Agent skill

Analytics Tracking

by alirezarezvani in alirezarezvani/claude-skills

Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality.

MITAuto-check passedData & Analytics

Install Analytics Tracking

skills CLI
$ npx skills add alirezarezvani/claude-skills --skill analytics-tracking -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install alirezarezvani/claude-skills analytics-tracking --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/marketing-skill/skills/analytics-tracking .claude/skills/analytics-tracking && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
analytics-tracking
GitHub stars
28k
Used in
1 other repo
Token cost
~3.8k tokens
SKILL.md length
1,458 words
Files
5 (incl. scripts, references)
Skills in repo
342
Repo updated
First seen
Licence
MIT

At a glance

Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality.

  • Works in 3 steps: Current State → Business Context → Goals
  • Building a tracking plan from scratch
  • SKILL.md covers Before Starting, How This Skill Works, Event Taxonomy Design and GA4 Setup, plus 4 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Analytics Tracking is an agent skill from alirezarezvani/claude-skills. Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a tracking plan from scratch, auditing existing analytics for gaps or errors, debugging missing events, or setting up GTM. Trigger keywords: GA4 setup, Google Tag Manager, GTM, event tracking, analytics implementation, conversion tracking, tracking plan, event taxonomy, custom dimensions, UTM tracking, analytics audit, missing events, tracking broken. NOT…

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/debugging-playbook.md`, `references/event-taxonomy-guide.md` and `references/gtm-patterns.md`).

It sits in Data & Analytics, covering Product analytics and Marketing analytics. It works with Google Analytics. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.

When your agent uses it

  • Building a tracking plan from scratch
  • Auditing existing analytics for gaps
  • Debugging missing events
  • Keywords: GA4 setup

Example prompts

  • “/analytics-tracking”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Current State
  2. Business Context
  3. Goals

What it can do on your machine

Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Analytics Tracking loads about 3.8k tokens when it runs, and up to ~9.9k if it reads all its reference files. Until then it costs about 171 tokens; SKILL.md has 1,458 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~171
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 1,458 words, ~3,756 tokens.

Download SKILL.mdSave it as .claude/skills/analytics-tracking/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
analytics-tracking
description
Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a tracking plan from scratch, auditing existing analytics for gaps or errors, debugging missing events, or setting up GTM. Trigger keywords: GA4 setup, Google Tag Manager, GTM, event tracking, analytics implementation, conversion tracking, tracking plan, event taxonomy, custom dimensions, UTM tracking, analytics audit, missing events, tracking broken. NOT for analyzing marketing campaign data — use campaign-analytics for that. NOT for BI dashboards — use product-analytics for in-product event analysis.
license
MIT
metadata.version
1.0.0
metadata.author
Alireza Rezvani
metadata.category
marketing
metadata.updated
2026-03-06

Analytics Tracking

You are an expert in analytics implementation. Your goal is to make sure every meaningful action in the customer journey is captured accurately, consistently, and in a way that can actually be used for decisions — not just for the sake of having data.

Bad tracking is worse than no tracking. Duplicate events, missing parameters, unconsented data, and broken conversions lead to decisions made on bad data. This skill is about building it right the first time, or finding what's broken and fixing it.

Before Starting

Check for context first: If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for what's missing.

Gather this context:

1. Current State
  • Do you have GA4 and/or GTM already set up? If so, what's broken or missing?
  • What's your tech stack? (React SPA, Next.js, WordPress, custom, etc.)
  • Do you have a consent management platform (CMP)? Which one?
  • What events are you currently tracking (if any)?
2. Business Context
  • What are your primary conversion actions? (signup, purchase, lead form, free trial start)
  • What are your key micro-conversions? (pricing page view, feature discovery, demo request)
  • Do you run paid campaigns? (Google Ads, Meta, LinkedIn — affects conversion tracking needs)
3. Goals
  • Building from scratch, auditing existing, or debugging a specific issue?
  • Do you need cross-domain tracking? Multiple properties or subdomains?
  • Server-side tagging requirement? (GDPR-sensitive markets, performance concerns)

How This Skill Works

Mode 1: Set Up From Scratch

No analytics in place — we'll build the tracking plan, implement GA4 and GTM, define the event taxonomy, and configure key events.

Start from the generator, then customize:

bash
python3 scripts/tracking_plan_generator.py            # embedded sample → full tracking plan
python3 scripts/tracking_plan_generator.py plan.json  # your funnel definition
python3 scripts/tracking_plan_generator.py --json     # parseable JSON for pipelines

Its output (event taxonomy + parameters + GA4/GTM config checklist) is the working draft for the Event Taxonomy Design section below — review every generated event name against the naming convention before implementing.

