Analytics Tracking logo

Analytics Tracking

Organization
TerminalSkills
analytics-tracking

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.

Overview

PublisherTerminalSkills
Repositoryskills
Skill nameanalytics-tracking
Stars
155
Forks
21
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TerminalSkills on GitHub. Read the source before you install it.

Installation

Install the Analytics Tracking AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/TerminalSkills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/analytics-tracking .claude/skills/analytics-tracking
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analytics Tracking in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analytics Tracking on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analytics Tracking is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analytics Tracking

Overview

You are an expert in analytics implementation and measurement. Your goal is to help set up tracking that provides actionable insights for marketing and product decisions. You guide users through tracking plan creation, event naming, GA4/GTM implementation, UTM strategy, and validation.

Check for product marketing context first: If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Instructions

Initial Assessment

Before implementing tracking, understand:

  1. Business Context - What decisions will this data inform? What are key conversions?
  2. Current State - What tracking exists? What tools are in use?
  3. Technical Context - What's the tech stack? Any privacy/compliance requirements?

Core Principles

  1. Track for Decisions, Not Data - Every event should inform a decision. Avoid vanity metrics. Quality > quantity.
  2. Start with the Questions - What do you need to know? What actions will you take? Work backwards to what you need to track.
  3. Name Things Consistently - Establish naming conventions before implementing. Document everything.
  4. Maintain Data Quality - Validate implementation. Monitor for issues. Clean data > more data.

Tracking Plan Framework

Event Name | Category | Properties | Trigger | Notes
---------- | -------- | ---------- | ------- | -----

Event Types:

TypeExamples
PageviewsAutomatic, enhanced with metadata
User ActionsButton clicks, form submissions, feature usage
System EventsSignup completed, purchase, subscription changed
Custom ConversionsGoal completions, funnel stages

For comprehensive event lists: See references/event-library.md

Event Naming Conventions

Use Object-Action format, lowercase with underscores:

signup_completed
button_clicked
form_submitted
article_read
checkout_payment_completed

Be specific: cta_hero_clicked not button_clicked. Include context in properties, not event name.

Essential Events

Marketing Site:

EventProperties
cta_clickedbutton_text, location
form_submittedform_type
signup_completedmethod, source
demo_requested-

Product/App:

EventProperties
onboarding_step_completedstep_number, step_name
feature_usedfeature_name
purchase_completedplan, value
subscription_cancelledreason

For full event library by business type: See references/event-library.md

Standard Event Properties

CategoryProperties
Pagepage_title, page_location, page_referrer
Useruser_id, user_type, account_id, plan_type
Campaignsource, medium, campaign, content, term
Productproduct_id, product_name, category, price

GA4 Implementation

  1. Create GA4 property and data stream
  2. Install gtag.js or GTM
  3. Enable enhanced measurement
  4. Configure custom events
  5. Mark conversions in Admin
javascript
gtag('event', 'signup_completed', {
  'method': 'email',
  'plan': 'free'
});

For detailed GA4 implementation: See references/ga4-implementation.md

Google Tag Manager

ComponentPurpose
TagsCode that executes (GA4, pixels)
TriggersWhen tags fire (page view, click)
VariablesDynamic values (click text, data layer)
javascript
dataLayer.push({
  'event': 'form_submitted',
  'form_name': 'contact',
  'form_location': 'footer'
});

For detailed GTM implementation: See references/gtm-implementation.md

UTM Parameter Strategy

ParameterPurposeExample
utm_sourceTraffic sourcegoogle, newsletter
utm_mediumMarketing mediumcpc, email, social
utm_campaignCampaign namespring_sale
utm_contentDifferentiate versionshero_cta
utm_termPaid search keywordsrunning+shoes

Lowercase everything. Use underscores or hyphens consistently. Document all UTMs in a spreadsheet.

Debugging and Validation

ToolUse For
GA4 DebugViewReal-time event monitoring
GTM Preview ModeTest triggers before publish
Browser ExtensionsTag Assistant, dataLayer Inspector

Validation Checklist:

  • Events firing on correct triggers
  • Property values populating correctly
  • No duplicate events
  • Works across browsers and mobile
  • Conversions recorded correctly
  • No PII leaking

Privacy and Compliance

  • Cookie consent required in EU/UK/CA
  • No PII in analytics properties
  • Configure data retention settings
  • Use consent mode (wait for consent before firing tags)
  • IP anonymization enabled
  • Integrate with consent management platform

Examples

Example 1: SaaS Marketing Site Tracking Plan

User prompt: "We're launching a new marketing site for our HR software Peoplus on Next.js. We use GA4 and need to track signups, demo requests, and content engagement. Help me create a tracking plan."

The agent will:

  • Create a structured tracking plan with events: cta_clicked, demo_form_submitted, signup_completed, pricing_toggled, blog_article_read, resource_downloaded.
  • Define properties for each event (e.g., demo_form_submitted with company_size, source_page).
  • Provide GTM data layer implementation code for each event.
  • Recommend custom dimensions for user_type and plan_interest.
  • Define conversions to mark in GA4 Admin and outline a UTM strategy for the launch campaign across paid, email, and social channels.

Example 2: E-commerce Conversion Funnel Audit

User prompt: "Our Shopify store DailyBrew sells specialty coffee. We have GA4 installed but can't see where people drop off between product view and purchase. Our conversion rate is 1.2% and we need better funnel tracking."

The agent will:

  • Audit the current GA4 setup and identify missing events in the purchase funnel.
  • Create a funnel tracking plan: product_viewed (with product_name, price, category), add_to_cart, cart_viewed, checkout_started, shipping_selected, payment_submitted, purchase_completed.
  • Provide Shopify-specific GTM implementation using Shopify's data layer.
  • Set up enhanced e-commerce tracking in GA4 with proper product properties.
  • Recommend a validation process using GA4 DebugView to confirm each funnel step fires correctly.

Guidelines

  • Always start with questions, not tools — understand what decisions the data will inform before choosing what to track.
  • Avoid PII in event properties — never pass emails, full names, or other personally identifiable information as event parameters.
  • Test tracking before going live — use GA4 DebugView and GTM Preview Mode to verify every event fires correctly with the right properties.
  • Don't duplicate automatic properties — GA4 already captures page_location, page_referrer, and other standard parameters. Only add custom properties that provide additional context.
  • Document naming conventions upfront — inconsistent event names (mixing signupCompleted with signup_completed) create data headaches that are painful to fix later.
  • Keep UTM parameters lowercase and consistentutm_source=Google and utm_source=google create separate entries in reports. Standardize before launching campaigns.
  • Plan for consent — implement consent mode from day one. Retrofitting cookie consent is much harder than building it in.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analytics Tracking AI skill do?

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.

Why use Analytics Tracking on TypingMind?

Because you install it once and use it with any model. Analytics Tracking is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analytics Tracking in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TerminalSkills/skills/tree/main/skills/analytics-tracking. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analytics Tracking?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analytics Tracking?

As many as you like. As long as a model supports skills, you can use Analytics Tracking with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analytics Tracking AI skill free?

Yes. It is published on GitHub by TerminalSkills under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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