analytics.py auth --port 8080

# 2. Discover accessible GA4 properties
uv run scripts/google_analytics.py properties

# 3. Create Deployment / Milestone Annotation (Cloud API + Local SQLite)
uv run scripts/google_analytics.py annotate \
  --title "Major Release / Architecture Overhaul" \
  --date 2026-08-18 \
  --commit abc1234 \
  --description "Milestone description and release context."

# 4. Incremental Sync (Updates newest days + 3-day latency lookback overlap)
uv run scripts/google_analytics.py sync --db path/to/database.db

# 5. Full Historical Backfill (Ingests up to 14 months of daily granular data)
uv run scripts/google_analytics.py sync --full --db path/to/database.db

# 6. Run Pre-Built Reports
uv run scripts/google_analytics.py report overview --db path/to/database.db
uv run scripts/google_analytics.py report top-pages --db path/to/database.db
uv run scripts/google_analytics.py report channels --db path/to/database.db
uv run scripts/google_analytics.py report geo --db path/to/database.db
uv run scripts/google_analytics.py report events --db path/to/database.db
uv run scripts/google_analytics.py report outbound --db path/to/database.db
uv run scripts/google_analytics.py report milestone-impact --db path/to/database.db

# 7. Execute Ad-Hoc SQL Query
uv run scripts/google_analytics.py query "SELECT page_path, total_views, total_users, avg_dwell_sec, avg_bounce_pct FROM v_page_performance LIMIT 10" --db path/to/database.db

If --db is omitted, the script defaults to google_analytics.db in the current working directory.

---

🗄️ Database Schema & Relational Structure

The database maintains 7 relational tables and 7 analytical views. Detailed DDL and schema definitions are in references/schema.md.

Tables

1. daily_pages: Granular daily page metrics by URL, country, device, and traffic source.
   • Key columns: id (PK), property_id, date, page_path, page_title, country, device_category, source_medium, screen_page_views, active_users, sessions, user_engagement_duration, bounce_rate, raw_json, synced_at.
2. daily_traffic: Acquisition channels and source/medium pairs.
   • Key columns: id (PK), property_id, date, session_source_medium, session_default_channel_group, country, device_category, sessions, active_users, new_users, engaged_sessions, user_engagement_duration, bounce_rate, raw_json, synced_at.
3. daily_events: User interaction event stream (scroll, click, first_visit, user_engagement, page_view).
   • Key columns: id (PK), property_id, date, event_name, page_path, country, device_category, event_count, total_users, raw_json, synced_at.
4. outbound_clicks: External link exit destinations and click counts.
   • Key columns: id (PK), property_id, date, link_url, page_path, , , , , .

---

📊 Analytical SQL Views

View Name	Description	Key Columns
v_daily_summary	Daily aggregated traffic metrics	date, total_sessions, total_active_users, total_page_views, total_engagement_min, avg_bounce_pct
v_page_performance	Page rollup with views, active users, dwell time, and bounce rate	page_path, page_title, total_views, total_users, total_sessions, avg_dwell_sec, total_dwell_min, avg_bounce_pct
v_channel_performance	Acquisition channel breakdown	channel_group, source_medium, total_sessions, total_users, total_new_users, total_engaged_sessions, engagement_rate_pct, total_dwell_min, avg_bounce_pct
v_geo_breakdown	Country traffic and dwell time	country, total_sessions, total_users, total_page_views, avg_dwell_sec, avg_bounce_pct
v_events_summary	Aggregate event counts	event_name, total_events, total_users
v_outbound_links	Outbound destination rankings	link_url, total_clicks, total_users, referring_pages_count

---

🔍 SQL Analytics Recipes

Pre-tested SQL query recipes are documented in references/queries.md.

1. Top Landing Pages by Active Dwell Time

SELECT
    page_path,
    page_title,
    total_views,
    total_users,
    avg_dwell_sec || 's' AS avg_dwell,
    total_dwell_min || 'm' AS total_dwell,
    avg_bounce_pct || '%' AS bounce_pct
FROM v_page_performance
ORDER BY total_dwell_min DESC
LIMIT 15;

2. Category / Subdirectory Rollup

SELECT
    CASE
        WHEN page_path LIKE '/docs/%' THEN 'Docs'
        WHEN page_path LIKE '/blog/%' THEN 'Blog'
        WHEN page_path = '/' THEN 'Homepage'
        ELSE 'Other'
    END AS category,
    COUNT(DISTINCT page_path) AS page_count,
    SUM(total_views) AS total_views,
    SUM(total_users) AS total_users,
    ROUND(SUM(total_dwell_min), 1) AS total_dwell_min
FROM v_page_performance
GROUP BY category
ORDER BY total_views DESC;

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⚠️ Cross-Tool Alignment: GA4 Organic Search vs. Search Console Property Totals

When cross-referencing GA4 acquisition metrics with Google Search Console data:

1. Multi-Engine Organic Reach: GA4 Organic Search aggregates landing sessions and active users across all search engines (Google Search, Bing, DuckDuckGo, Ecosia, Yahoo, AI search engines). Search Console exclusively measures Google Search impressions and clicks.
2. Session Arrivals vs. SERP Clicks: GA4 tracks 100% of landing sessions without query-level privacy truncation. In contrast, Search Console API keyword exports filter out "anonymized queries".
3. Property-Level Reconciliation: For organic traffic reporting, GA4 v_channel_performance (filtering for channel_group = 'Organic Search') naturally aligns with Search Console property-level totals (daily_site_performance in search-analytics and GSC Web UI Performance cards), while Search Console's search_performance table provides the granular ranking breakdown for identifiable keywords.

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📚 Progressive Disclosure & References

• Full DDL Schema Reference: references/schema.md — Complete SQL table definitions, column types, constraints, and views.
• SQL Query Cookbook: references/queries.md — Tested SQL recipes for reading depth, acquisition channels, and exit destinations.
• Authentication Guide: references/setup_auth.md — Google Cloud ADC login, API enablement, and GA4 property permissions.