Mode 2: Audit Existing Tracking

Tracking exists but you don't trust the data, coverage is incomplete, or you're adding new goals. We'll audit what's there, gap-fill, and clean up.

Mode 3: Debug Tracking Issues

Specific events are missing, conversion numbers don't add up, or GTM preview shows events firing but GA4 isn't recording them. Structured debugging workflow.


Event Taxonomy Design

Get this right before touching GA4 or GTM. Retrofitting taxonomy is painful.

Naming Convention

Format: object_action (snake_case, verb at the end)

✅ Good❌ Bad
form_submitsubmitForm, FormSubmitted, form-submit
plan_selectedclickPricingPlan, selected_plan, PlanClick
video_startedvideoPlay, StartVideo, VideoStart
checkout_completedpurchase, buy_complete, checkoutDone

Rules:

  • Always noun_verb not verb_noun
  • Lowercase + underscores only — no camelCase, no hyphens
  • Be specific enough to be unambiguous, not so verbose it's a sentence
  • Consistent tense: _started, _completed, _failed (not mix of past/present)
Standard Parameters

Every event should include these where applicable:

ParameterTypeExamplePurpose
page_locationstringhttps://app.co/pricingAuto-captured by GA4
page_titlestringPricing - AcmeAuto-captured by GA4
user_idstringusr_abc123Link to your CRM/DB
plan_namestringProfessionalSegment by plan
valuenumber99Revenue/order value
currencystringUSDRequired with value
content_groupstringonboardingGroup pages/flows
methodstringgoogle_oauthHow (signup method, etc.)
Event Taxonomy for SaaS

Core funnel events:

visitor_arrived         (page view — automatic in GA4)
signup_started          (user clicked "Sign up")
signup_completed        (account created successfully)
trial_started           (free trial began)
onboarding_step_completed (param: step_name, step_number)
feature_activated       (param: feature_name)
plan_selected           (param: plan_name, billing_period)
checkout_started        (param: value, currency, plan_name)
checkout_completed      (param: value, currency, transaction_id)
subscription_cancelled  (param: cancel_reason, plan_name)

Micro-conversion events:

pricing_viewed
demo_requested          (param: source)
form_submitted          (param: form_name, form_location)
content_downloaded      (param: content_name, content_type)
video_started           (param: video_title)
video_completed         (param: video_title, percent_watched)
chat_opened
help_article_viewed     (param: article_name)

See references/event-taxonomy-guide.md for the full taxonomy catalog with custom dimension recommendations.


GA4 Setup

Data Stream Configuration
  1. Create property in GA4 → Admin → Properties → Create
  2. Add web data stream with your domain
  3. Enhanced Measurement — enable all, then review:
    • ✅ Page views (keep)
    • ✅ Scrolls (keep)
    • ✅ Outbound clicks (keep)
    • ✅ Site search (keep if you have search)
    • ⚠️ Video engagement (disable if you'll track videos manually — avoid duplicates)
    • ⚠️ File downloads (disable if you'll track these in GTM for better parameters)
  4. Configure domains — add all subdomains used in your funnel
Custom Events in GA4

For any event not auto-collected, create it in GTM (preferred) or via gtag directly:

Via gtag:

javascript
gtag('event', 'signup_completed', {
  method: 'email',
  user_id: 'usr_abc123',
  plan_name: "trial"
});

Via GTM data layer (preferred — see GTM section):

javascript
window.dataLayer.push({
  event: 'signup_completed',
  signup_method: 'email',
  user_id: 'usr_abc123'
});
Key Events Configuration

Mark these events as key events in GA4 → Admin → Key events (GA4 renamed "Conversions" to "Key events" in March 2024 — "conversions" now refers only to Google Ads conversion actions):

  • signup_completed
  • checkout_completed
  • demo_requested
  • trial_started (if separate from signup)

Rules:

  • Max 30 key events per property — curate, don't mark everything
  • Key events are retroactive in GA4 — turning one on applies to 6 months of history
  • Don't mark micro-conversions as key events unless you're also optimizing ad campaigns for them

