claude

sql-database-pipeline

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

65 on GitHubby ScaleVector GmbHv0.2.1https://github.com/dlt-hub/dlthub-ai-harness/blob/master/LICupdated yesterdaydata
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Plugin sql-database-pipeline 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 (8)

  • add-table

    Add a new table or view to an existing dlt SQL database pipeline. Use when the user wants to load additional tables from the same database that already has a working pipeline.

  • adjust-table

    Adjust a working dlt SQL database pipeline for production — remove dev limits, add incremental loading, configure merge keys. Use when the user wants to remove .add_limit(), load the full table, or set up incremental loading on a cursor column. For speed/memory tuning (backend, chunk size, paralleli

  • create-sql-database-pipeline

    Create a dlt pipeline from a SQL database source (postgres, mysql, mssql, oracle, sqlite, or any SQLAlchemy-supported database). Use when the user wants to load tables from a relational database to a destination like DuckDB, BigQuery, or Snowflake. Not for REST APIs or file sources.

  • debug-sql-database-pipeline

    Debug and inspect a dlt SQL database pipeline after running it. Use after a pipeline run (success or failure) to inspect traces, load packages, schema, and diagnose errors like connection failures, missing credentials, driver issues, or failed load jobs.

  • find-sql-database-source

    Find and explore a SQL database source for a dlt pipeline. Use when the user wants to load data from a relational database (postgres, mysql, mssql, oracle, sqlite, or any SQLAlchemy-supported database), mentions a database connection, wants to discover available tables, or asks to build a pipeline f

  • optimize-sql-performance

    Speed up a dlt SQL database pipeline. Use when extraction from a relational database (postgres, mysql, mssql, oracle, snowflake, etc.) is slow or memory-heavy and the user wants to optimize it — pick a faster backend, tune chunk size, parallelize tables, or reduce reflection overhead. For first-time

  • validate-sql-database-data

    Validate schema and data after a successful dlt SQL database pipeline load. Use when the user wants to check if loaded data looks correct, inspect table schemas, fix data types, or verify column mappings from the source database.

  • view-sql-database-data

    Query, explore, or view data loaded by a dlt SQL database pipeline. Use when the user asks to query data, explore loaded tables, check row counts, write Python that reads pipeline data, or asks questions like "show me the data", "what users are there", "how much did we spend". Covers dlt dataset API

MCP servers (1)

  • dlt-workspace-mcp

    runs: uv run dlthub ai mcp --stdio

View source on GitHub dlthub.com

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