What file types does this skill analyze?

The skill specifically scans and interprets .dig (Digdag) workflow files, .sql query files, and .yml configuration files within your project directory.

Why does this skill require codebase access?

To provide accurate documentation, the skill must extract real table names, SQL logic, and watermark configurations directly from your files rather than using generic placeholders.

Can I use the output for Confluence?

Yes, the skill generates structured Markdown that follows production-ready templates, making it easy to copy into Confluence, GitHub wikis, or other internal documentation tools.

What is a hist-union workflow?

A hist-union workflow is a data engineering strategy used to combine historical archived data with new incremental data into a single unified table structure.

APS Hist-Union Documentation Expert

Name: APS Hist-Union Documentation Expert
Author: treasure-data

bytreasure-data

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Aprendizaje y Documentación

Generates comprehensive, production-ready documentation for data workflows that combine historical and incremental table strategies.

Acerca de

This skill automates the creation of detailed technical documentation for hist-union workflows by analyzing actual codebase artifacts like .dig, .sql, and .yml files. It extracts real schema definitions, watermark logic, and SQL UNION patterns to produce professional documentation for Confluence or internal wikis, ensuring that documentation accurately reflects the live implementation without relying on generic placeholders. It is specifically designed for developers managing complex data pipelines who need to maintain clear, accurate records of their data merging and backfill processes.

Características Principales

2 GitHub stars
Generates architectural overviews including directory trees and database purposes
Maps watermark management and inc_log table structures automatically from source code
Produces working SQL examples for incremental loads and full reloads directly from the codebase
Documents historical and incremental data combination strategies and schema validation
Analyzes Treasure Data workflows and SQL files to extract real implementation logic

Casos de Uso

Creating standardized Confluence documentation for historical data backfill processes
Validating and documenting schema consistency checks within hist-union layers
Documenting complex data pipeline workflows for team knowledge sharing

Acerca de

Características Principales

2 GitHub stars
Generates architectural overviews including directory trees and database purposes
Maps watermark management and inc_log table structures automatically from source code
Produces working SQL examples for incremental loads and full reloads directly from the codebase
Documents historical and incremental data combination strategies and schema validation
Analyzes Treasure Data workflows and SQL files to extract real implementation logic

Casos de Uso

Creating standardized Confluence documentation for historical data backfill processes
Validating and documenting schema consistency checks within hist-union layers
Documenting complex data pipeline workflows for team knowledge sharing