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tracking-live-gtm

Audit live Google Tag Manager runtime and extract event details.

134|39|Updated Apr 2, 2026
Search Tags:#gtm#ga4#tracking audit#live verification#event schema planning#measurement ids#javascript tag manager
One-click install
npx skills add https://github.com/jtrackingai/analytics-tracking-automation --skill tracking-live-gtm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tracking-live-gtm
Source: https://github.com/jtrackingai/analytics-tracking-automation/tree/main/skills/tracking-live-gtm
Command: npx skills add https://github.com/jtrackingai/analytics-tracking-automation --skill tracking-live-gtm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you reduce tracking mistakes by inspecting the real, currently published Google Tag Manager (GTM) runtime on the site before you generate or revise GA4 event schema.

Core Features & Use Cases

  • Live GTM baseline audit: Detects all live GTM containers and summarizes the primary comparison baseline so your schema work matches reality.
  • Event and measurement review: Extracts and names existing live events and measurement-related details, then highlights obvious issues.
  • Verification-first workflow: Supports a verify flow that separates runtime-detected live definitions from browser-verified firing evidence to keep evidence trails clear.
  • Go-live stop boundary: Stops after the live baseline review unless you explicitly ask to proceed into schema authoring.

Use it when you are comparing multiple live GTM containers, you want to test what is already published on the real site, or you need a tracking health audit with clearly separated “what the site is running” versus “what the automation preview verified.”

Quick Start

Ask an AI agent to run tracking-live-gtm for your existing site crawl artifact by providing your <artifact-dir>/site-analysis.json so it produces live GTM analysis and a decision-ready review.

Frequently Asked Questions about tracking-live-gtm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit live GTM containers before creating a GA4 event schema?▼

To audit live GTM containers before GA4 event schema generation, you can run a live runtime review that detects all published containers, summarizes the primary baseline, and extracts existing measurement IDs and events to prevent schema drift.

What is a GTM tracking audit and when do I need to verify live measurement IDs?▼

A GTM tracking audit inspects the real published runtime on a target site to verify live measurement IDs and events. You need it when a crawl may miss public GTM identifiers or before making schema changes to ensure your tracking matches reality.

How do I verify multiple live Google Tag Manager containers on a website?▼

You can verify multiple live Google Tag Manager containers by executing a live runtime analysis that detects all active containers and establishes a primary comparison baseline, ensuring your event schema work aligns with the actual published tracking setup.

Can I separate runtime-detected GTM definitions from browser-verified firing evidence?▼

Yes, you can separate runtime-detected GTM definitions from browser-verified firing evidence by using a verification-first workflow that distinguishes what the site is currently running from what the automation preview actually verified.

Does the live GTM audit automatically generate GA4 event schema after reviewing the baseline?▼

No, the live GTM audit stops after producing the baseline review artifacts and does not automatically generate GA4 event schema. It enforces a go-live stop boundary, requiring you to explicitly approve the baseline and request to proceed into schema authoring.

Why does my GA4 event schema drift from what is actually published in my GTM runtime?▼

GA4 event schema drifts from the live GTM runtime when schema changes are made without inspecting the real published containers. Auditing the live runtime first extracts existing events and highlights obvious issues to keep your schema aligned with reality.

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