Can I manage my project dependencies with this?

The skill is optimized for use with uv, allowing for fast and reliable Python package management within your data pipeline projects.

How do I run a pipeline using this skill?

You can simply ask Claude to run a specific pipeline, and it will use the flowerpower pipeline run command with your specified inputs and execution settings.

Does it support distributed computing?

Yes, the skill helps configure various executors including Ray and Dask for scaling your data processing across multiple cores or clusters.

FlowerPower is a framework designed to manage data pipelines using Hamilton DAGs, providing a structured way to handle configuration, execution, and project layout.

How does this skill help with Hamilton DAGs?

It automates the boilerplate for creating Hamilton functions and mapping them to YAML configurations, making it easier to define complex dataflows.

FlowerPower Pipeline Manager

Name: FlowerPower Pipeline Manager
Author: legout

bylegout

•

Ciencia de Datos y ML

Automates the creation and management of robust data pipelines using Hamilton DAGs and the FlowerPower framework.

Acerca de

This skill enables Claude to architect, implement, and orchestrate complex data processing workflows within the FlowerPower ecosystem. It provides comprehensive support for initializing structured projects, generating Hamilton-based pipeline modules, and configuring execution parameters like retries and parallel processing. Whether you're building CPU-bound data transformations or I/O-heavy ingestion tasks, this skill leverages the Hamilton DAG paradigm and uv package management to ensure clean, maintainable, and scalable data engineering projects.

Características Principales

2 GitHub stars
Automated project structure initialization with standardized directory layouts
Visualizing Directed Acyclic Graphs (DAGs) and listing available pipeline workflows
Support for diverse execution backends including ThreadPool, ProcessPool, Ray, and Dask
Built-in management for pipeline retries, delays, and input parameterization
Rapid generation of Hamilton-powered pipeline modules and YAML configurations

Casos de Uso

Migrating sequential scripts into parallelized Hamilton DAGs for better performance
Setting up a new data engineering project with modular, testable pipeline structures
Configuring distributed data processing tasks across Ray or Dask clusters

Acerca de

Características Principales

2 GitHub stars
Automated project structure initialization with standardized directory layouts
Visualizing Directed Acyclic Graphs (DAGs) and listing available pipeline workflows
Support for diverse execution backends including ThreadPool, ProcessPool, Ray, and Dask
Built-in management for pipeline retries, delays, and input parameterization
Rapid generation of Hamilton-powered pipeline modules and YAML configurations

Casos de Uso

Migrating sequential scripts into parallelized Hamilton DAGs for better performance
Setting up a new data engineering project with modular, testable pipeline structures
Configuring distributed data processing tasks across Ray or Dask clusters