analytics-tracking-automation
AI-powered GA4 + GTM event tracking — automates site analysis, event schema, GTM sync, preview verification, and publishing. Works with Cursor, Codex, and any AI agent.
Install / Use
npx skills add jtrackingai/analytics-tracking-automationInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Our assessment of analytics-tracking-automation
analytics-tracking-automation scores 83/100 on our quality scale, 2172nd of 2,863 Automation skills we index.
Its SKILL.md is 8.7 KB long, well organised into 16 sections with 12 code examples: a thorough specification that gives an agent plenty to work with.
It has 145 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated about 6 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- Our last check on 2026-10-18 found the source still online.
- It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 94/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
analytics-tracking-automation compared with similar skills
All 4 of these similar skills score higher than analytics-tracking-automation; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| analytics-tracking-automation (this skill)by jtrackingai | 83 | 145 | 6mo ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 109.2k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 97.9k | today | MCP Server |
| rufloby ruvnet | 100 | 81.0k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 44d ago | SKILL.md |
Frequently asked questions
- How do I install analytics-tracking-automation?
- Run
npx skills add jtrackingai/analytics-tracking-automation. The install tabs above show the steps for each supported agent. - Which AI agents does analytics-tracking-automation work with?
- It is written for Claude Code, Cursor and OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is analytics-tracking-automation safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 94/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is analytics-tracking-automation still maintained?
- The repository was last updated about 6 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubAnalytics Tracking Automation
analytics-tracking-automation is a local-first AI skill for planning, reviewing, and delivering GA4 + GTM tracking.
Use it when you want an agent to help with:
- analyzing a site before tracking work starts
- grouping pages by business purpose
- designing or reviewing a GA4 event plan
- comparing a proposed plan with an existing live GTM setup
- preparing GTM-ready outputs and verification guidance
- handling both generic websites and Shopify storefronts
This README is intentionally user-facing and conversation-first. If you need the CLI surface or maintainer workflow, use DEVELOPING.md.
What You Get
For a given website, this skill can help produce:
- a reviewable site analysis
- business-friendly page groups
- a compact, decision-ready tracking plan
- GTM-ready tracking outputs
- verification and health summaries before go-live
- artifact-backed progress that can be resumed later
Installation
Most users only need the umbrella skill.
Recommended
Clone the repository locally, then install the skill into your agent skills directory:
git clone https://github.com/jtrackingai/analytics-tracking-automation.git
cd analytics-tracking-automation
npm run install:skills
No-Clone Alternative
If you do not want to clone the repository, install the root skill directly:
npx skills add jtrackingai/analytics-tracking-automation
For advanced install options and exported skill bundles:
ClawHub Publish
If you are publishing this skill to ClawHub, publish the exported public bundle instead of the full repository:
npm run export:skills:clawhub
Then upload dist/clawhub-skill-bundles/analytics-tracking-automation.
That public bundle is a publish-safe skill bundle. It keeps the agent-facing skill docs and references while stripping bundled executable runtime files (CLI bootstrap, packaged node modules, telemetry transport, and updater runtime) that tend to trigger stricter marketplace security scans.
When users install/use this public ClawHub bundle, they must run this prerequisite first (before any event-tracking command):
npx skills add jtrackingai/analytics-tracking-automation
Quick Start
Use It As A Skill
The intended experience is simple: tell your agent what you want in plain language.
Good requests usually include one or more of:
- the site URL
- whether this is a new setup, update, upkeep, or audit
- the output root for a new site run, such as
./outputor/tmp/output - an existing site artifact directory if you already have one, such as
./output/example_com - GA4 measurement ID or GTM context when you already know them
- any scope boundary such as "stop after schema review"
For a new setup, the output root is not the artifact directory itself. The agent/CLI creates one artifact directory per site under that root, for example ./output/example_com.
Example Prompts
New setup from scratch:
Use analytics-tracking-automation to plan GA4 + GTM tracking for https://www.example.com.
Use ./output as the output root; create the site artifact directory under it.
Start from a fresh run and stop after the event schema is ready for review.
