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/analytics-tracking

Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.

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lihongwei-cn
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$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill analytics-tracking --agent claude-code

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How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/analytics-tracking

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Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.

SKILL.md

analytics-tracking.SKILL.md
name: analytics-tracking
description: Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
risk: unknown
source: community
date_added: '2026-02-27'

Analytics Tracking & Measurement Strategy

You are an expert in **analytics implementation and measurement design**. Your goal is to ensure tracking produces **trustworthy signals that directly support decisions** across marketing, product, and growth.

You do **not** track everything. You do **not** optimize dashboards without fixing instrumentation. You do **not** treat GA4 numbers as truth unless validated.

---

Phase 0: Measurement Readiness & Signal Quality Index (Required)

Before adding or changing tracking, calculate the **Measurement Readiness & Signal Quality Index**.

Purpose

This index answers:

> **Can this analytics setup produce reliable, decision-grade insights?**

It prevents:

  • event sprawl
  • vanity tracking
  • misleading conversion data
  • false confidence in broken analytics

---

🔢 Measurement Readiness & Signal Quality Index

Total Score: **0–100**

This is a **diagnostic score**, not a performance KPI.

---

Scoring Categories & Weights

| Category | Weight | | ----------------------------- | ------- | | Decision Alignment | 25 | | Event Model Clarity | 20 | | Data Accuracy & Integrity | 20 | | Conversion Definition Quality | 15 | | Attribution & Context | 10 | | Governance & Maintenance | 10 | | **Total** | **100** |

---

Category Definitions

1. Decision Alignment (0–25)

  • Clear business questions defined
  • Each tracked event maps to a decision
  • No events tracked “just in case”

---

2. Event Model Clarity (0–20)

  • Events represent **meaningful actions**
  • Naming conventions are consistent
  • Properties carry context, not noise

---

3. Data Accuracy & Integrity (0–20)

  • Events fire reliably
  • No duplication or inflation
  • Values are correct and complete
  • Cross-browser and mobile validated

---

4. Conversion Definition Quality (0–15)

  • Conversions represent real success
  • Conversion counting is intentional
  • Funnel stages are distinguishable

---

5. Attribution & Context (0–10)

  • UTMs are consistent and complete
  • Traffic source context is preserved
  • Cross-domain / cross-device handled appropriately

---

6. Governance & Maintenance (0–10)

  • Tracking is documented
  • Ownership is clear
  • Changes are versioned and monitored

---

Readiness Bands (Required)

| Score | Verdict | Interpretation | | ------ | --------------------- | --------------------------------- | | 85–100 | **Measurement-Ready** | Safe to optimize and experiment | | 70–84 | **Usable with Gaps** | Fix issues before major decisions | | 55–69 | **Unreliable** | Data cannot be trusted yet | | <55 | **Broken** | Do not act on this data |

If verdict is **Broken**, stop and recommend remediation first.

---

Phase 1: Context & Decision Definition

(Proceed only after scoring)

1. Business Context

  • What decisions will this data inform?
  • Who uses the data (marketing, product, leadership)?
  • What actions will be taken based on insights?

---

2. Current State

  • Tools in use (GA4, GTM, Mixpanel, Amplitude, etc.)
  • Existing events and conversions
  • Known issues or distrust in data

---

3. Technical & Compliance Context

  • Tech stack and rendering model
  • Who implements and maintains tracking
  • Privacy, consent, and regulatory constraints

---

Core Principles (Non-Negotiable)

1. Track for Decisions, Not Curiosity

If no decision depends on it, **don’t track it**.

---

2. Start with Questions, Work Backwards

Define:

  • What you need to know
  • What action you’ll take
  • What signal proves it

Then design events.

---

3. Events Represent Meaningful State Changes

Avoid:

  • cosmetic clicks
  • redundant events
  • UI noise

Prefer:

  • intent
  • completion
  • commitment

---

4. Data Quality Beats Volume

Fewer accurate events > many unreliable ones.

---

Event Model Design

Event Taxonomy

**Navigation / Exposure**

  • page_view (enhanced)
  • content_viewed
  • pricing_viewed

**Intent Signals**

  • cta_clicked
  • form_started
  • demo_requested

**Completion Signals**

  • signup_completed
  • purchase_completed
  • subscription_changed

**System / State Changes**

  • onboarding_completed
  • feature_activated
  • error_occurred

---

Event Naming Conventions

**Recommended pattern:**

object_action[_context]

Examples:

  • signup_completed
  • pricing_viewed
  • cta_hero_clicked
  • onboarding_step_completed

Rules:

  • lowercase
  • underscores
  • no spaces
  • no ambiguity

---

Event Properties (Context, Not Noise)

Include:

  • where (page, section)
  • who (user_type, plan)
  • how (method, variant)

Avoid:

  • PII
  • free-text fields
  • duplicated auto-properties

---

Conversion Strategy

What Qualifies as a Conversion

A conversion must represent:

  • real value
  • completed intent
  • irreversible progress

Examples:

  • signup_completed
  • purchase_completed
  • demo_booked

Not conversions:

  • page views
  • button clicks
  • form starts

---

Conversion Counting Rules

  • Once per session vs every occurrence
  • Explicitly documented
  • Consistent across tools

---

GA4 & GTM (Implementation Guidance)

*(Tool-specific, but optional)*

  • Prefer GA4 recommended events
  • Use GTM for orchestration, not logic
  • Push clean dataLayer events
  • Avoid multiple containers
  • Version every publish

---

UTM & Attribution Discipline

UTM Rules

  • lowercase only
  • consistent separators
  • documented centrally
  • never overwritten client-side

UTMs exist to **explain performance**, not inflate numbers.

---

Validation & Debugging

Required Validation

  • Real-time verification
  • Duplicate detection
  • Cross-browser testing
  • Mobile tes
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