Agent skill · data analytics · sickn33
analytics-tracking
Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
Why this skill is useful
Provides a structured framework for evaluating and improving analytics tracking systems, which enhances the AI's ability to generate reliable data strategies.
What it needs
About 5k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
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 Evidence and Optional Review Rubric Before changing tracking, inspect actual event definitions and sample events. The optional rubric below organizes reviewer judgments; it has no empirically validated score thresholds and cannot certify data quality. Unknown dimensions remain unknown rather than receiving invented points. Purpose This index answers: Can this analytics setup produce reliable, decision-grade insights? Use it to identify possible: 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/analytics-tracking--a07f80