marketing-analytics-lab-engine

Ingest and validate marketing campaign data into a canonical schema.

8|Updated May 16, 2026

Search Tags:#data ingestion #marketing analytics #campaign analysis #data schema #Pydantic validation

Ready to run

npx skills add https://github.com/Aradotso/marketing-skills --skill marketing-analytics-lab-engine

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: marketing-analytics-lab-engine
Source: https://github.com/Aradotso/marketing-skills/tree/main/skills/marketing-analytics-lab-engine
Command: npx skills add https://github.com/Aradotso/marketing-skills --skill marketing-analytics-lab-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, pydantic, pytest, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the complex challenge of marketing data ingestion and analysis across various platforms by providing a unified schema and canonical data model.

Core Features & Use Cases

Unified Schema: A single source of truth for campaign data across multiple advertising platforms.
Data Ingestion: Supports ingestions from Google Ads, Meta Ads, and other platforms.
Validation: Strict validation of campaign records with Pydantic for consistency.
Data Analysis: Set up marketing analytics pipelines with standardized campaign data.
Use Case: If you have marketing data from various sources and need a standardized approach to analysis, this Skill provides a solution.

Quick Start

Use the marketing-analytics-lab-engine skill to ingest campaign data from Google Ads into the unified data model.

Frequently Asked Questions about marketing-analytics-lab-engine

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

FAQPage Schema

How do I standardize cross-platform marketing data for consistent analysis?▼

To standardize cross-platform marketing data, you ingest campaign records from platforms like Google Ads and Meta Ads into a unified schema. This enforces a canonical data model, ensuring consistent analysis across all advertising sources.

How does Pydantic validation work for marketing campaign data ingestion?▼

Pydantic validation enforces a strict canonical schema during marketing campaign data ingestion. It validates each campaign record for consistency before processing, ensuring only correctly structured data enters your analytics pipeline.

Can I use pandas to analyze Google Ads and Meta Ads data in a unified schema?▼

Yes, you can use pandas to analyze Google Ads and Meta Ads data once it is ingested into the unified schema. The Skill processes validated campaign records using pandas, enabling cross-platform data analysis within a standardized model.

What is the best way to manage cross-platform campaign data for marketing analytics?▼

The best way to manage cross-platform campaign data is to enforce a canonical data model during ingestion. Validating records from various platforms into a single source of truth allows you to set up consistent marketing analytics pipelines.

Do I need pytest to set up marketing analytics pipelines with standardized campaign data?▼

Yes, pytest is required to test and validate your marketing analytics pipelines. Alongside pandas and pydantic, pytest ensures your data ingestion scripts and unified schema enforcement function correctly before analyzing campaign data.

Why does my cross-platform marketing data analysis show inconsistent campaign metrics?▼

Inconsistent marketing data analysis often results from lacking a unified schema across platforms. By enforcing a canonical data model with strict validation during ingestion, you ensure consistent campaign metrics from sources like Google Ads and Meta Ads.