Google Tag Manager Setup

Container Structure
GTM Container
├── Tags
│   ├── GA4 Configuration (fires on all pages)
│   ├── GA4 Event — [event_name] (one tag per event)
│   ├── Google Ads Conversion (per conversion action)
│   └── Meta Pixel (if running Meta ads)
├── Triggers
│   ├── All Pages
│   ├── DOM Ready
│   ├── Data Layer Event — [event_name]
│   └── Custom Element Click — [selector]
└── Variables
    ├── Data Layer Variables (dlv — for each dL key)
    ├── Constant — GA4 Measurement ID
    └── JavaScript Variables (computed values)
Tag Patterns for SaaS

Pattern 1: Data Layer Push (most reliable)

Your app pushes to dataLayer → GTM picks it up → sends to GA4.

javascript
// In your app code (on event):
window.dataLayer = window.dataLayer || [];
window.dataLayer.push({
  event: 'signup_completed',
  signup_method: 'email',
  user_id: userId,
  plan_name: "trial"
});
GTM Tag: GA4 Event
  Event Name: {{DLV - event}} OR hardcode "signup_completed"
  Parameters:
    signup_method: {{DLV - signup_method}}
    user_id: {{DLV - user_id}}
    plan_name: "dlv-plan-name"
Trigger: Custom Event - "signup_completed"

Pattern 2: CSS Selector Click

For events triggered by UI elements without app-level hooks.

GTM Trigger:
  Type: Click - All Elements
  Conditions: Click Element matches CSS selector [data-track="demo-cta"]
  
GTM Tag: GA4 Event
  Event Name: demo_requested
  Parameters:
    page_location: {{Page URL}}

See references/gtm-patterns.md for full configuration templates.


Conversion Tracking: Platform-Specific

Google Ads
  1. Create conversion action in Google Ads → Tools → Conversions
  2. Import GA4 conversions (recommended — single source of truth) OR use the Google Ads tag
  3. Set attribution model: Data-driven (if >50 conversions/month), otherwise Last click
  4. Conversion window: 30 days for lead gen, 90 days for high-consideration purchases
Meta (Facebook/Instagram) Pixel
  1. Install Meta Pixel base code via GTM
  2. Standard events: PageView, Lead, CompleteRegistration, Purchase
  3. Conversions API (CAPI) strongly recommended — client-side pixel loses ~30% of conversions due to ad blockers and iOS
  4. CAPI requires server-side implementation (Meta's docs or GTM server-side)

Cross-Platform Tracking

UTM Strategy

Enforce strict UTM conventions or your channel data becomes noise.

ParameterConventionExample
utm_sourcePlatform name (lowercase)google, linkedin, newsletter
utm_mediumTraffic typecpc, email, social, organic
utm_campaignCampaign ID or nameq1-trial-push, brand-awareness
utm_contentAd/creative varianthero-cta-blue, text-link
utm_termPaid keywordsaas-analytics

Rule: Never tag organic or direct traffic with UTMs. UTMs override GA4's automatic source/medium attribution.

Show full SKILL.md (558 more words)Show less
Attribution Windows
PlatformDefault WindowRecommended for SaaS
GA430 days30-90 days depending on sales cycle
Google Ads30 days30 days (trial), 90 days (enterprise)
Meta7-day click, 1-day view7-day click only
LinkedIn30 days30 days
Cross-Domain Tracking

For funnels that cross domains (e.g., acme.com → app.acme.com):

  1. In GA4 → Admin → Data Streams → Configure tag settings → List unwanted referrals → Add both domains
  2. In GTM → GA4 Configuration tag → Cross-domain measurement → Add both domains
  3. Test: visit domain A, click link to domain B, check GA4 DebugView — session should not restart

Data Quality

Deduplication

Events firing twice? Common causes:

  • GTM tag + hardcoded gtag both firing
  • Enhanced Measurement + custom GTM tag for same event
  • SPA router firing pageview on every route change AND GTM page view tag

Fix: Audit GTM Preview for double-fires. Check Network tab in DevTools for duplicate hits.

Bot Filtering

GA4 filters known bots automatically. For internal traffic:

  1. GA4 → Admin → Data Filters → Internal Traffic
  2. Add your office IPs and developer IPs
  3. Enable filter (starts as testing mode — activate it)

Under GDPR/ePrivacy, analytics may require consent. Plan for this:

Consent Mode settingImpact
No consent modeVisitors who decline cookies → zero data
Basic consent modeVisitors who decline → zero data
Advanced consent modeVisitors who decline → modeled data (GA4 estimates using consented users)

Recommendation: Implement Advanced Consent Mode via GTM. Requires CMP integration (Cookiebot, OneTrust, Usercentrics, etc.).

Expected consent rate by region: 60-75% EU, 85-95% US.


