By apache
Build, debug, integrate, observe, and scale Python data pipelines using Apache Hamilton. Turn natural language into DAGs via DOT graphs and TDD, add LLM/RAG workflows, connect to Airflow/FastAPI/Streamlit, visualize executions, and optimize with async/Spark for production.
npx claudepluginhub apache/hamilton --plugin hamiltonCore Hamilton patterns for creating DAGs, applying decorators, testing, and debugging dataflows. Use for basic Hamilton development tasks.
Systematic 5-step workflow for building Hamilton DAGs - DOT graphs, signatures, validation, TDD implementation. Use this workflow when creating new Hamilton modules from scratch.
Hamilton integration patterns for Airflow, Dagster, FastAPI, Streamlit, Jupyter notebooks, and other frameworks. Use when integrating Hamilton with other tools.
LLM and AI workflow patterns for Hamilton including RAG pipelines, embeddings, vector databases, and prompt engineering. Use for building AI applications with Hamilton.
Interactive Hamilton DAG development via MCP tools. Validate, visualize, scaffold, and execute Hamilton pipelines without leaving the conversation. Use when building or debugging Hamilton dataflows interactively.
Hamilton UI and SDK patterns for tracking, monitoring, and debugging dataflows. Use for observability, lineage tracking, and production monitoring.
Performance and parallelization patterns for Hamilton including async I/O, Spark, Ray, Dask, caching, and multithreading. Use for scaling Hamilton workflows.
Quick insights from dlt pipeline data. Connect to a pipeline, profile tables, plan charts, and assemble marimo dashboards.
Share bugs, ideas, or general feedback.
Data engineering plugin - warehouse exploration, pipeline authoring, Airflow integration
Comprehensive Dagster development conventions and best practices
Orchestrate complex workflows with DAG-based execution, parallel tasks, and run history tracking
Editorial "Data Engineering" bundle for Claude Code from Antigravity Awesome Skills.
Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture