dlthub-ai-harness

Build dlt pipelines for REST APIs: find a source, scaffold the pipeline, add endpoints and incremental loading, debug it, validate the data and view it in a report. Requires dlt[hub].

8 skills · 1 MCP server

Load tables from any SQLAlchemy-supported database into a destination with dlt, and tune performance with the available backends. Requires dlt[hub].

8 skills · 1 MCP server

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].

6 skills · 1 MCP server

Shared rules, secrets handling, background agent authoring and the workspace MCP server used by the other dlthub toolkits. Requires dlt[hub].

5 skills · 1 MCP server

Add column-level checks and load metrics to dlt pipelines, run them on every load and review failures with diagnosis. Requires dlt[hub].

4 skills

Prepare and deploy your dlthub workspace and pipelines to the dltHub platform, and debug failed deployments. Requires dlt[hub].

4 skills

Load CSV, Parquet, JSONL or custom files from local disk, S3, GCS, Azure or SFTP into a destination with dlt. Requires dlt[hub].

3 skills

Get quick insights from a dlt pipeline: profile its tables, plan charts and assemble a marimo dashboard. Requires dlt[hub].

2 skills

Build and deploy a minimal custom REST API pipeline to the dltHub platform after running uvx dlthub-init. Requires dlt[hub].

1 skill · 1 MCP server

A guided path from ingestion to visualization, transformation and deployment in 3-5 prompts. Requires dlt[hub].

1 skill · 1 command

Set up the Python environment for a dlthub workspace. Requires dlt[hub].

1 command

Build a minimal GitHub issues pipeline and deploy it to the dltHub platform in one go. Requires dlt[hub].

1 skill

Find the slow stage of a dlt pipeline (extract, normalize or load) and tune parallelism, workers, buffers, file rotation and batching. Requires dlt[hub].

1 skill