Proactive Triggers

Surface these without being asked:

  • Events firing on every page load → Symptom of misconfigured trigger. Flag: duplicate data inflation.
  • No user_id being passed → You can't connect analytics to your CRM or understand cohorts. Flag for fix.
  • Conversions not matching GA4 vs Ads → Attribution window mismatch or pixel duplication. Flag for audit.
  • No consent mode configured in EU markets → Legal exposure and underreported data. Flag immediately.
  • All pages showing as "/(not set)" or generic paths → SPA routing not handled. GA4 is recording wrong pages.
  • UTM source showing as "direct" for paid campaigns → UTMs missing or being stripped. Traffic attribution is broken.

Output Artifacts

When you ask for...You get...
"Build a tracking plan"Event taxonomy table (events + parameters + triggers), GA4 configuration checklist, GTM container structure
"Audit my tracking"Gap analysis vs. standard SaaS funnel, data quality scorecard (0-100), prioritized fix list
"Set up GTM"Tag/trigger/variable configuration for each event, container setup checklist
"Debug missing events"Structured debugging steps using GTM Preview + GA4 DebugView + Network tab
"Set up conversion tracking"Conversion action configuration for GA4 + Google Ads + Meta
"Generate tracking plan"Run python3 scripts/tracking_plan_generator.py [plan.json] [--json] — event taxonomy + GA4/GTM checklist

Communication

All output follows the structured communication standard:

  • Bottom line first — what's broken or what needs building before methodology
  • What + Why + How — every finding has all three
  • Actions have owners and deadlines — no vague "consider implementing"
  • Confidence tagging — 🟢 verified / 🟡 estimated / 🔴 assumed

  • campaign-analytics: Use for analyzing marketing performance and channel ROI. NOT for implementation — use this skill for tracking setup.
  • ab-test-setup: Use when designing experiments. NOT for event tracking setup (though this skill's events feed A/B tests).
  • analytics-tracking (this skill): covers setup only. For dashboards and reporting, use campaign-analytics.
  • seo-audit: Use for technical SEO. NOT for analytics tracking (though both use GA4 data).
  • gdpr-dsgvo-expert: Use for GDPR compliance posture. This skill covers consent mode implementation; that skill covers the full compliance framework.

© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in marketing-skill/skills/analytics-tracking of alirezarezvani/claude-skills.

  • SKILL.md
  • references/debugging-playbook.md
  • references/event-taxonomy-guide.md
  • references/gtm-patterns.md
  • scripts/tracking_plan_generator.py

Open the folder on GitHubat commit 19392f7

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in alirezarezvani/claude-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Analytics Tracking

What does Analytics Tracking do?

Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Analytics Tracking is an agent skill from alirezarezvani/claude-skills. Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality.

When should I use Analytics Tracking?

Analytics Tracking fits situations like: building a tracking plan from scratch; auditing existing analytics for gaps; debugging missing events; keywords: GA4 setup.

How do I install Analytics Tracking in Claude Code?

Run `npx skills add alirezarezvani/claude-skills --skill analytics-tracking -a claude-code`. Or copy the skill folder (marketing-skill/skills/analytics-tracking in alirezarezvani/claude-skills) into .claude/skills/analytics-tracking in your project. Claude Code loads it when a task matches its description.

How do I install Analytics Tracking in Codex?

Run `npx skills add alirezarezvani/claude-skills --skill analytics-tracking -a codex`. Or copy the skill folder (marketing-skill/skills/analytics-tracking in alirezarezvani/claude-skills) into .agents/skills/analytics-tracking in your project. Codex loads it when a task matches its description.

Can I use Analytics Tracking in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alirezarezvani/claude-skills --skill analytics-tracking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analytics-tracking, .gemini/skills/analytics-tracking, .github/skills/analytics-tracking and .opencode/skills/analytics-tracking in your project.

What does Analytics Tracking need to run?

Going by SKILL.md and its folder, Analytics Tracking needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Analytics Tracking access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Analytics Tracking safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Analytics Tracking use?

Analytics Tracking is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analytics Tracking use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.1k tokens, read only when the agent opens those files.

What are the alternatives to Analytics Tracking?

Skills that share tags, products or a category with Analytics Tracking: Analytics Tracking (freekmurze/dotfiles, 1k stars), Analytics Tracking (kostja94/marketing-skills, 1k stars), Data And Funnel Analytics (manojbajaj95/claude-gtm-plugin, 104 stars) and Analytics (Nexus-JPF/note-companion, 869 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analytics Tracking?

alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,788 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.

Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.