A
analytics-tracking

analytics-tracking

coreyhaines31
2026-04-06

When the user wants to set up, improve, or audit analytics tracking and measurement.


Introduction and Core Concept

The analytics-tracking skill transforms the AI into an expert analytics implementation consultant. It addresses the common issue of teams drowning in meaningless data (“vanity metrics”) by enforcing a strategic, decision-first approach to event tracking and measurement before any tags or analytics scripts are implemented.

Core Concept

Track for decisions, not just for data. By working backward from business questions and enforcing strict naming conventions, this skill ensures that every tracked event provides actionable insights.

Installation and Usage Guide

https://github.com/coreyhaines31/marketingskills

Workflow Analysis (SOP)

Read original SKILL.md

  • Initial Context Assessment: The AI checks for existing marketing context to avoid redundant questions, then evaluates the business context (what decisions need informing), current state of tracking, and technical/privacy constraints.
  • Decision-Driven Tracking: Enforces the principle of “Start with the Questions.” The AI works backwards from what actions the user will take based on the data, filtering out useless metrics.
  • Tracking Plan Framework Generation: The AI must output a structured tracking plan table (Event Name | Category | Properties | Trigger | Notes) before writing implementation code.
  • Naming Convention Enforcement: Mandates a strict Object-Action naming format (e.g., signup_completed instead of Clicked Sign Up) in lowercase with underscores, ensuring database cleanliness.

Skill Design Evaluation

  • Strengths of SOP Integration: Focuses on decision-driven tracking by working backward from business questions. Enforces strict event naming conventions (Object-Action format) to maintain data consistency.
  • Potential Limitations: The rigid adherence to the Object-Action naming format might conflict with existing data schemas in organizations that use different conventions.
  • What Makes a Good Skill: Prioritizes data quality over quantity by explicitly directing the AI to include context in event properties rather than creating overly specific event names.