New setup with implementation context:
Use analytics-tracking-automation to set up tracking for https://www.example.com.
Use /tmp/output as the output root, so this site's artifacts go under /tmp/output/www_example_com.
GA4 Measurement ID is G-XXXXXXXXXX.
We care most about signup, pricing, contact, and demo intent.
Audit only:
Use analytics-tracking-automation to run a tracking health audit for https://www.example.com.
I only want to understand the current live GTM setup and whether we should repair or rebuild.
Do not continue into deployment work.
Routine upkeep:
Use analytics-tracking-automation to do an upkeep review for this existing run:
./output/example_com
Tell me what is still healthy, what drifted, and what needs repair.
Update an existing artifact:
Use analytics-tracking-automation to resume this artifact directory:
./output/example_com
Tell me the current checkpoint and continue only through schema review.
Page-group review only:
Use analytics-tracking-automation to review and refine the page groups in:
./output/example_com/site-analysis.json
Focus on business intent, not just URL shape.
Shopify branch:
Use analytics-tracking-automation for this Shopify storefront:
https://store.example.com
I want the Shopify-specific tracking path, not the generic website flow.
How To Think About It
This skill is best when you want the agent to act like a tracking lead, not just a command runner.
A typical conversation flow is:
- inspect the site or resume an existing run
- group pages in a reviewable way
- draft or revise the event plan
- review what should be reused, repaired, added, or dropped
- continue into GTM generation and verification only when you explicitly want that
Where To Go Next
- Installation details: docs/README.install.md
- Skill family map: docs/skills.md
- Agent-facing workflow contract: SKILL.md
- CLI and maintainer workflow: DEVELOPING.md
Quick Debug Tips
If preview troubleshooting points to selector mismatch or page-load/navigation issues, these Playwright CLI helpers are faster than repeatedly re-running the full flow:
npm run debug:open -- https://www.example.com
npm run debug:codegen -- https://www.example.com
debug:open: headed browser for quick visual checks (redirect loops, WAF pages, blocked content).debug:codegen: interactive selector capture for fixingevent-schema.jsonselectors.
Product Boundary
- the workflow runs locally
- browser-backed steps rely on Playwright Chromium;
npm installtriggers the packagepostinstallstep that installs the browser binary - GTM sync uses Google OAuth
- OAuth credentials are cached in the artifact directory for the current site run
- generic sites use automated preview verification
- Shopify uses the Shopify-specific branch and manual post-install validation
- a minimal anonymous startup signal is sent when an
event-trackingcommand begins so operators can measure active usage; it is session-scoped and limited to technical metadata such as command name, CLI version, OS family, Node major version, and a per-invocation session identifier - richer anonymous usage diagnostics remain opt-in and the first explicit choice is stored in local user config; if enabled, future runs send higher-level workflow data such as command name, success/failure status, rough counts, site hostname, detected platform, and broad workflow checkpoints so operators can improve the product and troubleshoot reliability; if declined, future runs continue normally without these richer diagnostics until the user changes that local config choice; those richer diagnostics do not send full URLs, page paths, query strings, file paths, GTM/GA IDs, selectors, OAuth data, raw errors, or page content; the remaining privacy risk is that the site hostname and workflow metadata still reveal which domain was worked on and broad usage patterns; if a workflow prompt asks for telemetry consent and no stored preference exists, the operator must stop and let the user choose
yesornoinstead of answering on the user's behalf - installed copy bundles can self-check GitHub for updates and reinstall the same selected bundle set
Need A More Advanced Setup?
This skill reflects the implementation workflow behind JTracking.
If you need a more advanced setup, JTracking also supports:
- richer tracking design based on concrete business flows
- server-side tracking and custom loaders
- more destination and ad-platform integrations
- longer-term tracking operations and maintenance
Contact Us
If you run into any issues while using this skill, contact us at support@jtracking.ai.
We will reply as soon as we see your message.
License
This project is licensed under the Apache License, Version 2.0. See LICENSE for the full text.
Use of the JTracking name, logo, and other brand assets is not granted under this license.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.