modeling-analytics-events

Define and validate versioned analytics event schemas with naming conventions and user context.

Updated Apr 14, 2026
One-click install
npx skills add https://github.com/somachak/claude-code-skills-db --skill modeling-analytics-events
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: modeling-analytics-events
Source: https://github.com/somachak/claude-code-skills-db/tree/main/skills/data/modeling-analytics-events
Command: npx skills add https://github.com/somachak/claude-code-skills-db --skill modeling-analytics-events

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill defines and validates standardized analytics event schemas, including naming conventions and required user context to ensure reliable product insights.

Core Features & Use Cases

  • Versioned event schemas with required properties (user_id, timestamp) and optional metadata.
  • Standardized user context (session_id, OS, browser, country) to enable meaningful attribution.
  • Guidance on sampling, attribution models, and anti-patterns to avoid.

Quick Start

Define a versioned analytics event schema and implement the instrumentation guidelines in your feature rollout.

Frequently Asked Questions about modeling-analytics-events

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I standardize analytics event schemas for reliable product insights?▼

Standardizing analytics event schemas involves defining versioned schemas with required properties like user_id and timestamp, along with standardized user context such as session_id and OS, to ensure consistent and reliable product insights.

What is the best way to name analytics events for attribution modeling?▼

Naming analytics events for attribution requires applying a standardized naming convention framework that enforces property consistency and includes required user context like session_id and country to enable meaningful attribution models.

How do I audit frontend and backend instrumentation for data quality?▼

Auditing frontend and backend instrumentation involves applying a validation framework to existing features, checking for versioned schemas, property consistency, and required user context to identify anti-patterns and ensure data quality.

Can I use this analytics schema framework for feature rollout sampling?▼

This analytics schema framework supports feature rollout by providing specific sampling guidance and attribution models alongside versioned schemas to maintain data quality across product analytics use cases.

What anti-patterns should I avoid when instrumenting versioned event schemas?▼

When instrumenting versioned event schemas, avoid anti-patterns that break property consistency, omit required user context like session_id, or bypass data privacy safeguards, as these compromise attribution and overall data quality.