Why is it important to read the full schema first?

Reading the full schema ensures that all available attributes are identified and avoids errors when dealing with large, complex CDP environments with dozens of columns.

Can I analyze behavior data along with customer attributes?

Absolutely. The skill helps you identify specific behavior tables and provides patterns to join them with the master customers table for comprehensive analysis.

Does this skill support Treasure Data's specific SQL syntax?

Yes, it provides specialized guidance for Trino SQL as implemented in Treasure Data, including time functions like td_interval and td_time_parse.

How do I get the schema for a specific parent segment?

You can use the command 'tdx ps desc [Segment Name] -o schema.json' to export and read the full database structure, including customer and behavior tables.

How are complex array columns handled in queries?

The skill utilizes CROSS JOIN UNNEST for array analysis and provides specific syntax for the 'contains' function to ensure high-performance queries.

Treasure Data Parent Segment Analysis

Name: Treasure Data Parent Segment Analysis
Author: treasure-data

bytreasure-data

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Ciencia de Datos y ML

Queries and analyzes Treasure Data CDP parent segments to uncover deep customer insights and behavioral patterns.

This skill enables developers and data analysts to explore and query complex Customer Data Platform (CDP) schemas within Treasure Data directly through Claude. It automates the discovery of parent segment schemas—including master customer tables and behavior-specific data—while providing optimized SQL patterns for array unnesting and time-series analysis. It is particularly useful for building detailed customer reports, validating audience attributes, and performing ad-hoc behavioral analysis efficiently within a command-line workflow.

Características Principales

01Automated schema discovery and documentation for CDP parent segments

02Optimized Trino SQL query generation for customer and behavior tables

03Data quality auditing via null-ratio checks and attribute exploration

04Advanced array analysis support using CROSS JOIN UNNEST patterns

05Time-series data handling with Treasure Data-specific time functions

0616 GitHub stars

Casos de Uso

01Analyzing purchase behaviors by correlating master customer records with transaction tables

02Auditing data quality by checking null ratios and schema consistency across audience segments

03Exploring customer attribute distributions within a specific 'Customer 360' segment

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add treasure-data/td-skills parent-segment-analysis

For use in Claude.ai and ChatGPT

Download Skill