Ga4

GA4

Use this skill for Google Analytics 4 reporting through the Google Analytics Data API: sessions, key events, revenue, traffic-source breakdowns, landing-page breakdowns, time-series queries, ad-hoc analyst reads, dashboards, scheduled reporting, ETL, and monitoring.

Scope

• run GA4 runReport requests for a configured property
• translate common English analyst requests into reviewable Data API payloads
• review GA4 request JSON before execution
• build traffic-source, channel-group, landing-page, daily trend, revenue, session, user, and key-event reports
• support delegated-user OAuth and service-account auth through the same gateway bearer handle contract
• keep auth-mode selection in tenant/runtime config, not in prompt logic
• measure skill run cost through normal HybridClaw UsageTotals

Default Workflow

1. Start with report-plan for natural-language requests unless the user already supplied a GA4 Data API request JSON.
2. Review generated or user-supplied request JSON with review-request before a live call.
3. For live GA4 API calls inside HybridClaw, use http_request directly when available. Pass the emitted httpRequest object from the helper to the tool.
4. Use the helper for offline planning/review and for gateway-proxied execution when http_request is unavailable:

   python3 skills/ga4/scripts/ga4.py ...
   

5. Treat all GA4 operations in this skill as read-only. Do not attempt Admin API mutations, property access changes, key-event changes, or tag changes.

Auth Contract

The skill supports two auth modes without exposing tokens or private keys to the model:

• delegated-user OAuth through a bearer secret handle
• Google service-account JWT bearer exchange through the gateway

Tenant config decides which mode to use.

Default delegated-user OAuth handle:

hybridclaw auth login google \
  --client-id "<google-oauth-client-id>" \
  --client-secret "<google-oauth-client-secret>" \
  --account you@example.com \
  --scopes "https://www.googleapis.com/auth/analytics.readonly"

hybridclaw auth status google

For delegated OAuth, use the default bearerSecretName: "GOOGLE_WORKSPACE_CLI_TOKEN" or a google-oauth URL auth route.

For service-account automation, store the service-account client_email and PEM private_key as encrypted runtime secrets. The gateway reads those named secrets, signs the JWT assertion server-side, exchanges it for a short-lived Google access token, and injects that token into the GA4 request.

Set or update those secrets in this order:

1. Browser admin: open the active HybridClaw admin URL ending in /admin/secrets.
2. Browser /chat or TUI fallback: /secret set GA4_SERVICE_ACCOUNT_EMAIL "<client-email>" and /secret set GA4_SERVICE_ACCOUNT_PRIVATE_KEY "<pem-private-key>".
3. Local console fallback:

hybridclaw secret set GA4_SERVICE_ACCOUNT_EMAIL "<client-email>"
hybridclaw secret set GA4_SERVICE_ACCOUNT_PRIVATE_KEY '<pem-private-key>'

Do not paste OAuth access tokens, refresh tokens, service-account private keys, or downloaded JSON key files into the prompt. Real credentials stay in the HybridClaw secret runtime and are used server-side by the gateway.

Optional stored defaults use the same setup order: browser admin at /admin/secrets, then /secret set in browser /chat or TUI, then local console fallback.

hybridclaw secret set GA4_PROPERTY_ID "<numeric-property-id>"
hybridclaw secret set GA4_BEARER_SECRET_NAME "GOOGLE_WORKSPACE_CLI_TOKEN"

For unattended service-account jobs, set environment defaults in the tenant runtime:

GA4_SERVICE_ACCOUNT_EMAIL_SECRET=GA4_SERVICE_ACCOUNT_EMAIL
GA4_SERVICE_ACCOUNT_PRIVATE_KEY_SECRET=GA4_SERVICE_ACCOUNT_PRIVATE_KEY

Command Contract

Plan a report from natural language:

python3 skills/ga4/scripts/ga4.py --format json report-plan \
  "Show me last week's organic conversions vs the prior week"

Build an http_request payload for a planned or hand-written request:

python3 skills/ga4/scripts/ga4.py --format json http-request 123456789 \
  --request-json '{"dateRanges":[{"startDate":"7daysAgo","endDate":"yesterday"}],"dimensions":[{"name":"date"}],"metrics":[{"name":"sessions"}],"limit":25}'

Run a report through the gateway helper path:

python3 skills/ga4/scripts/ga4.py --format json run-report 123456789 \
  --request-json '{"dateRanges":[{"startDate":"7daysAgo","endDate":"yesterday"}],"metrics":[{"name":"sessions"}]}'

Use service-account auth by choosing the stored service-account secret names:

python3 skills/ga4/scripts/ga4.py --format json http-request 123456789 \
  --google-service-account-email-secret GA4_SERVICE_ACCOUNT_EMAIL \
  --google-service-account-private-key-secret GA4_SERVICE_ACCOUNT_PRIVATE_KEY \
  --request-json '{"dateRanges":[{"startDate":"7daysAgo","endDate":"yesterday"}],"metrics":[{"name":"sessions"}]}'

Review request JSON offline:

python3 skills/ga4/scripts/ga4.py --format json review-request \
  '{"dateRanges":[{"startDate":"30daysAgo","endDate":"yesterday"}],"dimensions":[{"name":"landingPagePlusQueryString"}],"metrics":[{"name":"sessions"},{"name":"totalRevenue"}],"limit":25}'

Emit the analyst-query prompt-template payload:

python3 skills/ga4/scripts/ga4.py --format json prompt-template \
  "Daily sessions and revenue for the last 30 days"

Run the bundled eval suite:

python3 skills/ga4/scripts/ga4.py --format json eval-scenarios

Working Rules

• Use numeric GA4 property ids. Accept properties/<id> input, but normalize it before API calls.
• Use keyEvents for conversion-style reporting unless the user explicitly requests a custom event metric.
• Use sessionDefaultChannelGroup, sessionSourceMedium, landingPagePlusQueryString, and date for the common production breakdowns before reaching for custom dimensions.
• Include a row limit. Default to 25 for ad-hoc reports and increase only when the user asks for an export or ETL-sized result.
• Ask for clarification when the request names a quarter without a year.
• Do not add Admin API writes to this skill. A separate admin skill would need explicit approval tiers and boundary tests.

Eval Suite

The bundled scenarios cover 25 representative analyst requests across sessions, key events, revenue, traffic source, landing page, time series, comparisons, ecommerce, geographic, device, and safety/clarification cases.