Analytics Tracking

Analytics Tracking & Measurement Strategy

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/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

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

Use @analytics-tracking 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

Review @analytics-tracking 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

Use @analytics-tracking 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

Review @analytics-tracking 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-awesome-skills-claude/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

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - 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.

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 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)

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

Conversions

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.