dlt

Automate data loading, normalization, and schema management for SignalRoom ETL pipelines.

Updated Dec 19, 2025

Search Tags:#pipeline #data ingestion #etl #schema evolution #incremental loading

Ready to run

npx skills add https://github.com/mmbianco78/signalroom --skill dlt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dlt
Source: https://github.com/mmbianco78/signalroom/tree/main/.claude/skills/dlt
Command: npx skills add https://github.com/mmbianco78/signalroom --skill dlt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill removes the burden of building and maintaining complex ETL pipelines, automatically handling schema evolution and incremental loading.

Core Features & Use Cases

Data Ingestion: Extract and load data from multiple marketing platforms automatically.
Schema Management: Handle evolving data structures without manual database changes.
Use Case: When you need to add a new data source like Google Ads, this Skill handles all the pipeline complexity.

Quick Start

Use the dlt skill to create a new pipeline for importing Facebook Ads data with incremental loading.

Frequently Asked Questions about dlt

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

FAQPage Schema

How do I automate data pipelines without manual ETL work?▼

Automate data pipelines by using dlt to declare sources and resources with decorators, configure write_disposition modes (append, merge, replace), and enable schema evolution. dlt handles data loading, normalization, and incremental loading automatically across multiple sources.

How do I handle schema evolution when adding new data sources?▼

Schema evolution is handled automatically by enabling dlt's auto-evolving schemas. When you add a new data source, dlt detects structural changes and adapts the schema without manual database modifications.

Can I load data incrementally from marketing platforms like Facebook Ads or Google Ads?▼

Yes, enable incremental loading with dlt.sources.incremental and resource_state to track progress across data sources. Define primary keys for merge operations to load only new or changed records efficiently.

What do I need to set up before creating a new data pipeline?▼

Declare your sources and resources using dlt decorators, configure your target write_disposition (append, merge, or replace), define primary keys if using merge, and enable auto-evolving schemas for your data source.

How do I debug pipeline failures and monitor data loads?▼

Expose diagnostic metadata queries for loads and pipeline state to inspect what data was ingested, identify schema mismatches, and track incremental loading progress across your ETL pipeline.