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python-data-pipeline-designer

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Mis à jour25 janvier 2026 à 18:01

Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

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jorgealves

jorgealves/agent_skills

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Scientifiques des donnéesProfessions informatiques et mathématiques·SOC 15-2051

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2 fichiers

SKILL.md

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name	python-data-pipeline-designer
description	Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

Python Data Pipeline Designer

Purpose and Intent

Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

When to Use

Project Setup: When initializing a new Python project.
Continuous Integration: As part of automated build and test pipelines.
Legacy Refactoring: When updating older Python codebases to modern standards.

When NOT to Use

Non-Python Projects: This tool is specialized for the Python ecosystem.

Error Conditions and Edge Cases

Missing Requirements: If the project lacks a requirements.txt or pyproject.toml.
Incompatible Versions: If the project uses a Python version not supported by the tools.

Security and Data-Handling Considerations

All analysis is performed locally.
No source code or credentials are ever transmitted externally.

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name	python-data-pipeline-designer
description	Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

Python Data Pipeline Designer

Purpose and Intent

Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

When to Use

Project Setup: When initializing a new Python project.
Continuous Integration: As part of automated build and test pipelines.
Legacy Refactoring: When updating older Python codebases to modern standards.

When NOT to Use

Non-Python Projects: This tool is specialized for the Python ecosystem.

Error Conditions and Edge Cases

Missing Requirements: If the project lacks a requirements.txt or pyproject.toml.
Incompatible Versions: If the project uses a Python version not supported by the tools.

Security and Data-Handling Considerations

All analysis is performed locally.
No source code or credentials are ever transmitted externally.