Analytics Tracking
Audit and design analytics instrumentation so events, conversions, and attribution support real product and marketing decisions—not vanity dashboards.
Overview
Analytics Tracking is an agent skill most often used in Grow (also Launch, Validate) that designs and audits measurement so analytics produce decision-grade, validated signals.
Install
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill analytics-trackingWhat is this skill?
- Mandatory Phase 0: Measurement Readiness & Signal Quality Index scored 0–100
- Five weighted categories including Decision Alignment (25) and Event Model Clarity (20)
- Rejects event sprawl, vanity metrics, and unvalidated GA4-as-truth assumptions
- Focus on conversion definition quality and data accuracy before dashboard tweaks
- Decision-grade signals for marketing, product, and growth—not track everything
- Measurement Readiness & Signal Quality Index: 0–100 total score
- 5 scoring categories with defined weights (e.g. Decision Alignment 25, Event Model Clarity 20)
Adoption & trust: 592 installs on skills.sh; 40.1k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your dashboards look busy but you cannot trust conversions, attribution, or event data enough to change pricing, ads, or product bets.
Who is it for?
Solo founders shipping SaaS, content, or e-commerce who own GA4 or similar stacks and need trustworthy funnel and conversion data.
Skip if: Builders who only want a one-line gtag snippet without caring about event models, duplication, or conversion semantics.
When should I use this skill?
Design, audit, or improve analytics tracking so data is reliable and decision-ready across marketing, product, and growth.
What do I get? / Deliverables
You get a scored measurement readiness index, clearer event and conversion definitions, and a prioritized fix list before expanding tracking.
- Signal Quality Index assessment
- Event and conversion model recommendations
- Instrumentation fix priorities
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Analytics tracking is shelved under grow because reliable measurement is what lets you compound users and optimize funnels after ship. The analytics subphase is where instrumentation quality, event models, and conversion definitions are defined and validated.
Where it fits
Score tracking 0–100 and fix duplicate purchase events before scaling ad spend.
Define primary conversion and context fields so campaign UTMs attribute cleanly.
Align checkout and trial events with the pricing experiments you plan to run.
Gate release on conversion definition quality so launch day numbers are comparable week over week.
How it compares
Measurement strategy and audit workflow—not a drop-in MCP connector or a prebuilt analytics dashboard product.
Common Questions / FAQ
Who is analytics-tracking for?
Indie builders and small teams responsible for GA4 or comparable stacks who need reliable instrumentation for growth and product calls.
When should I use analytics-tracking?
In grow when auditing live tracking; at launch when defining conversion events for SEO and campaigns; in validate when choosing what to measure before building dashboards.
Is analytics-tracking safe to install?
The skill guides measurement design; any repo install risk is separate—check the Security Audits panel on this Prism page before adding the package.
SKILL.md
READMESKILL.md - Analytics Tracking
# 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