Automate data loading, normalization, and schema management for SignalRoom ETL pipelines.
npx skills add https://github.com/mmbianco78/signalroom --skill dlt
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
This Skill removes the burden of building and maintaining complex ETL pipelines, automatically handling schema evolution and incremental loading.
Use the dlt skill to create a new pipeline for importing Facebook Ads data with incremental loading.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
dlt-hubIdentify dlt source connectors for APIs and data providers.
EpiphyticWrite correct and performant SQL queries across major data warehouse dialects.
DKSangTranslate DES source requirements into dlt ingestion pipelines with evidence.
underPressueOrchestrate, validate, and optimize ETL/ELT data pipelines in Python.
