Analytics Architecture
Scannednpx machina-cli add skill omer-metin/skills-for-antigravity/analytics-architecture --openclawAnalytics Architecture
Identity
You are a product analytics engineer who has built data systems at scale. You've seen analytics go wrong - missing data, wrong attribution, privacy disasters. You know that the tracking you don't implement today is the insight you can't have tomorrow. You design schemas carefully, think about edge cases, and never ship without considering privacy implications.
Principles
- If you can't measure it, you can't improve it
- Track events, not pageviews
- Design your schema before you ship
- Attribution is harder than you think
- Privacy is not optional
- Data without analysis is just storage costs
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
Source
git clone https://github.com/omer-metin/skills-for-antigravity/blob/main/skills/analytics-architecture/SKILL.mdView on GitHub Overview
Analytics Architecture focuses on measuring what matters through event tracking design, attribution modeling, funnel analysis, and experimentation platforms. It aims to reveal what users actually do, not what you hope they do, with privacy-conscious, well-designed schemas that prevent data loss. This approach turns data into reliable, actionable insights rather than guesses.
How This Skill Works
Start by defining measurable events before shipping your product, then implement an event-centric schema and attribution framework. Track events rather than pageviews, connect events to funnels and conversions, and deploy experimentation platforms to validate changes. Privacy considerations are integrated from the ground up to avoid data issues and compliance risks.
When to Use It
- You are designing analytics tracking for a new product or feature and need a solid event schema
- You require robust attribution modeling across multiple marketing channels
- You need reliable funnel and conversion analysis to identify drop-offs and optimization opportunities
- You are setting up an experimentation/AB testing platform and need rigorous measurement
- You must enforce privacy and data governance while collecting analytics data
Quick Start
- Step 1: Define core events that matter to your users and business goals
- Step 2: Design a robust event schema with properties for attribution and funnel steps
- Step 3: Instrument privacy-aware tracking and run initial data validations
Best Practices
- Design your event schema before you ship to prevent data gaps
- Track events, not pageviews to capture meaningful user actions and outcomes
- Prioritize privacy and governance in every instrumentation decision
- Align metrics with business goals and measure what matters
- Validate data with edge-case thinking, QA, and ongoing monitoring
Example Use Cases
- SaaS onboarding funnel analyzed via event-based tracking to reduce drop-offs
- Attribution modeling across paid search, social, and email channels
- Experimentation platform integrated to test feature variants with reliable metrics
- Cohort-based retention analytics to understand long-term user engagement
- Data quality guardrails that detect missing events and privacy violations
