claude

transformations

Turn raw dlt pipeline data into a Canonical Data Model: annotate sources, build an ontology, design a Kimball-style model and write @dlt.hub.transformation functions. Requires dlt[hub].

65 on GitHubby ScaleVector GmbHv0.1.3https://github.com/dlt-hub/dlthub-ai-harness/blob/master/LICupdated yesterdaydata
/

Plugin transformations from marketplace dlthub-ai-harness in dlt-hub/dlthub-ai-harness. Commands read from the repo's manifest — yesterday.

This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (6)

  • annotate-sources

    Annotate dlt pipeline sources for transformation. Use when the user wants to transform data, do data modelling, design a data model, describes their data sources and use cases, or wants to build a CDM from existing pipelines.

  • create-ontology

    Build a business entity graph (ontology) from annotated sources and taxonomy. Use after annotate-sources to design the entity model before CDM generation.

  • create-transformation

    Write dlthub transformation functions that map source tables to CDM entities. Use after generate-cdm to produce the transformation Python script.

  • debug-transformation

    Debug dlthub transformation failures. Use when a transformation fails on a different destination than it was developed on, SQL dialect errors occur after deployment, pipeline recovery is needed after a failed run, or columns are silently dropped from output.

  • generate-cdm

    Generate a Canonical Data Model (CDM) in DBML using Kimball dimensional modeling. Use after create-ontology to produce the implementation-ready CDM schema.

  • incremental-transformation

    Switch a dlthub transformation from full-replace to incremental loading. Use when the user wants to process only new or changed rows, reduce transformation run time, or schedule frequent transformation runs without reprocessing all data.

MCP servers (1)

  • dlt-workspace-mcp

    runs: uv run dlthub ai mcp --stdio

View source on GitHub dlthub.com

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