analytics-tracking-v3

---
name: analytics-tracking-v3
description: "Analytics Tracking & Measurement Strategy workflow skill. Use this skill when the user needs Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off."
version: "0.0.1"
category: data-ai
tags: ["analytics-tracking-v3", "analytics-tracking", "design", "audit", "and", "improve", "analytics", "tracking"]
complexity: intermediate
risk: safe
tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"]
source: community
author: "sickn33"
date_added: "2026-04-26"
date_updated: "2026-04-26"
---

# Analytics Tracking & Measurement Strategy

## Overview

This public intake copy packages `plugins/antigravity-bundle-data-analytics/skills/analytics-tracking` from `https://github.com/sickn33/antigravity-awesome-skills` into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the `external_source` block in `metadata.json` plus `ORIGIN.md` as the provenance anchor for review.

# 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. ---

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Phase 1: Context & Decision Definition, Event Model Design, Conversion Strategy, GA4 & GTM (Implementation Guidance), UTM & Attribution Discipline, Validation & Debugging.

## When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

- This skill is applicable to execute the workflow or actions described in the overview.
- Use when the request clearly matches the imported source intent: Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
- Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
- Use when provenance needs to stay visible in the answer, PR, or review packet.
- Use when copied upstream references, examples, or scripts materially improve the answer.
- Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

## Operating Table

| Situation | Start here | Why it matters |
| --- | --- | --- |
| First-time use | `metadata.json` | Confirms repository, branch, commit, and imported path through the `external_source` block before touching the copied workflow |
| Provenance review | `ORIGIN.md` | Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | `SKILL.md` | Starts with the smallest copied file that materially changes execution |
| Supporting context | `SKILL.md` | Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | `## Related Skills` | Helps the operator switch to a stronger native skill when the task drifts |

## Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

### Imported Workflow Notes

#### Imported: 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

---

## Examples

### Example 1: Ask for the upstream workflow directly

```text
Use @analytics-tracking-v3 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
```

**Explanation:** This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

### Example 2: Ask for a provenance-grounded review

```text
Review @analytics-tracking-v3 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
```

**Explanation:** Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

### Example 3: Narrow the copied support files before execution

```text
Use @analytics-tracking-v3 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
```

**Explanation:** This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

### Example 4: Build a reviewer packet

```text
Review @analytics-tracking-v3 using the copied upstream files plus provenance, then summarize any gaps before merge.
```

**Explanation:** This is useful when the PR is waiting for human review and you want a repeatable audit packet.



## Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

- What you need to know
- What action you’ll take
- What signal proves it
- cosmetic clicks
- redundant events
- UI noise
- intent

### Imported Operating Notes

#### Imported: 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.

---

## Troubleshooting

### Problem: The operator skipped the imported context and answered too generically

**Symptoms:** The result ignores the upstream workflow in `plugins/antigravity-bundle-data-analytics/skills/analytics-tracking`, fails to mention provenance, or does not use any copied source files at all.
**Solution:** Re-open `metadata.json`, `ORIGIN.md`, and the most relevant copied upstream files. Check the `external_source` block first, then restate the provenance before continuing.

### Problem: The imported workflow feels incomplete during review

**Symptoms:** Reviewers can see the generated `SKILL.md`, but they cannot quickly tell which references, examples, or scripts matter for the current task.
**Solution:** Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

### Problem: The task drifted into a different specialization

**Symptoms:** The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
**Solution:** Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.



## Related Skills

- `@ab-test-setup-v3` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@algolia-search-v3` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@algorithmic-art-v3` - Use when the work is better handled by that native specialization after this imported skill establishes context.
- `@azd-deployment-v3` - Use when the work is better handled by that native specialization after this imported skill establishes context.

## Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

| Resource family | What it gives the reviewer | Example path |
| --- | --- | --- |
| `references` | copied reference notes, guides, or background material from upstream | `references/n/a` |
| `examples` | worked examples or reusable prompts copied from upstream | `examples/n/a` |
| `scripts` | upstream helper scripts that change execution or validation | `scripts/n/a` |
| `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` |
| `assets` | supporting assets or schemas copied from the source package | `assets/n/a` |



### Imported Reference Notes

#### Imported: 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

---

#### Imported: 🔢 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.

---

#### Imported: 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

---

#### Imported: 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

---

#### Imported: 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

---

#### Imported: UTM & Attribution Discipline

### UTM Rules

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

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

---

#### Imported: Validation & Debugging

### Required Validation

* Real-time verification
* Duplicate detection
* Cross-browser testing
* Mobile testing
* Consent-state testing

### Common Failure Modes

* double firing
* missing properties
* broken attribution
* PII leakage
* inflated conversions

---

#### Imported: Privacy & Compliance

* Consent before tracking where required
* Data minimization
* User deletion support
* Retention policies reviewed

Analytics that violate trust undermine optimization.

---

#### Imported: Output Format (Required)

### Measurement Strategy Summary

* Measurement Readiness Index score + verdict
* Key risks and gaps
* Recommended remediation order

---

### Tracking Plan

| Event | Description | Properties | Trigger | Decision Supported |
| ----- | ----------- | ---------- | ------- | ------------------ |

---

### Conversions

| Conversion | Event | Counting | Used By |
| ---------- | ----- | -------- | ------- |

---

### Implementation Notes

* Tool-specific setup
* Ownership
* Validation steps

---

#### Imported: Questions to Ask (If Needed)

1. What decisions depend on this data?
2. Which metrics are currently trusted or distrusted?
3. Who owns analytics long term?
4. What compliance constraints apply?
5. What tools are already in place?

---

#### Imported: Limitations

- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.